ISMRM 23rd Annual Meeting & Exhibition • 30 May - 05 June 2015 • Toronto, Ontario, Canada

Electronic Poster Session • Pulse Sequences & Reconstruction
3380 -3403 MR Fingerprinting & Quantitative Imaging
3404 -3427 Reconstruction & Processing Algorithms

Note: The videos below are only the slides from each presentation. They do not have audio.

Monday 1 June 2015
Exhibition Hall 17:30 - 18:30

  Computer #  
49 Nonlinear Dimensionality Reduction for Magnetic Resonance Fingerprinting with Application to Partial Volume
Debra McGivney1, Anagha Deshmane2, Yun Jiang2, Dan Ma2, and Mark Griswold1,2
1Radiology, Case Western Reserve University, Cleveland, Ohio, United States, 2Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States

Magnetic resonance fingerprinting (MRF) is a technique that can provide quantitative maps of tissue parameters such as T1 and T2 relaxation times through matching observed signals to a precomputed complex-valued dictionary of modeled signal evolutions. Since each dictionary entry is uniquely defined by two real parameters, specifically T1 and T2, we propose to compress the dictionary onto a real-valued manifold of three dimensions using the nonlinear dimensionality reduction technique of kernel principal component analysis. Once the compression is achieved, we explore new computational applications for MRF, namely solving the partial volume problem.

50 A Bayesian Approach to the Partial Volume Problem in Magnetic Resonance Fingerprinting
Debra McGivney1, Anagha Deshmane2, Yun Jiang2, Dan Ma2, and Mark Griswold1,2
1Radiology, Case Western Reserve University, Cleveland, Ohio, United States, 2Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States

Magnetic Resonance Fingerprinting (MRF) can produce quantitative maps of tissue parameters such as T1 and T2 relaxation times by matching acquired signals to a predefined dictionary of signal evolutions. One inherent issue is that all voxels are assigned only one dictionary entry, even if they exhibit the partial volume effect. We apply a Bayesian statistical framework to solve the general partial volume problem for MRF without assigning in advance the specific dictionary entries that comprise a signal from one of these mixed voxels, rather, assumptions are made on the probability distributions of the mixed signals and their component signals.

3382.   51 MR fingerprinting based on realistic vasculature in mice: identifiability of physiological parameters
Philippe Pouliot1,2, Louis Gagnon3, Tina Lam4, Pramod Avti5, Michèle Desjardins1, Ashok Kakkar4, Sava Sakadzic3, David Boas3, and Frédéric Lesage1
1Electrical Engineering, Ecole Polytechnique Montreal, Montreal, QC, Canada, 2Research Centre, Montreal Heart Institute, Montreal, QC, Canada, 3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, MA, United States, 4Chemistry Department, McGill University, QC, Canada, 5Montreal Heart Institute, QC, Canada

MR vascular fingerprinting is a novel approach to estimate cerebral blood volume, vessel radius and oxygenation. To our knowledge, this approach has not yet been fully validated. Here we implemented the sequence in mice and exploited a dictionary built on simulations of the MR signal based on realistic vasculature built on 2-photon angiograms. A dictionary for fingerprint extraction was generated by sampling along 5 parameters: hemoglobin saturation, vessel radius, capillary density, SPION concentration and magnetic field inhomogeneity. Following linearization, the dictionary eigensystem was characterized. This confirmed that all its eigenvalues are positive and distinct, and therefore all parameters studied are theoretically identifiable.

3383.   52 Uncertainty Volume Analysis - A Measure for Protocol Performance
Cristoffer Cordes1 and Matthias Günther1,2
1Fraunhofer MEVIS, Bremen, Germany, 2MR-Imaging and Spectroscopy, University of Bremen, Bremen, Germany

In order to extract the information density of images acquired with a given protocol, data was parameter mapped (T1, T2, M0) using an objective function based on a simulated signal model, minimized with a variation of the simulated annealing algorithm. Calculating the uncertainty volumes based on an uncertainty condition of the objective function reveals a contrast that is able to rank the performance of the utilized sequences by eliminating the sequence of least preferable impact in a greedy fashion. It also reveals the voxel-wise shape of the remaining flaws. The algorithm was tested on a series of TSE acquisitions.

