ISMRM 21st Annual Meeting & Exhibition 20-26 April 2013 Salt Lake City, Utah, USA

2556 -2566 Artifacts & Correction
2567 -2576 Artifacts & Correction: Off-Resonance & Eddy Currents
2577 -2591 Artifacts & Correction: Motion
2592 -2600 B1 Mapping
2601 -2628 Compressed Sensing
2629 -2661 Image Reconstruction: Non-Cartesian & Parallel Imaging
2662 -2686 Image Reconstruction & Analysis
2687 -2706 Advances in Image Analysis

Wednesday, 24 April 2013 (10:00-12:00) Exhibition Hall
Artifacts & Correction

Accelerating a Spectrally-Resolved Fully Phase-Encoded (SR-FPE) Method for Metal Artifact Reduction
Nathan S. Artz1, Alexey A. Samsonov1, Diego Hernando2, Valentina Taviani1, Matthew R. Smith1, Jean H. Brittain3, and Scott B. Reeder1,4
1Radiology, University of Wisconsin, Madison, WI, United States, 2Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Global Applied Science Laboratory, GE Healthcare, Madison, WI, United States, 4Medical Physics, University of Wisconsin, Madison, WI, United States

A spectrally-resolved fully phase-encoded (SR-FPE) technique was recently introduced for imaging near metal. The primary limitation of SR-FPE is long scan time. This work first compares distortion in SR-FPE and conventional 3D-FSE, and then examines the potential for acceleration. A hip prosthesis was scanned with SR-FPE using a 16-channel coil, and data were retrospectively under-sampled to demonstrate the feasibility of parallel imaging in all three phase-encoding directions, in combination with corner-cutting and half-Fourier sampling. Highly accelerated distortion-free SR-FPE images were reconstructed using the equivalent of ~7.5minutes of scanning, compared to 4 hours of fully sampled data, demonstrating feasibility for clinical implementation.

2557.   MR Measurement of Alloy Magnetic Susceptibility: Towards Developing Tissue-Susceptibility Matched Metals
Garrett W. Astary1, Marcus K. Peprah2, Charles R. Fisher3, Paul R. Carney4, Malisa Sarntinoranont5, Mark W. Meisel2, Michele V. Manuel3, and Thomas H. Mareci1
1Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, United States, 2Physics, University of Florida, Gainesville, FL, United States, 3Materials Science and Engineering, University of Florida, Gainesville, FL, United States, 4Biomedical Engineering, University of Florida, Gainesville, FL, United States, 5Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, United States

Our goal is to develop an alloy that is magnetic susceptibility-matched to brain tissue to create electrodes and cannula that do not distort MR images. We have developed an MR method for measuring magnetic susceptibility and evaluated the method by measuring the susceptibility of copper-tin alloys and comparing the results to SQUID magnetometry. The MR method was evaluated at 4.7 T and 11.1 T and deviated by less than 3.1% from SQUID magnetometry measurements. The MR method is free of geometric and sample-size restrictions associated with SQUID magnetometry and can be implemented at any facility with MR hardware.

2558.   Highly Accelerated SEMAC Metal Implant Imaging Using Joint Compressed Sensing and Parallel Imaging
Mathias Nittka1, Ricardo Otazo2, Leon D. Rybak2, Kai Tobias Block3, Christian Geppert4, Daniel K. Sodickson2, and Michael P. Recht2
1Siemens AG, Erlangen, Germany, 2Department of Radiology, New York University School of Medicine, New York, New York, United States, 3Department of Radiology, NYU Langone Medical Center, New York, New York, United States, 4Siemens Medical Systems, New York, New York, United States

A highly accelerated implementation of SEMAC for metal implant imaging is presented, which aims to achieve efficient metal artifact redcution at clinically tolerable scan times. An approach of joint compressed sensing and parallel imaging is used, kz-kz undersampling is based on a Poisson-disk pattern with fully sampled k-Space for autocalibration. Experiments on a cadaver knee with a joint replacement were carried out both with a 4-channel flex coil and a 15 channel TX/RX coil. First results show good metal artifact reduction without significant loss in image quality at a total acceleration factor of 6.9.

2559.   Development of Cerebral Aneurysm Coils with Equivalent Volume Magnetic Susceptibility to Body Tissue That Generate Small Susceptibility Artifacts in MRI
Ryusuke Nakai1,2, Takashi Azuma3, Tomonobu Kodama1, Hiroshi Tanemura4, Kenichi Hamada5, Hidenori Suzuki4, Waro Taki4, and Hiroo Iwata1
1Institute for Frontier Medical Sciences, Kyoto University, Kyoto, Japan, 2Kokoro Research Center, Kyoto University, Kyoto, Japan, 3National Institute of Information and Communications Technology, Suita, Osaka, Japan, 4Department of Neurosurgery, Mie University Graduate School of Medicine, Tsu, Mie, Japan, 5Institute of Health Biosciences, The University of Tokushima Graduate School, Tokushima, Japan

To remedy susceptibility artifact of conventional aneurysm coils, we developed aneurysm coils made of a gold-platinum alloy. In this study, tensile strength and breaking strength of the new Au-Pt aneurysm coil were approximately equivalent to those of the conventional Pt-W coil. The MRI compatibility of the Au-Pt aneurysm coil was confirmed. These results of MRI artifact evaluation show that use of the Au-Pt coil results in a smaller susceptibility artifact than that obtained with the Pt-W coil, and suggest that the Au-Pt coil may allow a more precise observation of the status of a cerebral aneurysm embolus using MRI.

2560.   Assessing Measurement Accuracy Near Orthopaedic Materials in Magnetic Resonance Imaging
Matthew F. Koff1, Parina Shah1, Kevin M. Koch2, and Hollis G. Potter1
1Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, United States, 2Applied Science Laboratory, General Electric Healthcare, Waukesha, WI, United States

The presence of orthopaedic hardware creates significant distortion artifacts in MR images. This study evaluated the accuracy of artifact reduction between standard of care 2D fast-spin-echo (FSE) images and corresponding MAVRIC images as compared to a gold standard. Metal samples produced significant distortions in 2D-FSE images, which were eliminated in MAVRIC images. The average difference between the known phantom data points and MAVRIC data points was less than 2 pixels for scans of all materials. MAVRIC scans are effective in reducing image distortion, when compared to known dimensions.

2561.   A Fast and Robust Method for Off-Resonance Detection in Metal Implant Imaging
Theresa Bachschmidt1,2, Mathias Nittka1, and Peter M. Jakob2
1Siemens Healthcare Sector, Erlangen, Germany, 2EP5, University of Wuerzburg, Wuerzburg, Germany

Field inhomogeneities are the dominant source for artifacts in images of volumes containing metal implants. They can be determined by measurement of the geometric distortion of the slice profile. Its shape is determined by the amplitude and polarity of the readout gradient. This work depicts analytically two inaccuracies of this method, both based on the fact that slices are not infinitely thin and suggests solutions. Application of readout gradients with amplitudes in the range of the slice select gradient require opposite polarity relative to the slice select gradient. The application of two different readout gradients can resolve the inaccuracy of the excitation bandwidth.

2562.   On the Relation of the Ripple Artifact in Multi-Spectral Imaging and Susceptibility Induced Field Gradients
Chiel J. den Harder1, Ulrike A. Blume2, Gert van IJperen1, and Clemens Bos3
1MRI Technology Development, Philips, Best, Noord Brabant, Netherlands, 2Imaging Systems, Philips, Hamburg, Hamburg, Germany, 3Image Sciences Institute, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

Multi-Spectral Imaging (MSI) techniques have been shown to significantly reduce susceptibility artifacts. Especially in techniques that use gradient selection, such as Slice Encoding for Metal Artifact Correction (SEMAC), remaining ripple artifacts can be prominent. This work presents a simulation analysis verified by phantom experiments, showing that the ripple artifact appears only if B0 varies both in-plane and through-plane. As shown before, the ripple artifact is the remaining limitation of the capability of MSI techniques to reduce metal artifacts. The simulations presented here help define the origin of the ripple artifact and provide a means to investigate ways to address it.

2563.   Correcting bSSFP Distortion Near Metals with Geometric Solution Phase
Michael Nicholas Hoff1 and Qing-San Xiang1,2
1Physics, University of British Columbia, Vancouver, BC, Canada, 2Radiology, University of British Columbia, Vancouver, BC, Canada

Balanced steady state free precession imaging can suffer from geometric distortion near metals. The geometric solution (GS) for banding correction has phase which maps the local field inhomogeneity; here this phase is unwrapped and used to remap distorted signal in the GS magnitude image. This distortion correction did not require a phase reference due to the insignificance of phase error relative to the off-resonant phase accumulation near the metal, and the unwrapping algorithm proved robust in all signal regions. The advantage of this correction is that it does not require excess scan time beyond that needed to compute the GS.

2564.   Recovering BSSFP Signal Loss Near Metals with Shimming
Michael Nicholas Hoff1 and Qing-San Xiang1,2
1Physics, University of British Columbia, Vancouver, BC, Canada, 2Radiology, University of British Columbia, Vancouver, BC, Canada

A method for recovering signal lost near metals in balanced steady free precession (bSSFP) imaging is described. Twelve bSSFP images are acquired with shim gradients applied in six different directions (±x, ±y, and ±z) at two different phase cycles for added band suppression. The sum of squares of these images is combined with a regular debanded image to yield a composite solution with recovered signal. Residual artifacts could be ameliorated and scan time minimized with an optimal choice of gradient shim orientations and strengths. The technique indicates the potential for bSSFP imaging with comprehensive artifact correction near metals.

2565.   Restoration of Large Slice Profile Distortions Near Metallic Implants by Frequency Mapping
Viktor P. Morin1, Gunilla M. Müller2, and Sven Månsson3
1Electrical measurements, Lund University, Lund, Sweden, 2Radiology, Lund University, Malmö, Sweden, 3Medical Radiation Physics, Lund University, Malmö, Sweden

The interest to apply MRI examinations near metallic implants is increasing. Field mapping was previously used to correct through-plane distortions when the frequency offset was moderate. More recently, Slice Encoding for Metal Artifact Correction (SEMAC) was developed for correction of much larger frequency offsets. However, despite the use of acceleration techniques, SEMAC is still time consuming when the slice profile is heavily distorted. The purpose of this work is to investigate the use of field mapping to restore the slice profile of a rapid VAT sequence without SEAMC encoding, when the frequency offset is very large.

2566.   Order of Magnitude Speed-Up in 2pt Dixon Water/Fat Separation Processing
Dong Zhou1, Alexey Dimov1, Tian Liu1, Pascal Spincemaille1, Martin R. Prince1, and Yi Wang1
1Weill Cornell Medical College, New York, NY - New York, United States

In this study, a significant speedup in the 2pt Dixon fat-water separation is achieved by a separate estimation of the inhomogeneity field.


Wednesday, 24 April 2013 (10:00-12:00) Exhibition Hall
Artifacts & Correction: Off-Resonance & Eddy Currents

2567.   Shim Cycling Technique to Eliminate the Banding Artifacts in 3D BSSFP Inner Ear Images
Seong-Eun Kim1, John A. Roberts1, Richard Wiggins1, Bradley D. Bolster2, and Dennis L. Parker1
1UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah, United States, 2Siemens Healthcare, Salt Lake City, Utah, United States

In this work, we present a shim cycling technique to eliminate the banding artifacts in 3D bSSFP inner ear images in which multiple image sets are acquired with different shim currents during the acquisition of each set to change the local field compensation. These preliminary results give a strong indication that shim adjustments can sufficiently shift the banding artifact in 0.4 isotropic resolution 3D TrueFISP images, allowing very uniform composite images to be obtained. It would appear that banding artifacts in higher resolution images, with even longer TR, could be corrected. This work will be important for very high-resolution 3D TrueFISP imaging of the inner ear.

2568.   Rapid Volume Shimming with Gradient Reversed EPI
Kevin Koch1, Eric Printz1, and Dan Xu1
1GE Healthcare, Milwaukee, Wisconsin, United States

It has previously been demonstrated that EPI acquisitions with reversed phase-encode blip polarities can be used to correct image distortions. A by-product of these methods is a shift map that maps the two source images together. Here, we demonstrate that one of the algorithms previously utilized for distortion correction purposes can be modified to produce high-SNR volumetric field maps for shimming purposes. These results suggest that such methods could be used to collect spatially accurate volumetric field maps for shimming purposes in a matter of seconds.

2569.   Snapshot Field Monitoring Enables Correction of Slow Field Perturbations in High-Resolution Brain MRI
Signe Johanna Vannesjo1, Bertram J. Wilm1, Yolanda Duerst1, Benjamin E. Dietrich1, David Otto Brunner1, Christoph Barmet1,2, Thomas Schmid1, and Klaas P. Pruessmann1
1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 2Skope Magnetic Resonance Technologies, Zurich, Switzerland

Breathing- and motion-related field fluctuations can affect brain imaging at 7T. The resulting artifacts can be reduced by concurrent field monitoring. However, gradient dephasing and signal decay of the field probes set limits to the image resolution. Here we assume the field perturbations to be slow, and thus a single field measurement per readout suffices for correction. It is shown that with this approach good image quality can be recovered in T2*-weighted images, that display strong ghosting when not corrected. Unlike full k-space monitoring, the approach is applicable also to high-resolution imaging.

2570.   Magnetic Susceptibility and Field Map Estimation in fMRI Time Series Using a High Resolution Static Field Map
Hiroyuki Takeda1 and Boklye Kim2
1Radiology, University of Michigan, Ann Arbor, Michigan, United States, 2Radiology, University of Michigan, ann arbor, MICHIGAN, United States

In practice, the field map dynamically changes with head motion during the scan, and such a changing field leads to variations in geometric distortions in EPI. A previous retrospective approach of approximating a dynamic field map by applying rigid body transformations to an observed static field map may be insufficient in the presence of significant out-of-plane rotations. Our approach is to retrospectively estimate the object’s susceptibility map from an observed high-resolution static field map using an estimator derived from a probability density function of non-uniform noise. This approach is capable of finding the susceptibility map regardless of the wrapping effect.

2571.   Autocalibrated PROPELLER-EPI: Intrinsic Geometric Distortion Correction Without Additional Reference Data
Martin Krämer1 and Jürgen R. Reichenbach1
1Medical Physics Group, Institute of Diagnostic and Interventional Radiology I, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany

Novel reconstruction technique for PROPELLER-EPI which requires no additionally measured field map for distortion correction. By modulating the input data with constant frequency offsets the effective resonance frequency on which the image is reconstructed is modulated, enabling autocalibrated PROPELLER-EPI reconstruction. The theory of the technique is explained in detail including a proposed algorithm for automatic detection of on resonant image parts. We compare the proposed method to standard PROPELLER-EPI image reconstruction using statically measured field maps showing that comparable results can be achieved.

2572.   Phase Encoding Correction for 3D FSE Microscopy
Jonathan Bishop1, R Mark Henkelman1,2, and Brian J. Nieman1,2
1Hospital for Sick Children, Toronto, ON, Canada, 2Medical Biophysics, University of Toronto, Toronto, ON, Canada

Phase errors generated by the phase encoding gradients of a 3D fast spin-echo sequence for preclinical microscopy are measured with a novel prescan sequence and applied as a retrospective correction at reconstruction to improve image quality.

2573.   Field Probe Based Shot-To-Shot B0 Correction for Multi-Shot Breast-DWI at 7T
Tijl A. van der Velden1, Vincent Oltman Boer1, Peter R. Luijten1, and Dennis W.J. Klomp1
1Radiology, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

Field probes have been used for shot-to-shot B0 correction multi shot DWI images of the human breast at 7T. B0 correction is necessary for multi shot imaging to prevent ghosting due to temporal B0 fluctuations caused by physiological motions. In this study we demonstrate that the information simultaneously obtained from field probes reduce ghosting artefacts in multi-shot DWI at 7T in the human breast.

2574.   Dynamic Slice-Dependent Shim and Center Frequency Update in 3 T Breast DWI
Seung-Kyun Lee1, Ek T. Tan1, Ambey Govenkar2, and Ileana Hancu1
1GE Global Research, Niskayuna, NY, United States, 2Extenprise, Pune, India

Dynamic slice-dependent update of linear shim gradients and center frequency was implemented in 3 T breast imaging. The method was applied to axial bilateral breast DWI on four volunteers. In all volunteers the measured B0 maps showed significantly improved homogeneity. Anatomy-referenced ADC maps also showed reduced image registration error obtainable with the proposed method.

2575.   Dynamically Unwarped Diffusion Imaging Removes Direction-Dependent Eddy Current Effects and Unveils Hidden Fiber Structures
Erik B. Beall1, Myung-Ho In2, Oliver Speck2, Ken E. Sakaie1, and Mark J. Lowe1
1Imaging Institute, Cleveland Clinic, Cleveland, OH, United States, 2Biomedical Magnetic Resonance, Otto-von-Guericke University, Magdeburg, Germany

Eddy current artifacts reduce resolving power of DTI scans and obliterate small fiber tracts. By acquiring forward and reverse phase-encodes for every direction and using new algorithms for unwarping these together, both the static and dynamic (diffusion direction, breathing and head position-dependent changes) field inhomogeneity can be removed. This dramatically improves the tensor fit error and uncovers structures invisible with only static unwarping.

2576.   Characterization and Correction of Eddy-Current Artifacts in Unipolar and Bipolar Diffusion Sequences Using a Field-Monitoring Approach: Application to Renal Diffusion Tensor Imaging (DTI)
Rachel Wai-chung Chan1, Sebastian Kozerke2,3, Daniel Giese3, Jack Harmer3, Christian T. Stoeck2, Constantin von Deuster2,3, Andrew Peter Aitken3, and David Atkinson1
1Centre for Medical Imaging, University College London, London, United Kingdom, 2Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 3Division of Imaging Sciences, King's College London, London, United Kingdom

In diffusion tensor imaging (DTI), time-varying eddy-currents over long readout durations cause images from different directions to be misregistered. Using a field camera with 16 NMR probes, higher-order spatial phase offsets from eddy-currents were measured and used for correction of misregistration artifacts in renal DTI. The unipolar Stejskal-Tanner and velocity-compensated bipolar sequences were compared. Phantom experiments reveal that higher-order correction is beneficial for the unipolar sequence. In an in vivo experiment where both kidneys in a healthy volunteer were simultaneously imaged, the fractional anisotropy (FA) maps showed improved image quality with eddy-current correction.


Wednesday, 24 April 2013 (10:00-12:00) Exhibition Hall
Artifacts & Correction: Motion

2577.   Automatic Rigid-Body Motion Correction Via Phase Retrieval and Sparsity Constraints
Joseph Y. Cheng1, Shreyas S. Vasanawala2, Michael Lustig3, and John M. Pauly4
1Electrical Engineering, Stanford University, Stanford, California, United States, 2Radiology, Stanford University, Stanford, California, United States, 3Electrical Engineering and Computer Sciences, University of California, Berkeley, California, United States, 4Electrical Engineergin, Stanford University, Stanford, California, United States

There have been major advancements in reducing motion corruption especially for rigid-body motion. Even with these methods, there may be residual artifacts due to motion measurement error. Automatic correction methods can be applied without any motion information. We propose a novel automatic approach using phase-retrieval and sparsity constraints. This approach is incorporated with parallel imaging and compressed sensing to help guide the correction. First, the accuracy of the algorithm is demonstrated in a simulation head study. Afterwards, the method is used to correct motion from conventional in vivo scans.

2578.   Prospective Real Time Rigid Body Motion Correction at 7 Tesla Using Inductively Coupled Wireless NMR Markers.
Saikat Sengupta1, Sasidhar Tadanki2, John C . Gore1, and E. Brian Welch1
1Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States, 2Vanderbilt University, Nashville, TN, United States

We present the development and use of inductively coupled wireless NMR markers for prospective rigid body motion correction at 7 Tesla with the ultimate goal of real time head motion correction. Three 5 mm markers mounted on a phantom are located using a linear navigator. Motion is estimated in real time and imaging geometry is updated prospectively to compensate for motion with 6 degrees of freedom. Effective real time correction of complex motions in 1mm3 voxel size gradient echo imaging is demonstrated. Inductively coupled markers add significant benefits in flexibility, comfort, size and receiver channel requirements while maintaining performance.

2579.   In-Plane Motion Correction for Diffusion-Weighted 3D Multi-Slab EPI
Mathias Engström1,2 and Stefan Skare1,2
1Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Stockholm, Sweden, 2Department of Neuroradiology, Karolinska University Hospital, Stockholm, Stockholm, Sweden

This work details a two step in-plane motion correction method for diffusion-weighted 3D Multi-Slab EPI, using the navigator readouts and the overlapping area between adjacent slabs.

2580.   Correction of Motion-Induced Phase Variance in Single-Voxel 1H Spectroscopy
Brian Robert Keating1 and Thomas Ernst2
1Department of Medicine, Unversity of Hawaii, Honolulu, HI, United States, 2Department of Medicine, University of Hawaii at Manoa, Honolulu, HI, United States

Subject motion introduces unwanted phase variance in the MR signal, resulting in image/spectral artifacts. We performed single-voxel brain scans (PRESS) while subject head movement was monitored by an external optical tracking system. The motion-induced phase evolution was estimated based on tracking data and knowledge of the gradient waveforms. Phase corrections were applied in post-processing, resulting in improved phase coherence and increased SNR on real spectra. These results demonstrate the promise of a high-accuracy motion tracking system for reducing movement-related artifacts in phase-sensitive modalities.

2581.   Automatic ROI Specification and Reference Frame Selection for Motion Correction in Cardiac Imaging
Jieying Luo1, R. Reeve Ingle1, and Dwight G. Nishimura1
1Electrical Engineering, Stanford University, Stanford, California, United States

An automatic method for ROI specification and reference frame selection has been developed to enable automatic motion estimation from two-dimensional image navigators. Filtering and feature region searching techniques are used to automatically detect an ROI covering the heart. A novel principal component analysis technique is used to extract respiratory information from the image navigators. This respiratory information is used to identify an image at end expiration to use as a reference for subsequent motion estimation. This method is applied to a free-breathing coronary MR angiography acquisition to enable automatic display of motion-corrected images after a scan.

2582.   Optimized Reconstruction for PROPELLER MRI
James G. Pipe1, Nicholas Ryan Zwart1, Michael Schar2, Wende N. Gibbs3, and John P. Karis3
1Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona, United States, 2Philips Healthcare, Phoenix, Arizona, United States, 3Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, United States

A novel method for estimating motion from PROPELLER data is given. A modification to the original algorithm allows one to simultaneously solve for correlations between all blade pairs at the same time. Comparison to the original method shows a small but consistent improvement in image quality.

2583.   A Novel Swallow Detection Device for Carotid Artery Imaging
Jason K. Mendes1, Dennis L. Parker1, Robb Merrill1, and J. Rock Hadley1
1Radiology, University of Utah, Salt Lake City, Utah, United States

Carotid MRI still suffers from blurring and ghosting artifacts due to patient swallowing and associated palatal motion. A simple pneumatic device coupled to a respiration monitoring system (available on most clinical scanners) can be used to detect patient swallowing and improve image quality.

2584.   Robust Low-Rank Matrix Completion for Sparse Motion Correction in Auto Calibration PI
Zhongyuan Bi1, Martin Uecker2, Dengrong Jiang3, Michael Lustig2, and Kui Ying3
1Biomedical Engineering, Tsinghua University, Beijing, Beijing, China, 2Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California, United States, 3Engineering Physics, Tsinghua University, Beijing, Beijing, China

Auto-calibration parallel imaging (acPI) is based on local correlations in k-space. It is known to perform robustly in practice, especially when accurate sensitivity information is hard to obtain. However, corruption of ACS data, e.g. by motion, often leads to serious artifacts in the reconstructed images. In this work, we propose to exploit the redundancy in k-space to detect and correct sparse corruptions in ACS data, which could result from random, time-limited motion in clinical practice (e.g. swallowing, jerk, etc). Our work is based on low-rank matrix completion with sparse errors.

2585.   Comparison of DTI Data in 5-Year Old Children Acquired Using Standard and Navigated DTI Sequences
A. Alhamud1, B. Laughton2, Khader M. Hasan3, André J. W. van der Kouwe4, and Ernesta M. Meintjes1
1Human Biology, MRC/UCT Medical Imaging Research Unit, University of Cape Town, Cape Town, Western Cape, South Africa, 2Paediatrics and Child Health, Stellenbosch University and Tygerberg Hospital, Cape Town, Western Cape, South Africa, 3University of Texas Health Science Center at Houston, Houston, Texas, United States, 4Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States

Many diffusion tensor imaging (DTI) studies have reported changes in DTI measures especially fractional anisotropy (FA) when studying brain development or neuro-pathological diseases. Moreover, other studies have found that motion and retrospective motion correction may introduce a positive or negative bias to DTI data. In this study we exploit the navigated diffusion sequence (Alhamud et al., 2012) to measure the patterns of head motion in 5-year old children and their effect on DTI data. The influence of retrospective motion correction with and without rotating the diffusion table on DTI data acquired using the standard diffusion sequence was also investigated.

2586.   Improved Motion Correction in PROPELLER by Using Grouped Blades as Reference
Zhe Liu1, Zhe Zhang1, Sheng Fang2, Juan Wei3, Chun Yuan1,4, and Hua Guo1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2Institute of nuclear and new energy technology, Tsinghua University, Beijing, China, 3Philips Research Asia Shanghai, Beijing, China, 4Department of Radiology, University of Washington, Seattle, WA, United States

In PROPELLER, reference data plays a critical role for rigid motion correction. In current practice, there are two methods to generate the reference, single-blade reference method and combined-blade reference method. However, these two methods may fail in certain scenarios. In this study, we propose a new method, grouped¨Cblade reference, for reference generation. Instead of using a single blade or forcing all blades to one direction, our method groups blades with the similar orientations together. Preliminary results show that the new method needs less iteration to converge, and is able to give results with higher quality compared to other methods.

2587.   Motion Detection for Diffusion Weighted MRI Using EPI Phase Correction Lines
Onur Afacan1, Ali Gholipour1, Benoit Scherrer1, and Simon K. Warfield1
1Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, United States

The sensitivity of diffusion imaging to motion combined with this increased scan time creates a need for a motion correction strategy, especially with uncooperative patients such as children. Here in this work, we demonstrated that the information from the phase encoding correction lines acquired with an EPI acquisition can be used to detect motion in real time, and can be used to improve the quality of long diffusion scan when there is substantial motion during the scan.

2588.   Correction of Bulk Motion and Assessment of Non-Rigid Deformations in Follow-Up Examinations of the Pelvis
Julien Senegas1, Christian Buerger1, Torbjoern Vik1, Peter Mazurkewitz1, and Peter Koken1
1Philips Research Laboratories, Hamburg, Germany

The focus of this work is on the pelvis area, as MRI is being more and more established as imaging modality to assess and monitor prostate cancer and is expected to play an increasing role in the adaptive planning of radiation therapy. We investigated in volunteers a method to correct for bulk motion between consecutive examinations and analyzed the amplitude of local, non-rigid deformations as the consequence of bladder and rectum filling, with particular focus on the prostate. We think that the methodology proposed in this work to control bulk motion and assess local deformations between consecutive imaging sessions has important applications in therapy planning and image-guided therapy delivery, especially in the field of radio-therapy.

2589.   Motion Detection and Dual Retrospective Correction for MR Spectroscopy in the Human Spinal Cord
Andreas Hock1, Spyros S. Kollias2, Peter Boesiger1, and Anke Henning1,3
1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 2Institute of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland, 3Max Planck Institute for Biological Cybernetics, Max Plank Institute, Tuebingen, Germany

Subject motion is one of the major problems for MR spectroscopy since it often leads to inaccurate results. The detection of patient motion by observing exclusively the spectra is sometimes impossible. Therefore, in this investigation, 1D navigator acquisitions interleaved with non-water-suppressed MRS measurements are used for real-time subject-motion detection and correction in the spinal cord. Interleaved navigators allow precise real-time motion detection for MRS without the need of additional scan-time and additional hardware. Therefore, it allows early intervention (e.g. asking the subject to lay still) and in combination with non-water-suppressed MRS for a retrospective motion correction even in very small regions of interest like the spinal cord.

2590.   Free-Stop Scanning for 3D TSE
Guobin Li1, Maxim Zaitsev1, Esther Meyer2, Dominik Paul2, and Jürgen Hennig1
1University Medical Center Freiburg, Freiburg, Germany, 2Siemens Healthcare, Erlangen, Germany

The measurement time of 3D MR acquisition with turbo spin echo (TSE) sequence is usually long, and very prone to patient movement during the scanning. This leads to degrading of reconstructed image by motion artifacts. An compressed sensing based acquisition scheme is proposed to address this problem, which has these features: scanning stops at any time when motion is detected by built-in navigators; the distribution of acquired k-space points is always optimal whenever the scanning is interrupted; Artifact-free images are reconstructed if a minimum amount of data has been acquired.

2591.   Real-Time Motion Extraction from 2D Image-Based Navigators in Under 20 Milliseconds: An Integrated 2D Navigator Image Processing During MR Data Acquisition
Keigo Kawaji1,2, Pascal Spincemaille3, Thanh D. Nguyen3, Mitchell A. Cooper1,3, Martin R. Prince3, and Yi Wang1,3
1Department of Biomedical Engineering, Cornell University, New York, NY, United States, 2Department of Medicine, Beth Israel Deaconess Med. Ctr. and Harvard Medical School, Boston, MA, United States, 3Department of Radiology, Weill Cornell Medical College, New York, NY, United States

2D navigator image processing during MR data acquisition for real-time motion tracking applications such as prospectively gated and/or motion corrected coronary artery imaging is severely constrained by a short processing window of <50 ms for motion extraction. We present a real-time and interactive processing method that performs on a standard clinical hardware which does not require any additional dedicated hardware components for processing. This approach allows interactive 2D navigator setup by the scanner operator during data acquisition, facilitates rapid motion extraction from raw 2D navigator k-space data, and adjusts the scan instruction within 20 milliseconds based on the extracted motion information.


Wednesday, 24 April 2013 (10:00-12:00) Exhibition Hall
B1 Mapping

Ventricular B1 Enhancement - Truth or Fiction?
Wyger M. Brink1, Peter Börnert1,2, Kay Nehrke2, and Andrew Webb1
1Radiology, Leiden University Medical Center, Leiden, Netherlands, Zuid-Holland, Netherlands, 2Philips Research Laboratories, Hamburg, Hamburg, Germany

High resolution B1+ mapping techniques in the brain at high field often show “residual” structure from, in particular, the ventricles. This can be partially explained by the high contrast in electrical conductivity of CSF with respect to white and grey matter, in addition to long relaxation time-related effects. This factor should be considered when assessing the accuracy of various B1+ mapping sequences.

2593.   Fast Prediction of RF Fields in the Human Brain
Jay Moore1,2, William A. Grissom1,3, and John C. Gore1,2
1Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, 2Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States, 3Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States

B1+ distributions in the brain are predicted by matching head shapes as determined from survey scans to those in a database consisting of pre-existing B1+ maps and associated survey scans. Results are evaluated through comparison with actual maps as well as through the performance of RF pulses designed from both actual and predicted maps.

2594.   Interferometric Bloch-Siegert B1+ Mapping at 7T
Patrik Wyss1, Shaihan J. Malik2, Vincent Oltman Boer3, Johanna J. Bluemink3, Alexander Raaijmakers3, Mustafa Cavusoglu4, Peter R. Luijten3, Johannes J. Hoogduin3, Giel Mens5, and Anke Henning1,6
1Institute for Biomedical Engineering, UZH and ETH Zurich, Zurich, Switzerland, 2Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom,3University Medical Center Utrecht, Utrecht, Netherlands, 4Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 5Philips Healthcare, Best, Netherlands, 6Max Planck Institute for Biological Cybernetics, Tübingen, Germany

To achieve a sufficient dynamic range for single channel B1+ mapping required for calibration of multi-channel transmit arrays two distinct solutions have been suggestion recently: (1) Bloch-Siegert effect based B1+ mapping and (2) B1+ mapping using linear combinations of transmit channels. In this work, both approaches are combined and interferometric Bloch-Siegert B1+ mapping is introduced. This approach is cross-validated against two recently introduced interferometric B1+ mapping methods based on actual flip angle imaging (AFI) and a multi flip-angle pre-pulse method as well as against single-channel AFI based B1+ mapping.