3384.   53 Tier-specific weighted echo sharing technique (WEST) for extremely undersampled Cartesian magnetic resonance fingerprinting (MRF)
Taejoon Eo1, Jinseong Jang2, Minoh Kim2, Dong-hyun Kim2, and Dosik Hwang2
1Yonsei University, Seoul, Seoul, Korea, 2Yonsei University, Seoul, Korea

Proposed tier-specific WEST method could sufficiently suppress the noise-like artifacts in the maps obtained by the conventional WEST. Consequently, this method enables acquisition of accurate maps from extremely undersampled Cartesian MRF data.

3385.   54 3D Balanced-EPI Magnetic Resonance Fingerprinting at 6.5 mT
Mathieu Sarracanie1,2, Ouri Cohen1, and Matthew S Rosen1,2
1MGH/A.A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Department of Physics, Harvard University, Cambridge, MA, United States

2D MR Fingerprinting has recently been shown at low magnetic field. Here, we demonstrate MRF in 3D at 6.5 mT, using an optimized set of 15 flip angles and repetition times (FA/TR), in a Cartesian acquisition of k-space with a new hybrid b-SSFP-EPI sequence. We measure quantitative parameters in 3D, and generate several image contrasts in a single acquisition (proton density, T1, T2) in less than 30 minutes. The combination of 3D MRF with low field MRI scanners has great potential to provide clinically relevant contrast with portable low cost MR scanners.

55 Pulse Sequence Optimization for Improved MRF Scan Efficiency
Jesse Ian Hamilton1, Katherine L Wright1, Yun Jiang1, Luis Hernandez-Garcia2, Dan Ma1, Mark Griswold1,3, and Nicole Seiberlich1,3
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 3Radiology, Case Western Reserve University, Cleveland, OH, United States

A flexible framework for MR Fingerprinting pulse sequence design is presented that includes the MRI signal encoding, gridding, and pattern recognition directly in the optimization. The method was validated in a phantom study by designing sequence for mapping T1, T2, and M0 in under 3s using a highly undersampled spiral trajectory. Parameter maps obtained with the optimized sequence have fewer artifacts and higher agreement with spin echo measurements compared to unoptimized sequences. The optimization framework is easily generalizable to other MRF applications.

56 Multiple Preparation Magnetic Resonance Fingerprinting (MP-MRF): An Extended MRF Method for Multi-Parametric Quantification
Christian Anderson1, Ying Gao1, Chris Flask1,2, and Lan Lu2,3
1Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States, 2Radiology, Case Western Reserve University, Cleveland, Ohio, United States, 3Urology, Case Western Reserve University, Cleveland, Ohio, United States

Magnetic resonance fingerprinting (MRF) offers rapid simultaneous multi-parametric quantification, and also provides the potential to generate maps of other parameters. We have developed a novel scheme named "Multi-Preparation MRF" (MP-MRF) that implements adaptable magnetization preparations periodically during the dynamic MRF acquisition. Our initial simulations of the MP-MRF methodology show sensitivity to diffusion and perfusion contrast and reasonable estimates of T1, T2, and velocity in Shepp-Logan phantoms.

3388.   57 Quantitative evaluation of the effect of reduction of signal acquisition number in MR fingerprinting
Te-Ming Lin1, Su-Chin Chiu1, Cheng-Chieh Cheng1, Wen-Chau Wu1,2, and Hsiao-Wen Chung1
1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 2Graduate Institute of Oncology, National Taiwan University, Taipei, Taiwan

The signal acquisition number is related to the computational complexity during signal analysis in MR fingerprinting. In this study, we develop a contour area index and demonstrate a quantitative method to evaluate the mapping precision under different signal acquisition numbers. It has potential in evaluating different RF excitation schemes in MR fingerprinting.