2595.   SNR Analysis of Adiabatic Bloch-Sigert B1+ Mapping
Mohammad Mehdi Khalighi1, Jason H. Su2,3, and Brian K. Rutt3
1Applied Science Lab, GE Healthcare, Menlo Park, California, United States, 2Department of Electrical Engineering, Stanford University, Stanford, California, United States,3Department of Radiology, Stanford University, Stanford, California, United States

Adiabatic Bloch-Siegert (ABS) B1+ mapping with spiral readout has been recently introduced as a fast and efficient way of volumetric B1+ mapping with no T1 or T2 dependency. Here we have analyzed the angle-to-noise ratio (ANR) of the ABS method and show phantom and brain ANR maps at 7T. We also compare analytically the phase-based ABS using spiral readout with the magnitude-based DREAM method. Our comparison shows that ABS generates >3 times ANR compared to DREAM for similar scan times.

2596.   B0-Independent Quantitative Measurement of Low B1 Field for Human Cardiac MRI at 7T
Yuehui Tao1, Aaron T. Hess1, Graeme A. Keith1, Christopher T. Rodgers1, and Matthew D. Robson1
1University of Oxford, Oxford, United Kingdom

Cardiac MRI at 7T has several potential advantages over 1.5T and 3T, such as higher signal-to-noise ratio and higher resolution. A quantitative B1 map is routinely required at 7T to allow the management of large B1 variations across the heart. Conventional methods based on rectangular saturation pulse are not accurate in this case because of large B0 inhomogeneity and low flip angle from low maximum B1 power. We propose to use a non-selective broad-band full-passage HS8 pulse that is operating outside its adiabatic state to obtain B1 maps in situations where the B1 is low and the B0 is inhomogeneous such as for cardiac applications at 7T.

2597.   Simultaneous T1 and B1 Mapping Using Variable Flip Angle Imaging on Fatty Tissue
KyungHyun Sung1, Manoj Saranathan2, Bruce L. Daniel2, and Brian Andrew Hargreaves2
1Radiological Sciences, UCLA, Los Angeles, California, United States, 2Radiology, Stanford University, Stanford, California, United States

Variable flip angle (VFA) imaging is a common choice to measure T1 since it can provide fast volumetric T1 mapping, but is highly sensitive to flip angle variation. We describe a novel way to simultaneously measure T1 and B1 maps using fat-only VFA images, assuming the T1 relaxation times in fat to be globally uniform, and the B1 inhomogeneity is smoothly varying across the object. We showed B1 maps using the proposed method are similar those using the conventional double angle method. Additionally, we demonstrated we can reduce a T1 estimation error by using our simultaneous T1 and B1 mapping method.

2598.   Rapid B1 Mapping Method for Multi-Channel RF Transmit Coil Using Phase-Difference
Kosuke Ito1, Masahiro Takizawa1, and Tetsuhiko Takahashi1
1MRI system division, Hitachi Medical corporation, Kashiwa, Chiba, Japan

Fast B1 mapping method for multi channel transmit coil is developed. In this method, B1 map acquisition is only one time with using all transmit channels. The acquired B1 map is decomposed to B1 map for each transmit channel by using the information of phase difference between transmit channels. Without repeating B1 map acquisition, short scan time is achieved. Also, only B1 map for all channel transmission is used, this method relaxes the required accuracy of B1 map. For 4-channel transmit coil, scan time is only 1 sec.

2599.   Rapid B1 Mapping Method Eliminating T1 Effect by Using Multi Td Sequence
Kosuke Ito1, Masahiro Takizawa1, and Tetsuhiko Takahashi1
1MRI system division, Hitachi Medical corporation, Kashiwa, Chiba, Japan

A new fast B1 mapping method (multi Td method) has developed. 3 images are used to calculate B1 maps in this method, images acquired without pre-pulse, and images acquired at two different delay times (Td1 and Td2) from pre-pulse. The scan time is short due to the use of single shot scan. No approximation about T1 relaxation is needed, and the dynamic range of B1 map calculation is wide. Scan time is only 980 ms per slice.

2600.   A 3D Calibration Protocol for 9.4T Human MRI
Daniel Brenner1, Kaveh Vahedipour1, Tony Stöcker1, Jörg Felder1, Frank Geschewski1, and Nadim Jon Shah1,2
1INM-4, Forschungszentrum Juelich, Juelich, Germany, 2JARA - Faculty of Medicine, RWTH Aachen University, Aachen, Germany

UHF MRI at 9.4T suffers from strong RF inhomogeneities. Measurement of the field distribution of a pTX system is crucial for RF shims and pulse design focussing on removing these problems. A robust 3D whole brain calibration protocol, yielding B1 and B0 maps and an approximate brain mask in approximately 5 minutes is demonstrated. The B1 information is corrected for the effect of off-resonances and limited dynamic range.


Wednesday, 24 April 2013 (10:00-12:00) Exhibition Hall
Compressed Sensing

2601.   Improved L1-SPIRiT Using Tensor-Based Sparsity Basis
Zhen Feng1, Feng Liu2, Stuart Crozier2, and He Guo1
1Dalian University of Technology, Dalian, Liaoning, China, 2The University of Queensland, Brisbane, Queensland, Australia

In the sequential combination of parallel imaging (PI) and compressed sensing (CS) MRI, the CS procedure is conventionally performed on individual coils. In fact, the individual coil data are sensitivity-weighted maps of the whole MRI image, therefore signal overlapping exists between coil data. In this work, we propose a novel sparsity basis to improve CS reconstruction through the exploitation of the inter-coil spatial redundancies. In addition, by introducing a new filter that separates the measured and reconstructed data during L2-norm optimization, noise and errors can be minimized in the sequential PI-CS method. The brain image study showed the promise of the new PI-CS scheme.

Automatic L1-SPIRiT Regularization Parameter Selection Using Monte-Carlo SURE
Daniel S. Weller1, Sathish Ramani1, Jon-Fredrik Nielsen2, and Jeffrey A. Fessler1
1EECS, University of Michigan, Ann Arbor, MI, United States, 2BME, University of Michigan, Ann Arbor, MI, United States

We apply a Monte-Carlo method for estimating Stein's Unbiased Risk Estimate (SURE) to regularization parameter selection for L1-SPIRiT auto-calibrating parallel imaging reconstruction. We validate the error criterion against observed mean-squared error and demonstrate the L1-SPIRiT reconstruction quality using the SURE-optimal regularization parameter for a range of noise levels using fully-sampled multi-channel real data.

2603.   Scalable and Accurate Variance Estimation (SAVE) for Joint Bayesian Compressed Sensing
Stephen F. Cauley1, Yuanzhe Xi2, Berkin Bilgic3, Kawin Setsompop4,5, Jianlin Xia2, Elfar Adalsteinsson4,6, V. Ragu Balakrishnan7, and Lawrence L. Wald4,8
1A.A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, 2Department of Mathematics, Purdue University, West Lafayette, IN, United States, 3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 4A.A. Martinos Center for Biomedical Imaging, Dept. of Radiology, MGH, Charlestown, MA, United States, 5Harvard Medical School, Boston, MA, United States,6Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States, 7School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States, 8Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, United States

The far reaching adoption of compressed sensing for clinic MRI hinges on the ability to accurately produce images in a reasonable time-frame. Multiple contrast studies have been successfully combined with joint Bayesian reconstruction for improved image quality. However, current techniques have prohibitive computational requirements. We consider a joint Bayesian approach that approximates point spread functions to exploit sparse matrix methods. We leverage hierarchical matrix analysis and compression schemes to facilitate scalable and accurate CS reconstruction. Our approach is over 100x faster than other multiple contrast approaches while still improving image accuracy by over 35% compared to single image CS techniques.

2604.   An Efficient Compressed Sensing Reconstruction Robust to Phase Variation on MR Images
Satoshi Ito1, Kazuki Nakamura1, and Yoshifumi Yamada1
1Research Division of Intelligence and Information Sciences, Utsunomiya University, Utsunomiya, Tochigi, Japan

We present a new Compressed Sensing reconstruction that is robust to phase variations in MR images. When the signal trajectory in k-space is symmetrical with respect to its origin, the k-space signal corresponding to the real and imaginary parts of the complex image can be synthesized independently by restricting the k-space signal to an even function or an odd function. The proposed method involves random but symmetrical k-space acquisition and independent reconstruction of the real and imaginary parts of images using the real-valued constraint.

2605.   Compressive Diffusion MRI – Part 3: Prior-Image Constrained Low-Rank Model (PCLR)
Hao Gao1,2, Longchuan Li3, and Xiaoping P. Hu3
1Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia, United States, 2Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, United States, 3Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States

In another submitted abstract “Compressive Diffusion MRI – Part 1: Why Low-Rank?”, we compared several sparsity models and found that the low-rank (LR) model is the most suitable for diffusion MRI. This abstract introduces the Prior-image Constrained LR (PCLR) model, through which prior images can be efficiently incorporated to improve LR. In addition, a simple-to-implement and efficient algorithm has been developed to solve PCLR. The application of PCLR to diffusion MRI, with the prior images that are different from the images to be reconstructed, showed that PCLR performs better than LR.

2606.   Self-Updating NonLocal Total Variation for Highly Undersampled Variable Density Spiral Reconstruction
Sheng Fang1, Wenchuan Wu2, Kui Ying3, and Hua Guo2
1Institute of nulcear and new energy technology, Tsinghua University, Beijing, China, 2Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 3Department of engineering physics, Tsinghua University, Beijing, China

A Nonlocal Total variation (NLTV) that automatically refines the image-dependent weights was proposed for reconstructing highly undersampled variable density spiral (VDS) imaging data. Unlike existing NLTV-related method, the proposed method doesn¡¯t rely on a reference image for weight map estimation. Instead, it automatically updates the weight based on a filtered intermediate image. The wavelet soft shrinkage method is used for the filtering step. Since the aliasing artifact of VDS is incoherent, it can be expected that the shrinkage can perturbation of aliasing artifact and increase the accuracy of weight computation. The in vivo VDS experiment demonstrates that the proposed method can effectively suppress noises amplification and perverse better image details than TV.

2607.   Fast Reconstruction of 3D LGE Images of the Left Atrium in a Compressed Sensing Framework Using Split Bregman
Srikant Kamesh Iyer1,2, Tolga Tasdizen3, Nathan Burgon4, Ganesh Adluru2, and Edward V.R. DiBella2
1Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, United States, 2UCAIR/Radiology, University of Utah, Salt Lake City, Utah, United States, 3SCI, University of Utah, Salt Lake City, Salt Lake City, Utah, United States, 4CARMA, Department of Internal Medicine, University of Utah, Salt Lake City, Salt Lake City, Utah, United States

Acquiring Late Gadolinium Enhanced (LGE) images of the left atrium is a valuable tool in assessing the degree of fibrosis in the left atrium. The current method of acquiring high resolution 3D Cartesian inversion recovery data with ECG gating and respiratory navigator is inherently time consuming. Advances in compressed sensing have made it possible to speed up acquisition by acquiring less data while maintaining image quality by using prior information about the underlying image as constraints in the reconstruction. Total variation is one such popular constraint used. The nonlinearity and poor conditioning of such L1 regularization based reconstruction schemes makes minimization using traditional schemes like gradient descent very slow. We propose to use the Split Bregman approach to reconstruct LGE images of the LA in a compressed sensing framework to achieve rapid reconstructions for high acceleration factors

2608.   Fast Non-Convex Statistical Compressed Sensing MRI Reconstruction Based on Approximated Lp(0<p<1))-Qusi-Norm with Fewer Measurements Than Using L1-Norm

Il Yong Chun1 and Thomas Talavage1,2
1School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, United States, 2Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States

We propose a fast constrained L(p,¥å)-L2-norm (L(p,¥å) is an approximated Lp-qusi-norm) minimization algorithm, based on 1) p- and ¥å-dependent weighting techniques, and 2) an efficient split Bregman-based (known to have rapid convergence, especially with an L1-norm ) reweighted L1-minimization algorithm. This L(p,¥å)-L2-norm minimization achieves exact reconstruction from fewer measurements than are required for the L1-L2-norm case.

2609.   Empirical Investigation of the Gardner Transform as a Sparsifying Transform for the Analysis of a New Class of Signals Using Compressed Sensing
Jordan Woehr1 and Michael Smith1,2
1Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada, 2Radiology, University of Calgary, Calgary, Alberta, Canada

Compressed sensing (CS) applied to under-sampled k-space is used in magnetic resonance to decrease 2D and 3D imaging times while maintaining image resolution. We present the Gardner transform (GT) as a potential sparsifying transform for use with CS with under-sampled or truncated signals in a non-k-space 4th dimension, e.g. time or frequency. New classes of signals can be sparsified with the GT such as sums of exponentials, Lorentzians, etc. We discuss the practical issues associated with GT-CS reconstruction of a simulated signal assumed to have two exponential components, MTTGM and MTTWM, and an ideal GT of two Dirac delta functions.

PROMISE: Parallel Reconstruction with Optimized Acquisition for Multi-Contrast Imaging in the Context of Compressed Sensing
Enhao Gong1, Feng Huang2, Kui Ying3, Wenchuan Wu4, Shi Wang3, Chun Yuan4,5, and George Randy Duensing6
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Philips Healthcare, Shanghai, China, 3Department of Engineering Physics, Tsinghua University, Beijing, China, 4Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, 5Department of Radiology, University of Washington, Seattle, WA, United States, 6Philips Healthcare, Gainesville, FL, United States

In this work, PROMISE (Parallel Reconstruction with Optimized acquisition for Multi-contrast Imaging in the context of compressed Sensing) is proposed to use manifold sharable information between multi-contrast scans for fast imaging. With the assumption that the same FOV is scanned for multi-contrast MRI, coil sensitivity maps, image structural information and optimal acquisition trajectory were extracted in seconds from previously acquired/reconstructed data to enhance the reconstruction of the following scans. Compared to previous work, PROMISE used more sharable information and resulted in lower artifact/noise level at higher reduction factors. Moreover, PROMISE is able to tolerate inter-scan motions better and is more clinically applicable.

2611.   Pseudo-Random Center Placement O-Space Imaging: Optimizing Incoherence for Compressed Sensing
Leo K. Tam1, Gigi Galiana2, Jason P. Stockmann3, Andrew Dewdney4, Terence W. Nixon2, Dana C. Peters2, and Robert Todd Constable2,5
1Biomedical Engineering, Yale University, New Haven, CT, United States, 2Diagnostic Radiology, Yale University, New Haven, CT, United States, 3Martinos Center, Massachusetts General Hospital, Boston, Massachusetts, United States, 4Siemens AG Healthcare, Erlangen, Bavaria, Germany, 5Neurosurgery, Yale University, New Haven, CT, United States

O-space imaging has shown distributed artifacts due to non-linear encoding via spatially-varying center placements (CPs). The success of non-linear encoding methods in the image domain lead to development of an approach to maximize incoherence in a sparse transform domain such as the Daubeuchies wavelets. By pseudo-randomizing CPs, an incoherence optimized O-space acquisition produced superior reconstructions under a compressed sensing framework.

2612.   Ultra-Fast Variable Density Spiral Imaging Technique Using Multiscale CORNOL Reconstruction
Sheng Fang1, Wenchuan Wu2, Kui Ying3, and Hua Guo2
1Institute of nulcear and new energy technology, Tsinghua University, Beijing, China, 2Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 3Department of engineering physics, Tsinghua University, Beijing, China

An ultra-fast variable density spiral (VDS) imaging technique using multiscale CORNOL (coherence regularization using a nonlocal operator) reconstruction is proposed. The multiscale CORNOL circumvents the conflict of large-scale artifact suppression and fine-scale structure perseveration of nonlinear reconstruction by sequentially handling these two tasks with smoothness constraint at different scales. This sequential procedure enables further reduction both residual aliasing artifact and ring-like artifact of VDS with well-preserved image details. Both simulation and in vivo experiment results demonstrate that the proposed method can suppress both large-scale artifacts and noise while preserving image details well at high sampling reduction factors.

2613.   Accelerated CEST MRI Using Compressive Sensing and Multishot Spiral Acquisitions
Sampada Bhave1, Jinsuh Kim1, Casey P. Johnson1, and Mathews Jacob1
1University of Iowa, Iowa City, Iowa, United States

A novel method based on multishot variable density spiral acquisitions and compressive sensing is introduced to reduce the scan time in high spatial resolution quantitative CEST imaging. Variable density acquisitions provide acceptable signal to noise ratio, even when high resolution acquisitions are used. The k-space data of each of the z-planes is undersampled by skipping the interleaves of the spiral trajectory. The recovery of the entire spatial spectral dataset is posed as a sparse optimization scheme. The total variation prior is used for spatial regularization, while the l1 norm of the second order temporal derivatives are used to exploit the smoothness of z-spectra.

2614.   Evaluation of Systematic and Statistical Reconstruction Errors in Compressed Sensing Reconstructions
Daniel Stäb1, Tobias Wech1, Dietbert Hahn1, and Herbert Köstler1
1Institute of Radiology, University of Würzburg, Würzburg, Bavaria, Germany

In CS, metrics for evaluating the image quality are hardly available. In this work, a simple Monte Carlo approach is presented that allows evaluating systematic and statistical reconstruction errors. Based on a fully sampled reference acquisition and a noise measurement, multiple pseudo measurements are synthesized. By comparing their reconstructions to the reference, systematic signal deviations can be quantified. In addition, the extent of statistical fluctuations can be estimated. The proposed evaluation was applied to a x-f-space CS reconstruction in myocardial perfusion MRI and systematic flattening of the signal intensity time courses was detected.

2615.   Quantitative Evaluation of 3D Variational Regularized Reconstruction of Undersampled Diffusion Tensor Imaging
Florian Knoll1, Rafael O'Halloran2, Kristian Bredies3, Rudolf Stollberger1, and Roland Bammer2
1Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 2Radiology, Stanford University, Palo Alto, California, United States, 3Department of Mathematics and Scientific Computing, University of Graz, Graz, Styria, Austria

Diffusion Tensor Imaging is a demanding application requiring the acquisition of many image volumes to extract the desired tensor parameters. k-space undersampling is a straightforward method that can be used to reduce the total scan time, however, if the undersampled data is reconstructed with conventional methods such as gridding, artifacts result. Parallel imaging and compressed sensing are successful in reducing undersampling but it is not clear what effect nonlinear regularization terms have with respect to quantitative evaluation of the images, as performed in Diffusion Tensor Imaging. Here the quantitative accuracy of a 3D spiral acquisition using nonlinear regularization is evaluated in a simulated atlas-based DTI phantom.

2616.   Investigating the Quantitative Fidelity of Prospectively Undersampled Chemical Shift Imaging with Compressed Sensing and Parallel Imaging Reconstruction
Kieren Grant Hollingsworth1 and David M. Higgins2
1Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, Tyne and Wear, United Kingdom, 2Philips Healthcare, Guildford, Surrey, United Kingdom

Compressed sensing (CS) and parallel imaging (PI) have been successfully applied to the problem of water-fat separation, providing valuable savings in acquisition time. However, it has not been shown that quantitative fat fraction is preserved. Fully sampled and prospectively undersampled (4.4x) 3D gradient echo scans were performed on the lower leg of a volunteer, reconstructed by CS and PI and fat fraction maps produced. 4 regions-of-interest were considered across 5 axial levels and compared individually and by Bland-Altman analysis. No significant difference was found in the fat fraction for the regions and no bias between fully-sampled and undersampled data.

2617.   Residual Reordering for Motion Compensated Compressed Sensing Cardiac Perfusion MR Imaging
Huisu Yoon1, Ganesh Adluru2, Edward V.R. DiBella2, and Jong Chul Ye1
1Department of Bio and Brain Engineering, KAIST, Daejeon, Korea, 2Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, UT, United States

k-t FOCUSS with motion estimation and compensation is a promising tool for highly accelerated dynamic MRI. One of the limitations of k-t FOCUSS with ME/MC is, however, that the motion residual signals are often contaminated with background noises, so the reconstruction of the residual signal using standard l1 sparsity constraint are often inefficient in capturing the physiological features and results in temporal blurring. In this work, we exploit the reordering algorithm to make the residual reconstruction more efficient. Using cardiac perfusion imaging, the spatio-temporal constrained reconstruction using re-ordering was found effective for reconstruction of the motion residual in k-t FOCUSS with ME/MC. By combining k-t FOCUSS ME/MC, the ordering based residual reconstruction may be a useful tool for compressed sensing MRI.

2618.   Accelerated High-Resolution MR Angiography of Fingers with Compressed Sensing
Wingchi Edmund Kwok1,2, Yue Hu3, Zhigang You1, and Mathews Jacob4
1Department of Imaging Sciences, University of Rochester, Rochester, NY, United States, 2Rochester Center for Brain Imaging, University of Rochester, Rochester, NY, United States, 3Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States, 4Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States

MR angiography (MRA) of fingers is challenging due to the small size of blood vessels. Though high-resolution MRA may be obtained with dedicated RF coils, the associated long scan time and limited coverage hinder applications. Therefore, we evaluated the feasibility of applying compressed sensing on high-resolution finger MRA to save scan time. Our results show that compressed sensing can significantly reduce scan time while preserving blood vessel information, facilitating the clinical application of high-resolution finger MRA. Accelerated high-resolution finger MRA with compressed sensing should be useful for the diagnostic evaluation and pathogenesis studies of systemic sclerosis and arthritis.

2619.   Lower Extremities Perfusion Imaging with Low-Rank Matrix Completion Reconstruction
Jieying Luo1, Taehoon Shin1, Tao Zhang1, Bob S. Hu2, and Dwight G. Nishimura1
1Electrical Engineering, Stanford University, Stanford, California, United States, 2Palo Alto Medical Foundation, Palo Alto, California, United States

An accurate measurement of lower extremities perfusion is potentially of significant help in the assessment of peripheral arterial disease. This work investigates and optimizes the use of low-rank matrix completion reconstruction for this application. As verified using both numerical simulations and retrospectively undersampled in-vivo data, reconstruction performance is improved by the use of reference images and a complementary uniformly random undersampling pattern. With this method, volumetric perfusion imaging of the lower extremities with temporal resolution of 2 seconds can be achieved.

2620.   Accelerated fMRI Using Low-Rank Model and Sparsity Constraints
Fan Lam1,2, Bo Zhao1,2, Yinan Liu3, Zhi-Pei Liang1,2, Michael Weiner3,4, and Norbert Schuff3,4
1Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Center for Imaging of Neurodegenerative Diseases, Department of Veteran Affairs Medical Center, San Francisco, CA, United States, 4Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States

We present a new method for image reconstruction from undersampled data for accelerating fMRI data acquisition. The proposed method integrates a low-rank model of the fMRI image series and a sparsity constraint in a unified mathematical formulation, enabling high quality reconstruction of fMRI images from highly undersampled data. Representative results from simulations based on experimental data were used to demonstrate the performance of the proposed method.

2621.   Bayesian Compressive Sensing of Multishell HARDI for CSA-ODF Reconstruction
Julio Duarte-Carvajalino1, Christophe Lenglet1, Junqian Xu2, Essa S. Yacoub1, Kamil Ugurbil1, Steen Moeller1, Lawrence Carin3, and Guillermo Sapiro3
1Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 2Mount Sinai School of Medicine, New York, NY, United States, 3Dept. of Electrical and Computer Engineering, Duke University, Durham, NC, United States

This work introduces a novel multi-task Bayesian compressive sensing approach for the direct and joint estimation of white matter fiber orientation distribution function and diffusion-weighted volumes from under-sampled HARDI data.

2622.   Accelerated Myocardial Perfusion MRI Using Motion Compensated Compressed Sensing (MC-CS)
Sajan Goud Lingala1, Edward V.R. DiBella2, and Mathews Jacob1
1The University of Iowa, Iowa city, IA, United States, 2University of Utah, Salt lake city, UT, United States

We propose a novel motion compensated compressed sensing reconstruction scheme for myocardial perfusion MRI. We develop an efficient energy minimization framework that jointly estimates the motion and the dynamic images from undersampled data. Our preliminary results show that proposed scheme is able to considerably reduce motion related artifacts in temporally constrained compressed sensing reconstruction.

2623.   Model-Based Reconstruction for Physiological Noise Correction in Functional MRI
Matthew J. Muckley1,2, Scott J. Peltier1,2, Douglas C. Noll1,2, and Jeffrey A. Fessler3
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 2Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States, 3Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

A novel application of low rank methods combined with temporal Fourier sparsity regularization and random sampling for removal of physiological noise in functional MRI is presented. This approach has the potential to recover high temporal frequency characteristics of the physiological noise while sampling these signals well below the Nyquist rate on average. The method is validated in a resting state connectivity task, where it is used to reconstruct a data set with high spatiotemporal resolution before removing physiological noise using low pass filtering.

2624.   Fast 3D DCE-MRI with Sparsity and Low-Rank Enhanced SPIRiT (SLR-SPIRiT)
Tao Zhang1, Marcus T. Alley2, Michael Lustig3, Xiaodong Li4, John Pauly1, and Shreyas S. Vasanawala2
1Electrical Engineering, Stanford University, Stanford, California, United States, 2Radiology, Stanford University, Stanford, California, United States, 3Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, California, United States, 4Mathematics, Stanford University, Stanford, California, United States

Dynamic contrast enhanced MRI is commonly used to detect and characterize lesions. In this work, a method named Sparsity and Low-Rank enhanced SPIRiT (SLR-SPIRiT) is proposed to push the limit of spatial-temporal resolution in 3D DCE MRI. SLR-SPIRiT exploits parallel imaging, transform-sparsity, and locally low-rank property in the dynamic image series to reconstruct highly accelerated DCE datasets. The proposed method has been validated on a pediatric DCE dataset with 1x1x2 mm3 spatial resolution and 3-second temporal resolution (actual acceleration 19.7).

2625.   Application of Compressed Sensing to Minimize Pulsation Artifacts and Distortions in High Resolution Time-Of-Flight Imaging at 7 Tesla MRI
Anders Garpebring1, Maarten J. Versluis2,3, and Matthias J.P. van Osch2,3
1Radiation Sciences, Umeå University, Umeå, Sweden, Sweden, 2Radiology, Leiden University Medical Center, Leiden, Zuid-Holland, Netherlands, 3CJ Gorter Center for high field MRI, Leiden University Medical Center, Leiden, Zuid-Holland, Netherlands

Ultra high field MRI time-of-flight angiograms can often be seriously degraded by pulsation artifacts. A method based on random order sampling, retrospective gating and L1-SPIRiT reconstruction was developed and tested on a 7 T scanner. The results confirmed that the proposed method can reduce the flow artifacts.

2626.   Improved Compressed Sensing Using Parallel Imaging: TGRAPPA-PRISM for Cardiac Cine MRI
Da Wang1, Stanislas Rapacchi2, Hao Gao3, and Peng Hu4
1Biomedical Physics/Radiological Sci, UCLA, Los Angeles, CA, United States, 2UCLA, Los Angeles, CA, United States, 3Emory University, Atlanta, GA, United States, 4University of California Los Angeles, Los Angeles, CA, United States

A novel compressed sensing MRI reconstruction method has been proposed for dynamic MRI using Prior Rank, Intensity and Sparsity Model (PRISM). By using a low rank decomposition, PRISM can extract the stationary background component from dynamic images to further promote sparsity of the motion component for L1 norm minimization. The combination of parallel MRI methods with compressed sensing methods has shown great potential to improve the reconstructed image quality and acceleration rate. We propose to further improve PRISM compressed sensing algorithm by using TGRAPPA to fill in additional data lines in the k-space before feeding to the PRISM algorithm.

2627.   Sparsity-Enforced Kalman Filter Technique for Dynamic Cardiac Imaging
MingJian Hong1, Feng Liu2, XiaoHong Zhang1, and YongXin Ge1
1School of Software Engineering, ChongQing University, ChongQing, ChongQing, China, 2School of Information Technology & Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia

In this work, a sparsity-enforced Kalman filter technique for dynamic cardiac imaging is presented. The Kalman filter is firstly casted into a framework of optimization, and then a sparsity constraint is incorporated to the framework for better motion capture of the imaging object. Applications to cardiac dynamic MRI clearly demonstrated the strength of the proposed method.

2628.   Wavelet Based Multiscale Selection CS Reconstruction for Multi-Contrast MR Images
Sehoon Lim1 and Dosik Hwang2
1SRI International Sarnoff, Princeton, NJ, United States, 2School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea

A set of different contrast MR images are usually necessary for proper diagnosis. However, the multiple acquisitions of several contrast images require long scanning time. In this study, we propose an efficient multimode compressed sensing (CS) framework to reduce the total scanning time by undersampling the k-space data of each contrast (mode) image and reconstructing artifact-minimized images. In contrast to other groups, we used a wavelet based multiscale selection CS technique to alleviate computational burden. In our studies, the running time of the proposed wavelet multimode CS is presented as a minute, which is promising for agile and compact applications.


Wednesday, 24 April 2013 (10:00-12:00) Exhibition Hall
Image Reconstruction: Non-Cartesian & Parallel Imaging

2629.   Phase Contrast (PC) MR Image Reconstruction Using Complex Expectation Maximization (EM)
Joonsung Choi1, Yeji Han1, and HyunWook Park1
1Department of Electrical Engineering, Korean Advanced Institute of Science and Technology (KAIST), Daejeon, Korea

The highly-constrained projection reconstruction (HYPR)-based algorithms provided high spatio-temporal images by using the composite information. However, HYPR-based methods have limitation that only can reconstruct magnitude image. In the proposed method, we provide a novel method to recontruct complex-valued image and apply the method to phase contrast imaging.

2630.   nuFFTW: A Parallel Auto-Tuning Library for Performance Optimization of the NuFFT
Mark Murphy1, Michal Zarrouk2, Kurt Keutzer2, and Michael Lustig2
1Google, Mountain View, CA, United States, 2EECS, UC Berkeley, Berkeley, CA, United States

We present a fast, autotuned, Gridding-based non-uniform FFT library with parallel implementions on CPUs and GPUs for reconstructing from non-Cartesian data. The influence of a nuFFT implementation and parameter selection on the resulting runtime is non-trivial. Our auto-tuning approach empirically selects an optimal implementation per trajectory by searching over algorithms and parameters, and saves it for future reconstructions (i.e. parallel imaging). We show that the optimal implementation depends also on the target platform and the sampling pattern itself. We also present a heuristic for near-optimal selection when exhaustive search is prohibitively expensive.

2631.   3D Through-Time Radial GRAPPA with In-Plane and Through-Plane Acceleration
Jesse I. Hamilton1, Katherine L. Wright1, Kestutis Barkauskas1, Vikas Gulani2, and Nicole Seiberlich1
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Radiology, Case Western Reserve University, Cleveland, OH, United States

Previous work has shown that through-time non-Cartesian GRAPPA can reconstruct images with high radial acceleration factors. Here we demonstrate that 3D through-time radial GRAPPA can reconstruct data accelerated not only in-plane but also through-plane to yield high spatiotemporal resolutions. Reconstruction was performed with a 3D kernel, and calibration data were amassed using fully-sampled data and repeating the kernel through-k-space, through-time, and through-partitions to calculate accurate non-Cartesian GRAPPA weights. Time-resolved renal MR angiography data with total acceleration R=12 (R=6 in-plane, R=2 through-plane) were reconstructed at 1.45x1.45x3.00 mm3 spatial resolution and 2 s/frame temporal resolution (after retrospectively undersampling partitions).

2632.   Exploring the Bandwidth Limits of ZTE Imaging
Markus Weiger1, David Otto Brunner1, Martin Tabbert2, Matteo Pavan1, Thomas Schmid1, and Klaas P. Pruessmann1
1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 2Bruker BioSpin MRI GmbH, Ettlingen, Germany

MRI of short T2 samples can be very efficiently performed with zero echo time (ZTE) imaging. ZTE data are incomplete in the k-space centre due to the initial RF dead time Δ, which can be addressed by algebraic reconstruction. The ZTE approach has been used for imaging T2s of several hundreds of µs. Targeting even shorter T2, however, increases the bandwidth and in turn the relative ∆, and images with large ∆ generally exhibit low-frequency artefacts. Therefore, the spatial response of ZTE as a function of ∆ is investigated and the practical bandwidth limits are explored by simulations and experiments.

2633.   Voxel Function and Signal-To-Noise Ratio (SNR): What Are the Optimal Reconstruction Method and Sampling Strategy in SENSE Imaging?
Marcel Gutberlet1, Mario Zeller2, Frank Wacker1, and Herbert Köstler2
1Institute of Radiology, Medical School Hannover, Hannover, Lower Saxony, Germany, 2Institute of Radiology, University of Würzburg, Würzburg, Bavaria, Germany

In the theory of SENSE imaging the reconstruction matrix is defined by choosing a set of desired voxel functions. The resulting voxel functions are either generated in the strong voxel approach (SVA) by a least squares minimization or in the weak voxel approach (WVA) by constraining the orthonormality relation to the desired voxel functions. In k-space density weighted imaging for the chosen desired voxel function the signal-to-noise ratio (SNR) is maximized by applying an optimized k-space sampling. In this work, the WVA and SVA of SENSE imaging are evaluated in respect of SNR efficiency depending on the desired voxel functions.

2634.   Non-Iterative Bayesian Reconstruction Algorithm for Undersampled MRI Data
Gengsheng Lawrence Zeng1 and Edward V.R. DiBella1
1Radiology, University of Utah, Salt Lake City, UT, United States

A non-iterative Bayesian reconstruction algorithm is derived to reconstruct dynamic undersampled MRI images. The k-space is radially sampled and 24 lines are acquired at each time frame. The Bayesian constraint uses the combination of immediately-before, current, and immediately-after data (referred to as the secondary data) to assist the image reconstruction. Unlike the ad hoc HYPR-type methods, the proposed algorithm is analytically derived and is able to track the object motion. The secondary data must be pre-filtered with a ramp filter before a small fraction of it is added to the current data for image reconstruction, with a modified ramp filter.

2635.   Symmetric Vs. Asymmetric Undersampling in 3D Cones Imaging
Michael Carl1, James H. Holmes2, and Graeme C. McKinnon3
1GE Healthcare, San Diego, CA, United States, 2GE Healthcare, Madison, WI, United States, 3GE Healthcare, Waukesha, WI, United States

In radial out sequences, isotropic undersampling the number of k-space spokes is a popular way to accelerate the acquisition time. In 3D Cones, the trajectories inherently lie on conical surfaces and therefore allow k-space trajectories to support asymmetric FOVs and undersampling. Here we investigated the image quality tradeoffs for asymmetric undersampling under constrain of a fixed total same scan time. We found that increasing the undersampling along the symmetry axis of the Cones surfaces (z-axis) increases the artifact appearance, while reduced artifacts were observed when the overall undersampling was performed preferentially in the x-y plane.

Accelerated 3D Radial Imaging with 3D Variational Regularization
Florian Knoll1, Kai Tobias Block2, Kristian Bredies3, Clemens Diwoky1, Leon Axel4, Daniel K. Sodickson4, and Rudolf Stollberger1
1Institute of Medical Engineering, Graz University of Technology, Graz, Styria, Austria, 2Center for Biomedical Imaging, NYU Langone Medical Center, New York, NY, United States, 3Department of Mathematics and Scientific Computing, University of Graz, Graz, Styria, Austria, 4Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States

Iterative parallel-imaging methods are highly promising for MR image reconstruction from undersampled data due to their flexibility to incorporate a priori knowledge using regularization. However, these methods are computationally very expensive and memory demanding. Consequently, most implementations so far used acquisition schemes that allow separating the reconstruction into smaller sub-problems, e.g. by reconstructing 3D volumes slice by slice. This comes at the expense of loosing acceleration capability in this direction, which limits the achievable overall scan efficiency. Furthermore, for certain imaging techniques like 3D radial ultra-short TE (UTE) imaging, separation of the reconstruction is not feasible. In this work, we present a method that treats the whole 3D imaging volume as single data set. This enables completely arbitrary 3D trajectories with acceleration in any dimension and incorporation of fully 3D regularization functionals.

2637.   Multi-Contrast JSENSE
Xiaodong Ma1, Feng Huang2, Chun Yuan1,3, George Randy Duensing4, and Hua Guo1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2Philips Healthcare, Beijing, China,3Department of Radiology, University of Washington, Seattle, WA, United States, 4Philips Healthcare, Gainesville, FL, United States

Since using all the calibration signals from images with different contrasts potentially provides more information of coil sensitivities, we propose to use multi-contrast information to enhance JSENSE. The same coil sensitivities as well as multiple contrast images are jointly reconstructed in the new model. Preliminary results demonstrated that the multi-contrast JSENSE algorithm with more accurate initialization results in images with improved quality, while costs no more computational time than original JSENSE algorithm

2638.   Augmented JSENSE: Faster Convergence and Less Sensitive to Regularization Parameter
Meng Liu1, Yunmei Chen1, Yuyuan Ouyang1, Xiaojing Ye2, Xiaodong Ma3, and Feng Huang4
1Department of Mathematics, University of Florida, Gainesville, FL, United States, 2School of Mathematics, Georgia Institute of Technology, Atlanta, GA, United States,3Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, 4Philips Healthcare, Shanghai, China

Partially parallel imaging has been used routinely for many MR applications. SENSE is one of the most commonly used methods, theoretically resulted in the optimal signal-to-noise ratio. However, SENSE reconstruction is highly depending on the accuracy of coil sensitivity maps. Several iterative methods were proposed to jointly reconstruct image and estimate sensitivity maps, have demonstrated the improved accuracy of coil sensitivity maps and the SENSE reconstruction quality. However, they suffer two numerical problems. One is the sensitivity to choosing regularization parameters; the other is the high computational cost. The target of this work is to tackle these two existing issues.

2639.   Parallel Imaging by Multi-Band Spatiotemporal Encoding and a Combined Super-Resolved / SENSE Reconstruction
Rita Schmidt1, Bikash Baishya1, Noam Ben-Eliezer2, Amir Seginer1, and Lucio Frydman1
1Chemical Physics, Weizmann Institute of Science, Rehovot, Israel, 2Center for Biomedical Imaging, New York University, New York, NY, United States

Recent studies described the potential of single shot methods based on spatiotemporal encoding (SPEN) principles. SPEN displays a higher robustness to frequency offsets. An important step still needed to endow SPEN scanning involves incorporating into the sequence the parallel imaging capabilities –without compromising the achievements of the SPEN super resolution reconstruction. The present work demonstrates the use of multi-band chirp pulses, to simultaneously encode multiple partial field-of-views. This approach is combined with a super-resolved SENSE-based method to reconstruct the full FOV image. The performance of the scanning and the reconstruction was explored in phantoms and in human imaging at 3T.

2640.   GPU-Enabled Individualized Acceleration Apportionment for SENSE and CAIPIRINHA
Eric A. Borisch1, Paul T. Weavers1, and Stephen J. Riederer1
1Mayo Clinic, Rochester, MN, United States

Improving the performance of 2D-accelerated 3D acquisitions by determining an individualized acceleration selection, (RY, RZ) for SENSE or (RY, RZ,Delta) for CAIPIRINHA, for each patient/coil combination is an area of active research. Determining what is the optimal choice of accelerations is a compute- and time-intensive step. To enable a clinical practice-compatible (fast) process, we have implemented a GPU-based optimization routine, enabling a wide range of potential acceleration choices to be examined in under 10 seconds. The process of converting these calculations to a GPU as well as observed performance improvements are discussed.

2641.   A Dictionary-Based Graph Cut Algorithm for MRI Reconstruction
Jiexun Xu1, Nicolas Pannetier2, and Ashish Raj3
1Department of Computer Science, Cornell University, Ithaca, New York, United States, 2Department of Radiology and Department of Veterans Affairs Medical Center, University of California at San Francisco, San Francisco, CA, United States, 3Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States

Among recent parallel imaging techniques, a Bayesian method that uses Cartesian under-sampling and sophisticated edge-preserving priors (EPP) have demonstrated its success in clinical applications. Recent compressive sensing related methods have proposed random under-sampling schemes that makes denoising and removing aliasing artifacts much easier. In this work we combine the strengths of both methods and propose a novel algorithm to solve the resulting problem, and demonstrate that our algorithm out performs popular existing methods.

2642.   Improved Balance of Artifact/noise Level and Fine Structure Preservation in Highly Accelerated PPI
Dengrong Jiang1, Kui Ying1, and Feng Huang2
1Engineering Physics, Tsinghua University, Beijing, Beijing, China, 2Philips Healthcare, Beijing, Beijing, China

Partially Parallel Imaging (PPI) has been widely used in clinical practice to accelerate acquisition, but at the cost of reduced Signal-to-Noise Ratio (SNR). Often, regularization schemes are used to preserve SNR. However, existing regularization schemes have the difficulty to balance SNR and the preservation of boundaries and fine structures, especially when the acceleration factor is high. In this work, we adopted non-local sparse as the regularization term for PPI and achieved better balance of SNR and fine structure preservation compared with CS-SENSE. Low errors image was reconstructed at acceleration factor as high as 8 with an 8-channel head coil.

2643.   Efficient Non-Cartesian SPIRiT Without Explicit Consecutive Regridding and Gridding
Claudio Santelli1,2, Tobias Schaeffter1, and Sebastian Kozerke1,2
1Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom, 2Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland

A modified coil-sensitivity based calibration operator was incorporated into non-Cartesian CG-like SPIRiT. While maintaining image quality, significant reduction in reconstruction time has been demonstrated for simulated spiral and radial data. In addition, the exchangeability of the two consecutive k-space interpolation steps with a diagonal matrix multiplication has been shown. Depending on the number of k-space samples, reconstruction times on the order of the highly optimized NUFFT gridder are achieved.

2644.   Improved Compressed Sensing and Parallel MRI Through the Generalized Series Modeling
Xi Peng1,2, Leslie Ying3, Xin Liu1,2, and Dong Liang1,2
1Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China, 2Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China, 3Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, New York, United States

The problem of reconstructing a high-spatial-temporal-resolution MR image sequence occurs in various MR applications, such as interventional imaging, dynamic contrast enhanced imaging, cardiac imaging, where a static reference image can be obtained with relative ease before the whole dynamic process. This work addresses the problem by integrating the generalized series (GS) model, in which the reference prior is incorporated, with standard compressed sensing (CS) and parallel imaging (PI) techniques. The proposed method is validated in a Monte-Carlo study and is shown to provide superior imaging quality with decreased g-factor to existing CS and PI based reconstruction methods.

2645.   Memory-Saving Iterative Reconstruction on Overlapping Blocks of K-Space
Martin Uecker1 and Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California, United States

The purpose of this work to develop memory-efficient implementations of iterative reconstruction algorithms, such as SENSE, nonlinear inversion, and ESPIRiT. Although iterative reconstruction provides faster imaging and improved image quality, high computational demand currently limits its application for large 3D data sets. Graphical processing units can reduce computation time, but do not have enough memory for conventional algorithms. The proposed technique exploits the typical structure of iterative reconstruction algorithms to divide the computation into small independent blocks to reduce memory consumption.

2646.   Sequential-Segment Multi-Shot Auto-Calibration for GRAPPA EPI: Maximizing Temporal SNR and Reducing Motion Sensitivity
Jonathan R. Polimeni1, Himanshu Bhat2, Thomas Benner3, Thorsten Feiweier4, Souheil J. Inati5, Thomas Witzel1, Keith A. Heberlein6, and Lawrence L. Wald1,7
1A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States, 2Siemens Medical Solutions, Charlestown, MA, United States, 3Siemens AG, Healthcare Sector, Erlangen, Bavaria, Germany, 4Siemens AG, Erlangen, Bavaria, Germany, 5National Institute of Mental Health, Bethesda, MD, United States, 6Siemens Healthcare USA, Charlestown, MA, United States, 7Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA, United States

Conventional auto-calibration data acquisition strategies for accelerated EPI seek to match the echo spacing of the image data, and therefore employ a segmented EPI acquisition. This approach is vulnerable to patient movement or respiration-induced dynamic B0 changes. Here we introduce a new auto-calibration method based on acquiring multi-shot, multi-slice auto-calibration data where the multiple segments in a given slice are acquired sequentially in time, shortening the time interval between segments. Our results show that the proposed method provides higher temporal SNR and reduced motion sensitivity that the conventional approach, while maintaining the echo spacing of the image acquisition.

2647.   A G-Factor Metric for K-T SENSE and K-T PCA
Christian Binter1, Rebecca Ramb2, Bernd Jung2, and Sebastian Kozerke1,3
1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 2Dept. of Radiology, Medical Physics, University Medical Center, Freiburg, Germany,3Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom

Spatiotemporal undersampling methods allow for significant speed-up of image acquisition. This work aims at providing an analytical assessment of noise behavior and temporal fidelity of k-t SENSE and k-t PCA. The g-factor formalism introduced for parallel imaging (SENSE, GRAPPA) is extended to the temporal frequency domain. Using in-vivo data it is demonstrated that the proposed g-factor shows good agreement with results obtained by pseudo-replica analysis for both k-t SENSE and k-t PCA, and also matches the temporal transfer function for k-t SENSE.

2648.   Dynamic Parallel-Imaging Reconstruction with Image-Block Dictionaries
Eric Y. Pierre1, Nicole Seiberlich1, Vidya Nadig2, and Mark A. Griswold3
1Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States, 2Heart and Vascular Center, MetroHealth Campus of Case Western Reserve University, Cleveland, Ohio, United States, 3Radiology, Case Western Reserve University, Cleveland, Ohio, United States

Dictionaries with spatial or temporal information shared with a target image are often used as a sparsifying transform to improve Compressed Sensing reconstruction results. However in accordance to Parallel Imaging principles, the coil sensitivity information within these dictionaries could also be directly exploited to reconstruct undersampled images with a light computational load. A new reconstruction scheme which exploits image-block dictionaries extracted from a calibration data is proposed. In vivo reconstruction results are shown for a dynamic cardiac experiment with radial acquisition accelerated by a factor 8.

2649.   Optimal Reconstruction Method of SENSE Imaging Depending on the Desired Voxel Function
Marcel Gutberlet1, Frank Wacker1, and Herbert Köstler2
1Institut of Radiology, Medical School Hannover, Hannover, Lower Saxony, Germany, 2Institute of Radiology, University of Würzburg, Würzburg, Bavaria, Germany

In the generalized theory of SENSE imaging the reconstruction matrix is defined by choosing a set of desired voxel functions. Either in the strong voxel approach (SVA) the resulting voxel functions are generated by a least squares minimization or in the weak voxel approach (WVA) by constraining the orthonormality relation to the desired voxel functions. In this work, the reconstruction accuracy of the SVA and WVA of SENSE imaging depending on the choice of the desired voxel functions is evaluated.

2650.   Multi-Scale Subband Weighted Partially Parallel Imaging
Suhyung Park1 and Jaeseok Park1
1Department of Brain and Cognitive Engineering, Korea University, Seoul, Seoul, Korea

Combination of partially parallel imaging (PPI) and compressed sensing (CS) [1-4] employs complementary properties of the two competitive methods. Among them, direct combination approaches [1-2], which jointly consider both CS and PPI constraints, potentially suffer from image artifacts at high acceleration, because sparsifying transform are less coherent with sensitivity encoding than Fourier encoding. Then, combination of CS and PPI in a sequential fashion [3-4] was recently introduced, demonstrating its feasibility in overcoming the aforementioned problems. In this work, we develop a novel, multi-scale subband weighted PPI algorithm, wherein 1) CS is utilized to yield multi-scale sparse solutions, 2) Subbands in each scale are employed to produce multiple de-noised filtered k-spaces, 3) Join estimation of PPI convolution kernels and k-spaces are performed, considering both inter-subband correlation and spatial correlation over multiple coils.

2651.   Quantitative G-Factor Calculation in K-T-GRAPPA Reconstructions
Rebecca Ramb1, Christian Binter2, Felix A. Breuer3, Gerrit Schultz1, Maxim Zaitsev1, Sebastian Kozerke2,4, and Bernd Jung1
1Dept. of Radiology, Medical Physics, University Medical Center, Freiburg, Germany, 2Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland,3MRB Research Center, Würzburg, Germany, 4Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom

The goal of this work is the analytical quantification of the noise enhancement in k-t-GRAPPA reconstructions from time-resolved k-t-accelerated acquisitions. Image reconstruction in x-f-space by transforming the three-dimensional convolution kernel is explained, the g-factor with respect to temporal frequencies is introduced and the method is validated in in-vivo-data by statistical noise estimation using the pseudo-replica method. In k-t-reconstruction temporal filtering is observed. The g-factor quantification in x-f-space for k-t-GRAPPA further reflects this and thus constitutes a measure for noise enhancement as well as the amount of temporal filtering introduced in the reconstruction process.

2652.   Elora: Enforcing Low Rank for Parallel MR Reconstruction
Jun Liu1, Axel Loewe1, Michael O. Zenge2, Alban Lefebvre1, Edgar Mueller2, and Mariappan S. Nadar1
1Imaging and Computer Vision, Siemens Corporation, Corporate Technology, Princeton, NJ, United States, 2MR Application & Workflow Development, Siemens AG, Healthcare Sector, Erlangen, Bavaria, Germany

Parallel imaging exploits the difference in sensitivities between individual coil elements in a receive array to reduce the number of gradient encodings required for imaging. SENSE and GRAPPA are two representative approaches. In this abstract, a new approach, called Elora is proposed. Elora implicitly uses coil sensitivities by estimating a low rank subspace from the calibration data, and then works by enforcing the low rank constraint on the sliding blocks of the k-space data.

2653.   A Random Projection Approach to Highly Efficient GRAPPA Reconstruction
Jingyuan Lyu1, Yuchou Chang2, and Leslie Ying1
1Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States, 2Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States

In GRAPPA, the computational time increases with the number of channels and the amount of ACS data. To address this issue, different from the existing approaches that compress the large number of physical channels to fewer virtual channels, we propose to use random projections to reduce the dimension of the problem in the calibration step. Experimental results show that randomly projecting the data onto a lower-dimensional subspace yields results comparable to those of traditional GRAPPA, but is computationally less expensive.

2654.   An Efficient Variable Splitting Based Algorithm for Regularized SENSE Reconstruction with Support Constraint
Mai T. Le1, Sathish Ramani1, and Jeffrey A. Fessler1
1EECS, University of Michigan, Ann Arbor, MI, United States

SENSE reconstruction for parallel MRI with random undersampling requires spatial regularization for improved image quality. Compressed sensing methods utilize sparsity promoting regularizers that demand computation intensive, non-linear optimization algorithms. Previous variable splitting based algorithms ignored prior information that patients are not rectangular. We formulate a regularized SENSE reconstruction that explicitly includes a support constraint in the problem formulation. We propose a specific variable splitting strategy that when combined with the augmented Lagrangian framework and alternating minimization yields an algorithm with simple, efficient, non-iterative update steps. Experiments with in-vivo data demonstrate the improved performance of this method.

2655.   Edge-Preserving Non-Iterative MAP SENSE MRI Reconstruction
Il Yong Chun1 and Thomas Talavage1,2
1School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, United States, 2Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States

We propose two pre-computation-allowable and non-iterative MAP SENSE reconstruction algorithms based on 1) a Gaussian Random Field (GRF) with non-zero mean and 2) a Huber-Markov Random Field (HMRF) with non-zero mean. Simulation results show that the non-iterative HMRF MAP regularization technique is more effective for edge preservation and residual aliasing artifact reduction than non-iterative GRF MAP and Tikhonov-type regularization methods.

2656.   Sparse Tikhonov-Regularized SENSE MRI Reconstruction
Il Yong Chun1 and Thomas Talavage1,2
1School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, United States, 2Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States

Here, we present a pre-computation-allowable sparse Tikhonov-regularized SENSE MRI reconstruction technique based on QR decomposition, fast regularization parameter estimation using a new L-curve , and sparse matrix representation. The simulation results show that it significantly reduces residual aliasing artifacts and noise amplification for ill-posed cases.

2657.   ESPIRIT-Based Coil Compression for Cartesian Sampling
Dara Bahri1, Martin Uecker1, and Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California, United States

While receiver arrays with many channels can increase parallel imaging acceleration and provide high signal-to-noise, processing the large datasets they produce is computationally demanding. Coil compression algorithms reduce, and denoise in the process, data from many coils into fewer virtual ones. Huang et al. proposed using principal component analysis to globally compress multi-coil k-space data. Zhang et al. developed an improved technique for Cartesian sampling by compressing locally along fully-sampled directions, but the method suffers in low-SNR sections of k-space. In this work we present an algorithm that compresses locally while remaining noise-robust.

2658.   Improved Temporal SNR of Accelerated EPI Using a FLASH Based GRAPPA Reference Scan
S. Lalith Talagala1, Joelle E. Sarlls1, and Souheil J. Inati2
1NMRF/NINDS, National Institutes of Health, Bethesda, MD, United States, 2FMRIF/NIMH, National Institues of Health, Bethesda, MD, United States

Current EPI based fMRI protocols frequently incorporate accelerated parallel acquisition techniques such as GRAPPA and SENSE . These techniques help to reduce EPI distortions and to increase the number of slices per TR. However, we have observed that the temporal SNR of GRAPPA EPI data can be highly inhomogeneous and highly compromised with certain EPI protocols. In this work, we show that the tSNR of GRAPPA accelerated EPI can be made more spatially uniform and enhanced by using a GRAPPA reference scan based on a FLASH acquisition scheme rather than an EPI acquisition scheme.

2659.   Simultaneous Channel Compression and Noise Suppression in Parallel MRI
Yuchou Chang1, Dong Liang2, and Leslie Ying3
1Electrical Engineering, University of Wisconsin - Milwaukee, Milwaukee, Wisconsin, United States, 2Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China, 3Department of Biomedical Engineering, Department of Electrical Engineering, State University of New York at Buffalo, Buffalo, New York, United States

To reduce the reconstruction complexity in parallel imaging, principal component analysis (PCA) has been used to compress large array coils into a new set of fewer virtual channels. In this study, a novel kernel (nonlinear) PCA approach is proposed to achieve noise suppression and channel reductions simultaneously. Using GRAPPA as the reconstruction method, experimental results demonstrate that the reconstruction from channels compressed by the proposed kernel PCA method has a higher SNR than those compressed by PCA or uncompressed conventional GRAPPA, while the proposed method takes almost the same computation time as the PCA method.

2660.   Comparison of an Iterative GRAPPA Method to Compressed Sensing
Lawrence Dougherty1, Walter R.T. Witschey2, Robert M, King1, and Gamaliel Isaac1
1Radiology, University of Pennsylvania, Philadelphia, PA, United States, 2Surgery, University of Pennsylvania, Philadelphia, PA, United States

An iterative GRAPPA method has been developed for use on non-Cartesian sampled data. The method uses repeated application of Cartesian GRAPPA interpolation following gridding. Optimal kernel size as well as multiple kernels are investigated. Using a radially undersampled data set, GRAPPA was compared to compressed sensing. The iterative GRAPPA approach is simple to implement and executes rapidly.

2661.   Dynamic Multiband Calibration for Improved Signal Fidelity
Steen Moeller1, Edward J. Auerbach1, Junqian Xu1, Christophe Lenglet1, Kamil Ugurbil1, and Essa S. Yacoub1
1Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, United States

The sensitivity of multiband separation to changes in the relative phase between simultaneously acquired slices is demonstrated. A phase estimation procedure is proposed and tested for a diffusion weighted acquisition. With a phase-updating strategy reduced signal fluctuations allows for improved detectability.


Wednesday, 24 April 2013 (10:00-12:00) Exhibition Hall
Image Reconstruction & Analysis

2662.   Discrete Tomography in MRI: A Proof of Concept
Hilde Segers1, Willem Jan Palenstijn1, Kees Joost Batenburg1,2, and Jan Sijbers1
1Vision Lab - dept. Physics, Universiteit Antwerpen, Antwerpen, Antwerpen, Belgium, 2Center for Mathematics and Informatics, Amsterdam, Noord-Holland, Netherlands

Segmentation refers to the classification of image pixels into distinct classes, typically based on their grey level. It is usually performed as a post-processing step on an MR magnitude image, which is influenced by reconstruction artifacts. In this abstract, we investigate the integration of reconstruction and segmentation into one single procedure. This combination is a regularized reconstruction problem where we exploit prior knowledge about the discreteness of the grey levels. Simulation results show that this integrated method yields better results than the conventional approach when the underlying truth is a discrete image.

2663.   Single Shot Multi-Dimensional Imaging Using Magnetic Field Monitoring and Including Maxwell Terms
Frederik Testud1, Daniel Gallichan2, Kelvin J. Layton3, Anna M. Welz1, Christoph Barmet4, Chris A. Cocosco1, Jürgen Hennig1, Klaas P. Pruessmann4, and Maxim Zaitsev1
1Medical Physics, University Medical Center Freiburg, Freiburg, Germany, 2Centre d'Imagerie BioMédicale, Ecole Polytéchnique Fédérale de Lausanne, Lausanne, Switzerland,3Electrical & Electronic Engineering, University of Melbourne, Parkville, Victoria, Australia, 4Institute for Biomedical Engineering, University and ETH Zürich, Zürich, Switzerland

Single shot multidimensional imaging using linear and non-linear enocoding fields auch as 4-Dimensional Radial In/Out or North West Echo Planar Imaging allow to combine the preservation the variability of spatial resolution and to avoid the total resolution loss in the center of the field of view. A field camera consisting of 16 field probes is used for trajectory calibration. Analytically derived Maxwell terms of the used PatLoc head insert gradient coil are used to choose an adequate set of basis functions to further improve the image quality.

2664.   Flexible Spatial Encoding Strategies Using Rotating Multipolar Fields for Unconventional MRI Applications
Jason P. Stockmann1,2, Clarissa Zimmerman3, Matthew S. Rosen2,4, and Lawrence L. Wald4
1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States, 2Department of Physics, Harvard University, Cambridge, MA, United States, 3Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States, 4Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States

Recently several encoding strategies have been proposed which use nonlinear spatial encoding magnetic fields (SEMs) to perform projection imaging, exploiting iterative matrix solvers for reconstruction. In the present work, we consider the case of generalized spatial encoding using a rotating multipolar field in the transverse plane, showing how a linear offset field and multiple receive coils can break the symmetry of an arbitrary nonlinear SEM, providing encoding throughout the FOV. This flexible encoding/reconstruction approach relaxes the need for a homogenous B0 field and linear gradient fields, opening the door to new, unconventional MR imaging systems.

2665.   Quantitative Evaluation of Non-Linear Reconstruction Methods in MRI
Matthias Schloegl1, Florian Knoll1, Katharina Gruber1, Franz Ebner2, and Rudolf Stollberger1
1Institute for medical Engineering, TU Graz, Graz, Austria, 2Universitätsklinik für Radiologie, Medizinische Universität Graz, Graz, Austria

This study investigates about the capability of image metrics as objective tool for quality evaluation of non-linear reconstruction methods in the context of compressed sensing. Metric rating of reconstructions with TGV, IRGN-TV, l1-SPIRiT and CGSENSE with Cartesian, radial and random sub-sampled trajectories was compared to that of six experienced radiologists, focusing on overall image quality and recognizability of anatomy and pathology. Datasets from several body regions affected by identified pathologies were selected. Good correlations were found for metrics based upon models of the human visual system as well as for common image metrics when calculated for a region of interest.

2666.   Automatic Model Recovery for MRSI Reconstruction
Jeffrey Adam Kasten1,2, François Lazeyras2, and Dimitri Van de Ville1,2
1Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, VD, Switzerland, 2Department of Radiology and Medical Informatics, Université de Genève, Geneva, GE, Switzerland

Model-based MRSI reconstruction often relies upon structural MR images to characterize the sample by specifying spectrally-homogenous compartments. However, either spatio-spectral disparities between the two modalities or model mismatch will lead to additional artifacts. We therefore consider a more data-driven approach in which the raw MRSI data itself is used to estimate the generating signal model, employing a general framework predicated on principal component analysis and spatial regularization. Phantom experiments show that our method can yield highly resolved spatial and spectral components, while simultaneously surmounting a number of limitations associated with traditional Fourier reconstruction.

2667.   Fast Diffusion-Guided QSM Using Graphical Processing Units
Owen L. Kaluza1, Amanda C. L. Ng2,3, David K. Wright4,5, Leigh A. Johnston5,6, John Grundy7, and David G. Barnes2
1Monash e-Research Centre, Monash University, Clayton, Victoria, Australia, 2Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia, 3Department of Electrical & Electronic Engineering, The University of Melbourne, Parkville, Victoria, Australia, 4Centre for Neuroscience, The University of Melbourne, Parkville, Victoria, Australia,5Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia, 6NeuroEngineering Laboratory, Dept. Electrical & Electronic Engineering, The University of Melbourne, Parkville, Victoria, Australia, 7Centre for Complex Software Systems and Services, Swinburne University of Technology, Hawthorn, Victoria, Australia

Diffusion-guided quantitative susceptibility mapping (QSM) is a new technique that promises improved mapping without the need for multiple-orientation (COSMOS) image acquisitions. However, the computation time for realistic image sizes on central-processing unit (CPU)-based supercomputers is prohibitively expensive. We have analysed the dQSM algorithm and developed an OpenCL-based implementation that runs on graphics processing unit (GPU)-based compute clusters. Our implementation yields identical results to the parallel CPU code, in drastically less time. Dual-GPU cluster nodes can compute the dQSM map 8 - 10 times faster when their GPUs are used compared to their multi-core CPUs. With this work, use of dQSM in research imaging facilities becomes practicable on quite modest computational facilities.

2668.   Predicting Image Quality of Under-Sampled Data Reconstruction in the Presence of Noise
Patrick Virtue1, Martin Uecker1, Michael Elad2, and Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California, United States, 2Computer Science, Technion - Israel Institute of Technology, Haifa, Israel

The results of under-sampling reconstruction algorithms are often compared to a fully-sampled reconstruction. This comparison is overly optimistic because even if the reconstruction removes the aliasing due to under-sampling, we would still have an inherent loss of SNR due to the reduced acquisition time. We present a process to predict image quality for a given reconstruction technique and under-sampling pattern. Using this prediction as a “gold standard” enables a fair comparison for reconstruction results and provides an efficient means of quickly assessing reconstruction algorithms and parameters.

2669.   An Estimation Method for Improved Reconstruction of MR Signal Parameters in Unilateral Scanner
Elad Bergman1, Arie Yeredor1, and Uri Nevo1
1Tel-Aviv University, Tel Aviv, Israel

This work shows the potential of post-processing estimation for SNR improvement in Unilateral NMR, aiding the use of such devices in bio-medical applications. We present a novel post-processing method to improve the SNR of the acquired signal in unilateral NMR scanners. We estimate the signal parameters from the noisy data with the weighted least square approach, and exploit more efficiently the inherently known characteristics of the NMR signal. The method was first develop and tested for T2 measurements with a CPMG-like sequence. Then, using a similar concept we further developed this method to improve the SNR of lateral slice–selective imaging scans.

2670.   Optimization and Acceleration of Multi-Band EPI Reconstruction Using the Reduced Reference K-Space Window
Wanyong Shin1, Erik B. Beall1, and Mark J. Lowe1
1Radiology Dept., Cleveland Clinic, Cleveland, Ohio, United States

Simultaneous excitation of multiple slices using multi-band (MB) radio-frequency (RF) excitation echo planar imaging (EPI) has shown potential to increase the spatial and temporal resolution. The Slice-GRAPPA method calculates a linear interpolation kernel for each slice of MB accelerated k-space and de-aliases images using the estimated kernel. It is known that the interpolation kernel size, acceleration factor (R value) and the number of reference lines (ACS line number) determine the reconstructed image quality in in-plane parallel imaging (PI) technique. Since Slice-GRAPPA employs the same basic principle as the GRAPPA technique, we hypothesize that the size of the interpolation kernel and data used to estimate the kernel could affect the performance of MB de-aliasing. In this study, we evaluate MB de-aliasing performance while varying the sizes of interpolation kernel size and fitted data using simulation and show considerable reductions in compute time are possible with no apparent loss in data quality.

2671.   Optimized Dynamic Contrast-Enhanced Imaging by View-Sharing PROPELLER
Tzu-Chao Chuang1, Hing-Chiu Chang2, Hsuan-Hung Huang1, and Ming-Ting Wu3,4
1Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, Taiwan, 2Brain Imaging and Analysis Center, Duke University, Durham, NC, United States, 3School of Medicine, National Yang-Ming University, Taipei, Taiwan, Taiwan, 4Radiology, Kaohsiung Veteran General Hospital, Kaohsiung, Taiwan, Taiwan

View-sharing PROPELLER (VS-Prop) with a pixel-based optimal blade selection (POBS) algorithm has been proposed to shorten the acquisition window during dynamic contrast imaging, leading to a higher spatiotemporal resolution or/and spatial coverage. In this work, flow phantom experiment was performed to evaluate the temporal and spatial accuracy. In addition, the result of first-pass dynamic contrast-enhanced (DCE) cardiovascular imaging on healthy volunteers was also presented.

2672.   A Solution to the Phase Problem in Adaptive Coil Combination
Souheil J. Inati1, Michael Schacht Hansen2, and Peter Kellman2
1National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States, 2National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States

We present an algorithm for adaptive combination of images from an array of MR coils that is suitable for applications in which complex valued images are required. This algorithm enforces smoothness in both the magnitude and phase of the estimated coil sensitivities and overcomes limitations inherent in previous methods.

2673.   Efficient Algorithm of B0 Drift Correction in Time Series of Phase Images
Andrzej Jesmanowicz1
1Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, United States

A correction method is presented that removes spatial artifacts related to magnetic field drift in MRI systems equipped with ferro-shim elements usually placed close to high power gradient systems. At high duty cycle of gradients the heat generated inside the magnet bore reduces the effectiveness of shim elements by shifting the temperature closer to the Curie point. In response to unequal initial radial distribution of these elements the new magnetic field is no more uniform than it was at room temperature. Higher order corrections are required to remove artifacts from the time-series of phase images.

2674.   An Eigen-Vector Approach for Coil Sensitivity Estimation in the 3D Scenario
Qiu Wang1, Jun Liu1, Michael O. Zenge2, Nirmal Janardhanan1, Edgar Mueller2, and Mariappan S. Nadar1
1Imaging and Computer Vision, Siemens Corporation, Corporate Technology, Princeton, NJ, United States, 2MR Application & Workflow Development, Siemens AG, Healthcare Sector, Erlangen, Bavaria, Germany

Parallel imaging achieves scan time reduction by utilizing the correlation among an array of receiver coils to reconstruct image from under-sampled data. For SENSE-type reconstruction, explicit estimation of the coil sensitivity map (CSM) is critical in achieving good image quality. When the data is acquired using 3D Cartesian sampling, one way for estimating the coil profiles is to decouple the data along the frequency encoding direction and then perform the estimation in a 2D manner, which, however, ignores the correlation between the calibration data in the frequency encoding direction. In this work, we propose a 3D CSM estimation method, extending the Eigen-Vector approach that was developed for the 2D scenario. Experiments show the coil profiles are smoother with proposed approach compared to the 2D estimation method, and good image quality has been achieved.

2675.   Reduction of Remained Artifacts in Alias-Free Reconstruction of MR Images
Satoshi Ito1 and Yoshifumi Yamada1
1Research Division of Intelligence and Information Sciences, Utsunomiya University, Utsunomiya, Tochigi, Japan

We have proposed a new image reconstruction technique in which images of an optional scale can be obtained and hence alias-free images can be reproduced from a single piece of data by applying a quadratic phase modulation to a Fourier imaging technique. Almost all the aliasing artifacts are removed, however, small aliasing artifacts that comes from the higher frequency components contained in the signal but not contribute to the down-scaled image are remained. In this paper, we propose a new alias-free reconstruction technique in which remaining aliasing artifact are reduced using SENSE-like algorithm.

2676.   A Generalized Series Approach to Sparsely-Sampled fMRI
Hien Nguyen1 and Gary H. Glover1
1Department of Radiology, Stanford University, Palo Alto, CA, United States

In high resolution functional MRI, it is often desirable to reduce the readout duration to make the acquired data less prone to T2* susceptibility artifacts at the expense of SNR. This can be achieved by undersampling k-space. However, the conventional Fourier transform-based reconstruction method suffers from undersampling artifacts such as high-frequency ringing and loss of resolution. In this work we propose a new imaging approach to fMRI with under-sampled data by exploiting the generalized series constraint in the penalized maximum-likelihood framework. The effectiveness of the method is characterized and illustrated by experiments at 3T.

2677.   High Resolution T2-Weighted Imaging with Whole Brain Coverage at 7 Tesla Using Multiband Slice Accelerated Spin Echo
Dingxin Wang1,2, Joseph Vu2, Essa S. Yacoub2, Kamil Ugurbil2, and Vibhas Deshpande3
1Siemens Medical Solution USA, Inc., Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States,3Siemens Medical Solutions USA, Inc., Austin, TX, United States

Our study demonstrates the feasibility of using slice accelerated SE sequence for acquiring high resolution T2-weighted images with whole brain coverage (72 slices, 2mm thickness). The slice acceleration and SAR reduction are the keys to enable increased coverage of the T2-weighted image acquisition within a reasonable time (8 minutes).

2678.   Evaluation of Multi-Band EPI in Resting State and Task fMRI Studies
Wanyong Shin1, Erik B. Beall1, and Mark J. Lowe1
1Radiology Dept., Cleveland Clinic, Cleveland, Ohio, United States

To accelerate 2D EPI, various methods have been proposed. One such promising method, simultaneous excitation of multiple slices using multi-band (MB) radio-frequency (RF) excitation, has caught the attention of many researchers because the number of excited slice simultaneously, (the MB factor), accelerates the acquisition’s possible temporal resolution by the MB factor. While the MB technique is expected to be beneficial for resting state and task fMRI studies, it has not be thoroughly evaluated yet. We hypothesize that the kernel size choice will produce non-negligible effect on MB-accelerated EPI image reconstruction, altering the result of rs-fMRI and task-fMRI. In this study, we evaluate rs-fMRI and fMRI analysis, and investigate the kernel size sensitivity of MB reconstruction in the simulation. Finally, we demonstrate rs-fMRI and fMRI analysis using MB EPI scans.

2679.   Multiplexed EPI at 9.4T with PSF-Based Distortion Correction
Seong Dae Yun1 and Nadim Jon Shah1,2
1Institute of Neuroscience and Medicine - 4, Forschungszentrum Jülich, Jülich, Germany, 2JARA - Faculty of Medicine, RWTH Aachen University, Aachen, Germany

The relatively high imaging speed of EPI has led to its widespread use in dynamic MRI studies. For even faster acquisition of multiple slices in EPI, M-EPI (Multiplexed EPI) method has been recently presented (Feinberg et al.). However, because of the intrinsically low bandwidth in the phase encode direction, EPI-based methods are highly sensitive to field inhomogeneities, which results in potentially severe geometric distortions. This problem becomes more challenging at ultra-high fields such as 9.4T. The present work verifies i) the use of M-EPI at 9.4T and ii) demonstrates the application of the PSF (Point Spread Function)-based correction method to the M-EPI images to remove the geometric distortions.

2680.   Fully-Refocused Multi-Slice Ultrafast 3D MRI by Spatiotemporal Encoding
Rita Schmidt1 and Lucio Frydman1
1Chemical Physics, Weizmann Institute of Science, Rehovot, Israel

Recent studies based on spatiotemporal encoding (SPEN) principles showed that many of the echo planar imaging (EPI - the leading “ultrafast” experiment) challenges can be alleviated, particularly if implemented in a “full-refocusing” mode. The present work extends this imaging modality by introducing a variety of new 3D multi-slice schemes, employing either inversion or –for lower Specific Absorption Rate– stimulated echo pulses, and are timed to fulfill the demands of full-refocusing. The work confirmed the advantages of these new imaging modalities with in vivo animal and human scans performed at 3T and 7T, demonstrating higher robustness of the SPEN comparing EPI.

2681.   Comprehensive Theoretical and Experimental Analysis of the Parametric Framework and SNR of Super-Resolved Spatiotemporally-Encoded (SPEN) MRI
Noam Ben-Eliezer1, Lucio Frydman2, and Daniel K. Sodickson1
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States, 2Department of Chemical Physics, Weizmann Institute of Science, Rehovot, Israel

Recently a new MR acquisition scheme has been emerging, based on progressive point-by-point refocusing in the object’s spatial, rather than frequency domain, through the use of quadratic phase encoding. This technique, termed Spatiotemporal-Encoding (SPEN), is capable of overcoming sizable B0 and B1 field distortions, performing single-shot chemical-shift imaging (CSI), and produces reliable images under conditions that preclude the use of conventional acquisition schemes. This work presents a comprehensive analysis of SPEN’s parametric framework and signal-to-noise ratio (SNR) as compared to conventional k-space encoding, showing – theoretically, and experimentally – that the SNR of these two encoding techniques is comparable.

2682.   in vivo Single-Scan 3D Spectroscopic Imaging by Spatiotemporal Encoding
Rita Schmidt1 and Lucio Frydman1
1Chemical Physics, Weizmann Institute of Science, Rehovot, Israel

“Single-shot” strategies –foremost among them Echo Planar Spectroscopic Imaging – alleviate the time constrain of the high dimensionality in the spectroscopic imaging. Although a powerful aid, EPSI’s echo trains face limitations related the rapidly oscillating gradients. Among EPSI’s alternatives is a recent proposal using spatiotemporal encoding (SPEN) principles. The acquired data in such experiment carries the spatial information, and its phase modulation that can convey the chemical shift offsets. The present study recover such additional chemical shift dimension using extended super resolution method that deals with the previous limitations and proves it in a in-vivo 7T animal and 3T human imaging.

2683.   Prospective Compressed Sensing Accelerated Spectroscopic Imaging for Use in Geometrically Accurate in vivo Imaging
Joost van Gorp1, Job G. Bouwman1, Chris J.G. Bakker1, and Peter R. Seevinck2
1Image Sciences Institute, UMC Utrecht, Utrecht, Utrecht, Netherlands, 2UMC Utrecht, Utrecht, Utrecht, Netherlands

Spectroscopic imaging can be used as a means to acquire geometrically accurate images and characterize signal decay curves in the presence of off-resonance effects, without the need for additional post-processing corrections. To decrease the acquisition time for this inherently slow technique and increase its use for in vivo research, compressed sensing was used to prospectively accelerate the sequence up to a factor 5. Retrospective error evaluation of the reconstruction showed that complex averaging of temporal information, which is available at no additional cost in this sequence, decreased the reconstruction error.

2684.   A Novel Golden-Angle Radial FLASH Motion-Estimation Sequence for Simultaneous Thoracic PET-MR
Chuan Huang1, Joyita Dutta1, Yoann Petibon1, Timothy G. Reese2, Quanzheng Li1, Ciprian Catana2, and Georges El Fakhri1
1Center for Advanced Radiological Sciences, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, 2AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States

Accurate lesion characterization is crucial for initial staging, follow up and assessment of response to treatment in non-small cell lung cancer. Conventional PET-CT suffers from a temporal mismatch between PET and CT, due to longer PET than CT acquisitions needed for achieving good PET SNR. This mismatch yields artifacts that affect PET diagnostic specificity as well as quantitation accuracy. Gated CT can be used to correct these motion artifacts; however, however, this is not usually done routinely due to longer exams and greater radiation dose. In this work, we present a slice-interleaved golden-angle radial FLASH sequence with short-duration slice-projection navigation for lung respiratory motion measurement and respiratory motion tracking for motion-corrected PET reconstruction using simultaneous PET-MR.

2685.   Preliminary Study of Resuability of Optimized Trajectory for SENSE
Dan Zhu1, Zhengwei Zhou2, Feng Huang3, and Kui Ying4
1Department of Biomedical Engineering, Tsinghua, Beijign, China, 2Department of Biomedical Engineering, Tsinghua, Beijing, China, 3Philips Healthcare, Beijing, China, 4Department of Engineering Physics, Tsinghua, Beijing, China

The speed of imaging remains one of the basic challenges in Magnetic Resonance Imaging. A large amount of work on fast imaging was focused on studying how to reconstruct an image from a fixed undersampling design. Much less work was done on how to choose the sampling design at the first place. Since trajectory optimization is usually computationally expensive, in this work, we focus on studying the reusability of optimized trajectory. This topic was studied from the following 4aspects: whether the optimized trajectory can be shared among images of different contrasts, different subjects, different slices and different coils.

2686.   Interpolated Parallel Imaging Compressed Sensing
Yong Pang1 and Xiaoliang Zhang1,2
1Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 2UC Berkeley/UCSF Joint Graduate Group in Bioengineering, Berkeley & San Francisco, CA, United States

In this project, we combined the parallel imaging with the interpolated compressed sensing (iCS) method to further accelerate the imaging speed for multi-slice 2-dimensional parallel MR imaging. The raw data of each slice from each channel is multiplied by a weighting function and then used to estimate the missed k-space data of the neighboring slice from the same array channel, which helps improve the image quality of the neighboring slice. In-vivo MR of human has been used to investigate the feasibility of the proposed method, showing obviously increased SNR and CNR.


Wednesday, 24 April 2013 (10:00-12:00) Exhibition Hall
Advances in Image Analysis

2687.   Cuvature-Based Biomarkers for Dyslexia: T1-Image Based Surface Analysis Shows Statistically Separable Dyslexic Features
Rudolph Pienaar1,2, Kiho Im3,4, Mathieu Dehaes3,4, Sara Smith5, Barbara Peysakhovic5, Bryce Becker5, Nora M. Raschle5, Patricia Ellen Grant2,6, and Nadine Gaab4,5
1Radiology, Children's Hospital Boston, Boston, MA, United States, 2Radiology, Harvard Medical School, Boston, MA, United States, 3Newborn Medicine, Children's Hospital Boston, Boston, MA, United States, 4Pediatrics, Harvard Medical School, Boston, MA, United States, 5Developmental Medicine, Children's Hospital Boston, Boston, MA, United States,6Radiology, Boston Children's Hospital, Boston, MA, United States

We present a curvature-histogram analysis as robust biomarker for characterizing development dyslexia from typically developing subjects.

2688.   Detection of Mild Traumatic Brain Injury Utilizing Multifeature Analysis of MRI
Yongxia Zhou1, Yao Wang2, Damon Kenul3, Yuanyi Xue2, Yulin Ge3, Joseph Reaume3, Robert I. Grossman4, and Yvonne W. Lui3
1Radiology/Center for Biomedical Imaging, New York University Langone Medical Center, New York, NY, United States, 2Electrical & Computer Engineering, Polytechnic Institute of New York University, Brooklyn, NY, United States, 3Radiology/Center for Biomedical Engineering, New York University Langone Medical Center, New York, NY, United States,4Radiology/Center for Biomedical Engineering, New York University, New York, NY, United States

The purpose of this study is to design and develop computational techniques to identify mild traumatic brain injury (MTBI) patients that can be used to help predict patient long-term outcome ultimately using multi-dimensional feature space based on several advanced quantitative MR measures. Fourteen imaging features (e.g. kurtosis, magnetic field correlations, thalamic network connectivity and regional volumetry), and nineteen clinical features were tested with different feature selection and classifier algorithms. Our study demonstrates that an automatic classification based on objective physical and imaging measures can achieve a high accuracy of nearly 100% and a robust prediction for the long-term outcome (P¡Ü0.01).

2689.   Surface and Voxel-Based Analysis of Multi-Modal Quantitative MRI for Pre-Surgical Evaluation of Epilepsy Patients
Ali R. Khan1, Maged Goubran1, Sandrine de Ribaupierre2, and Terry Peters1
1Robarts Research Institute, London, ON, Canada, 2Clinical Neurological Sciences, Western University, London, ON, Canada

Pre-surgical localization of the epileptogenic zone is challenging with conventional techniques and often diagnostic scans are found to be negative. Quantitative MRI techniques such as relaxometry and diffusion tensor imaging can potentially detect subtle abnormalities through comparison against a healthy control population atlas. Two approaches for this technique, surface-based and voxel-based, are evaluated in performing patient-specific analyses using quantitative T1, T2 relaxometry, and DTI metrics (FA and MD). We show that both methods produce comparable results and can reveal abnormalities in patients with negative MRI findings.

2690.   Support Vector Machines Detect Huntington's Gene Effects in Mouse Brain Images with >98% Accuracy
Stephen J. Sawiak1,2, A Jennifer Morton3, and T. Adrian Carpenter1
1Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, England, United Kingdom, 2Behavioural and Cognitive Neuroscience Institute, University of Cambridge, Cambridge, England, United Kingdom, 3Department of Pharmacology, University of Cambridge, Cambridge, England, United Kingdom

Support vector machines are used to detect whether high-resolution brain images are from healthy or transgenic Huntington's disease mice. We found that with leave-one-out cross validation the classifier has >98% accuracy at detecting the sick animals and we applied the same trained classifier to older healthy brains, revealing that the changes seen are not confused with healthy aging.

2691.   Software Tools for Anatomical ROI-Based Connectivity Analysis
David W. Shattuck1, Anand A. Joshi2, Justin P. Haldar2, Chitresh Bhushan2, Soyoung Choi3, Andrew C. Krause1, Jessica L. Wisnowski4,5, Arthur W. Toga1, and Richard M. Leahy2
1Laboratory of Neuro Imaging, University of California, Los Angeles, Los Angeles, CA, United States, 2Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, United States, 3Dana and David Dornsife Cognitive Neuroscience Imaging Institute, University of Southern California, Los Angeles, CA, United States, 4Brain and Creativity Institute, University of Southern California, Los Angeles, CA, United States, 5Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, United States

We describe a collection of software tools for jointly processing and visualizing structural and diffusion MRI of the brain. T1-weighted brain MRI are processed to extract models of the cortical surface. A brain atlas labeled with anatomical ROIs is registered to the subject data using a combined surface/volume registration procedure. Diffusion weighted images are processed to produce fiber tract models. The structural and diffusion results are combined to generate a brain connectivity map based on the set of anatomical ROIs. These tools can be applied using scripts or through a user interface that provides sophisticated interactive processing and visualization capabilities.

2692.   Separation of Signal and Noise in Dynamic MRI Data Using the Kolmogorov-Smirnov Test
David S. Smith1, Stephanie Barnes1, and Thomas E. Yankeelov1
1Vanderbilt University, Nashville, TN, United States

We present preliminary efforts that indicate that the Kolmogorov-Smirnov statistical test may be an extremely useful method for automatically separating signal from noise in dynamic imaging data, especially when aliased power should be captured but noise should be ignored. We compare to Otsu's method and demonstrate an improved automatic classification of signal and noise in in vivo tumor-bearing mouse data.

2693.   Are Two Samples of Parametric Maps Statistically Different? Indexed Distribution Analysis (IDA) Can Provide Better Inferences Than Conventional and Histogram Analysis Methods
Chris J. Rose1, James P. O'Connor1,2, Tim F. Cootes1, Chris J. Taylor1, Gordon C. Jayson3, Geoff J. M. Parker1, and John C. Waterton1,4
1Center for Imaging Sciences, Manchester Academic Health Science Center, The University of Manchester, Manchester, Greater Manchester, United Kingdom, 2Department of Radiology, The Christie, Manchester, Greater Manchester, United Kingdom, 3Department of Medical Oncology, The Christie, Manchester, Greater Manchester, United Kingdom,4AstraZeneca, Alderley Park, Cheshire, United Kingdom

MRI can spatially map biophysical and physiological parameters across organs and tumors, and is often used in natural history studies and preclinical and clinical trials of novel drugs. In such research, there is a need to draw statistical inferences about the population, based on a sample. It is common to perform hypothesis tests by taking averages over each structure, but spatially heterogeneous differences can attenuate statistical power. We compare the recently-proposed indexed distribution analysis (IDA) to the conventional and histogram analysis approaches using well-controlled simulated and clinical data. IDA has several advantages over conventional and histogram analysis methods.

2694.   Nonlinear Normalization of Magnetization Transfer Ratio Images for Multi-Centre Clinical Trials
Robert Allan Brown1, Sridar Narayanan1, and Douglas L. Arnold1
1Montreal Neurological Institute, McGill University, Montreal, QC, Canada

Magnetization transfer ratio (MTR) is a magnetic resonance imaging technique that can be used to measure changes in myelin. However, different MTR sequences produce data on different scales, making multi-site or long duration longitudinal studies difficult. We propose a non-linear normalization method to map scanner-specific MTR values to a standard scale. We compare this method with an existing linear normalization method and uncorrected data.

2695.   Non-Uniformity Normalization Using 3D Canny Edges and Legendre Polynomial Approximation of the Bias Field: Validation on 7T T1W Brain Images
Artem V. Mikheev1, Henry Rusinek1, and Graham Wiggins1
1Radiology, NYU Langone Medical Center, New York, NY, United States

MR signal intensity, especially at high field strength, is affected by inhomogeneity, or shading artifacts, manifested as a smooth spatially varying signal intensity distortions. Correction of this effect is the key to successful implementation of all MR image analyses, including segmentation, registration and functional modeling of dynamic data. We have developed a new method BiCal (Bias Calculation) for non-uniformity correction that uses 3D Canny Edge detection and 3D Legendre polynomials to represent the bias field.

2696.   MRI TGV Based Super-Resolution
Adrian Martin1,2, Antonio Marquina3, Juan Antonio Hernandez-Tamames1,2, Pablo Garcia-Polo1,2, and Emanuele Schiavi2,4
1Electronics, Rey Juan Carlos University, Mostoles, Madrid, Spain, 2Alzheimer's Project, Queen Sofia Foundation - CIBERNED, Madrid, Madrid, Spain, 3Applied Mathematics, Valencia University, Burjassot, Comunidad Valenciana, Spain, 4Applied Mathematics, Rey Juan Carlos University, Mostoles, Madrid, Spain

In some MRI applications, in particular when co-registration between modalities is needed, such as fMRI and 3DT1-IR, the acquired image needs to be upsampled to a higher resolution so common interpolation methods have been typically applied to increase this new apparent spatial resolution. Here we propose a new Super Resolution (SR) technique which outperforms these interpolation methods. It is based in a variational SR model proposed and validated in MRI by Joshi et al. in which we introduced the concept of the Total Generalized Variation. Using this operator the solutions obtained by the proposed method present a better image quality. A comparison between methods is presented with phantom and real brain MR images.

2697.   Z Spectral Analysis for the Quantification of Multiple Slow-Exchanging Metabolites
Kejia Cai1, Anup Singh1, Mohammad Haris1, Ravi Prakash Reddy Nanga1, Ranjit Ittyerah1, Damodar Reddy1, Harish Poptani1, Hari Hariharan1, and Ravinder Reddy1
1University of Pennsylvania, Philadelphia, PA, United States

This conventional quantification method may be confounded by the intrinsic magnetic transfer ratio (MTR) asymmetry as well as the Nuclear Overhauser Effect (NOE) effect. To decouple these confounding effects, we demonstrate a comprehensive Lorentzian fitting of Z spectra in brain tumor for the quantification of multiple slow-exchanging metabolites contributing to Z spectrum acquired with low RF saturating amplitude. Results show an increased amide proton transfer (APT) integral and a decreased CEST integral at 2ppm in tumor compared to normal brain tissue. The novel Z spectral analysis method may be used for differentiating tumor metabolic profile from normal tissue.

2698.   Quantification of Inhomogeneous Iron Oxide Uptake in a Model of AIA in Rat.
Lindsey Alexandra Crowe1, Azza Gramoun1, Wolfgang Wirth2, Frank Tobalem3, Kerstin Grosdemange4, Jatuporn Salaklang5, Anthony Redgem5, Alke Petri-Fink6, Felix Eckstein2, Heinrich Hofmann7, and Jean-Paul Vallée1
1Radiology / Faculty of Medicine, Geneva University Hospital, Geneva, Switzerland, 2Institute of Anatomy and Musculoskeletal Research, Paracelsus Medical University, Salzburg, Austria, 3Radiology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland, 4Faculty of Medicine, University of Geneva, Geneva, Switzerland, 5Adolphe Merkle Institute, Université de Fribourg, Fribourg, Switzerland, 6Adolphe Merkle Institute and Chemistry Departement, Université de Fribourg, Fribourg, Switzerland, 7Institute of Materials, Powder Technology Laboratory, EPFL, Lausanne, Switzerland

We present quantification of inhomogeneous SPION uptake over a 3D volume using dUTE and customized software in a rat AIA model at 3T. The use of SPION as a contrast agent is leading development of image acquisition and analysis techniques to quantify uptake and persistence. As signal from iron oxide uptake in vivo can be inhomogeneous, quantification of both the volume and signal intensity are needed for the true extent of SPION uptake. Positive contrast dUTE imaging is used due to its concentration dependence of signal intensity for iron oxide containing regions along with validation of a semi-automated segmentation technique.

2699.   Comparison of Locus Coeruleus Volume Between Gradient Echo and Turbo Spin Echo Sequences Using a Landmark-Based Segmentation Scheme
Jason Langley1, Daniel Huddleston2, Sinyeob Ahn1, Xiangchuan Chen1, Christopher Barnum3, and Xiaoping P. Hu1
1Biomedical Engineering, Emory University & Georgia Institute of Technology, Atlanta, GA, United States, 2Neurology, Emory University, Atlanta, GA, United States, 3Physiology, Emory University, Atlanta, GA, United States

In this abstract, we present a novel landmark-based method for segmentation of the locus coeruleus. The method is then tested on datasets from non-pathologic subjects.

2700.   Automatic Skeletal Muscle Segmentation Through Random Walks with Shape Prior Information
Pierre-Yves Baudin1,2, Noura Azzabou3,4, Pierre G. Carlier3,4, and Nikos Paragios1,5
1Center for Visual Computing, Ecole Centrale Paris, Châtenay-Malabry, IDF, France, 2SIEMENS Healthcare, Saint-Denis, IDF, France, 3Institute of Myology, Paris, IDF, France,4I2BM, MIRCen, IdM NMR Laboratory, CEA, Orsay, IDF, France, 5Equipe Galen, INRIA Saclay, Palaiseau, IDF, France

Developing an automatized tool for segmenting the different skeletal muscles in MRI with minimum user intervention is of paramount importance to facilitate muscle studies. Segmentation of skeletal muscles in 3D MRI poses some specific issues: non-discriminative appearance of the muscles, partial contours between them, large inter-subject variations, spurious contours due to fat infiltrations. We propose an automatic segmentation method based on the Random Walks algorithm to which we add a prior shape model based on learning from an annotated data set.

2701.   Automated Technique for the Segmentation of Deep and Superficial Subcutaneous Adipose Tissues: Association with Insulin Sensitivity in Normal and Overweight Chinese Men
Suresh Anand Sadananthan1,2, Bhanu Prakash K.N.3, Melvin K-S Leow1,4, ChinMeng Khoo5, Kavita Venkataraman2, Eric Khoo Yin Hao5, Lee Yung Seng1,6, Peter Gluckman1, Tai E Shyong1,5, and Sendhil S. Velan1,7
1Singapore Institute for Clinical Sciences, A*STAR, Singapore, 2Department of Obstetrics & Gynaecology, National University of Singapore, Singapore, 3Singapore Bioimaging Consortium, A*STAR, Singapore, 4Department of Endocrinology, Tan Tock Seng Hospital, Singapore, 5Department of Medicine, National University of Singapore, Singapore,6Department of Pediatrics, National University of Singapore, Singapore, 7Clinical Imaging Research Centre, A*STAR-NUS, Singapore

Obesity is associated with increased insulin resistance, a risk factor for type 2 diabetes or cardiovascular disease. Accumulation of fat in different depots may have different effects on insulin resistance. Visceral adipose tissue (VAT) is thought to have greater impact on insulin resistance than subcutaneous fat. More recently, it has been observed that subcutaneous adipose tissue (SAT) has two sub-compartments, deep SAT (DSAT) and superficial SAT (SSAT)), which may have different effects on insulin resistance. While there are many automated methods to accurately segment SAT and VAT, there is currently no technique to separate DSAT and SSAT. We have implemented a fully automated approach to segment DSAT and SSAT and evaluated it on normal and overweight Chinese adults.

2702.   Optimizing Correction of Geometric Distortion in MR Images
Radu Mutihac1,2, Allen Braun3, and Thomas J. Balkin2
1Department of Physics, University of Bucharest, Bucharest, Bucharest-Magurele, Romania, 2Psychiatry & Neuroscience, Department of Behavioral Biology, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States, 3Language Section, NIDCD / National Institutes of Health, Bethesda, Maryland, United States

Sleep data acquisition was carried out using uncommon low bandwidth in the phase encoding direction and low gradient amplitude for functional EPI readout resulting in low scanner noise, but substantially increasing the static magnetic field inhomogeneity. EPI images are prone to substantial signal dropout and spatial distortion in regions where the field is inhomogeneous. Two major sources of artifacts affect EPI reconstructions: Nyquist ghosts and geometric distortion. Post-processing optimization of geometric distortion correction in EPI FSE images based on paired field maps (magnitude and phase at different echo times)acquired at 3T in terms of spin-echo time difference is presented hereafter.

2703.   A Novel Non-Rigid Registration Approach for Accurate Quantification of Dynamic Contrast Enhanced MR Imaging (DCE-MRI) in Ovary Employing Residual Complexity Framework
Anahita Fathi Kazerooni1,2, Leila Torbati3, Mahrooz Malek3, and Hamidreza Saligheh Rad1,2
1Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Tehran University of Medical Sciences, Tehran, Iran, 2Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran, 3Imaging Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran

Typically, quantification of DCE-MRI of ovary is susceptible to errors caused by motion artifacts and intensity inhomogeneity induced by bias fields. Motion artifacts and bias fields introduce signal intensity variations in the images that must be resolved from intensity changes caused by the passage of contrast agent. Thus, registration of DCE-MRI image sequence is a challenging issue. In this work, we proposed a solution to the misregistration problem of DCE-MR images, by exploiting residual complexity (RC) similarity measure, to account for complex intensity variations in a non-rigid registration approach and for precise quantification of DCE-MRI to characterize ovarian masses.

2704.   Hepatic Perfusion Modeling Using DCE-MRI with Sequential Breath Holds
Eric M. Bultman1, Ethan K. Brodsky2, Debra E. Horng3, Pablo Irarrázabal4, William R. Schelman5, Walter F. Block1,3, and Scott B. Reeder1,6
1Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, 2Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, 3Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 4Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, 5Medicine, University of Wisconsin-Madison, Madison, WI, United States, 6Radiology, University of Wisconsin-Madison, Madison, WI, United States

Estimating quantitative hepatic perfusion parameters is challenging for several reasons, including the need to acquire data during periods of free breathing. In this work, we demonstrate the feasibility of estimating hepatic perfusion parameters using interrupted DCE-MRI data acquired with a 3D time-resolved radial imaging sequence during sequential breath-holds. Average time-signal curves corresponding to cirrhotic liver and hepatocellular carcinoma ROIs are fitted to a dual-input single-compartment model, and quantitative perfusion parameters are generated. Perfusion characteristics of HCC are shown to be distinctly different from background cirrhotic liver.

2705.   Motion Correction in Small Bowel DCE-MRI Using Robust Data Decomposition Registration
Valentin Hamy1, Shonit Punwani1, Jesica Makanyanga1, Stuart Taylor1, and David Atkinson1
1Centre for Medical Imaging, University College London, London, United Kingdom

Dynamic Contrast Enhanced MRI is of interest for the detection of small bowel disorders including ulcerative lesions in Crohn’s disease. However misalignments arise due to patient motion during the acquisition. This is likely to alter the time intensity curve shape of a given region of interest and can affect data analysis. Butylscopolamine injection prior to acquisition can limit the effect of bowel peristalsis but correcting for breathing motion in the presence of contrast changes remains challenging. In this study we investigate the application of a registration approach, robust to contrast changes, to obtain accurate realignment of clinically relevant features in small bowel DCE-MRI

2706.   rBET: Making BET Work for Rodent Brains
Tobias C. Wood1, David J. Lythgoe1, and Steven C.R. Williams1
1Neuroimaging, King's College London, Institute of Psychiatry, London, United Kingdom

We have modified the Brain Extraction Tool (BET) so that it can successfully produce brain masks for rodent images. Previously the algorithm failed due to the different size and shape of rodent brains compared to humans. We have selected a more appropriate initial brain shape and search constants and demonstrate results with rat data.