3389.   58 Kd-tree for Dictionary Matching in Magnetic Resonance Fingerprinting
Nicolas Pannetier1,2 and Norbert Schuff1,2
1Radiology, UCSF, San Francisco, California, United States, 2VAMC, San Francisco, CA, United States

We evaluate the use of kd-tree (a space partitioning data structure) to speed-up the matching process in magnetic resonance fingerprinting. We found that, in combination with PCA reduction, the matching time can be reduced by 2 to 3 order of magnitude while preserving the accuracy. The matching time, however, increases with noise level and the PCA threshold remains a key element to tune to achieve the best performance.

3390.   59 Three-Dimensional MR Fingerprinting (MRF) and MRF-Music Acquisitions
Dan Ma1, Eric Y Pierre1, Yun Jiang1, Kawin Setsompop2, Vikas Gulani3, and Mark A Griswold3
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2A.A Martinos Center for Biomedical Engineering, MGH, Harvard Medical School, Boston, MA, United States, 3Radiology, Case Western Reserve University, Cleveland, OH, United States

The purpose of this study is to extend the 2D MR Fingerprinting (MRF) and MRF-Music framework to 3D acquisitions. Both methods were originally implemented in 2D acquisitions and have shown high scan efficiency for quantifying multiple tissue properties simultaneously. In addition to the multi-parameter quantification in MRF, the MRF-Music sequence was proposed to provide musical sounds that can dramatically improve the patients’ experience in the MR scanner. In this study, the MRF and MRF-Music sequences were implemented to achieve 3D coverage while still maintaining a high scan efficiency and providing desirable sounds. T1 and T2 values from phantom studies of the 3D slab selective MRF and MRF-Music methods showed good agreement to the values from the standard measurements. The T1, T2, off-resonance and M0 maps from 3D non-selective MRF and MRF-Music also showed promising results of achieving 3D isotropic quantitative mapping.

3391.   60 PET-MRF: One-step 6-minute multi-parametric PET-MR imaging using MR fingerprinting and multi-modality joint image reconstruction
Florian Knoll1,2, Martijn A Cloos1,2, Thomas Koesters1,2, Michael Zenge3, Ricardo Otazo1,2, and Daniel K Sodickson1,2
1Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, United States, 2Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, United States, 3Siemens Medical Solutions USA, Malvern, PA, United States

Despite the extensive opportunities offered by PET-MR systems, their use is still far from routine clinical practice. While it is feasible to acquire PET data in about 5 minutes, collecting the clinically relevant variety of traditional MR contrasts requires substantially more time. This bottleneck formed by the traditional MR paradigm leads to inefficient use of the PET component. This work proposes a one-step procedure that merges the MR fingerprinting framework with the PET acquisition, and employs a dedicated multi-modality reconstruction to enable a 6 minute comprehensive PET-MR exam, which can provide the majority of clinical MR contrasts alongside quantitative parametric maps of the relaxation parameters (T1,T2) together with improved PET images.

3392.   61 Comparison of accuracy and reproducibility of MR Fingerprinting with conventional T1 and T2 mapping
Bernhard Strasser1, Wolfgang Bogner1, Peter Bär1, Gilbert Hangel1, Elisabeth Springer1, Vlado Mlynarik1, Mark A Griswold2,3, Dan Ma2, Yun Jiang2, Mathias Nittka4, Haris Saybasili4, and Siegfried Trattnig1
1MRCE, Department of Biomedical Imaging and Image-guided Therapy, University of Vienna, Vienna, Vienna, Austria, 2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States, 3Radiology, Case Western Reserve University, Cleveland, Ohio, United States, 4Siemens Healthcare USA, Inc., Chicago, Illinois, United States

Previously, MR Fingerprinting (MRF) has been presented as a new method for simultaneous quantitative mapping of different physical MR properties. In this study, the T1 and T2 values of MRF were compared to conventional T1- and T2-mapping methods in the brains of five volunteers at 1.5T. Each volunteer was measured five times with a TrueFISP and a FISP based spiral MRF sequence, an MP2RAGE and a multi echo spin echo sequence for conventional T1 and T2 maps, respectively. Both MRF sequences showed a similar reproducibility but seemed to slightly underestimate the T2-values in comparison to the conventional sequences.

3393.   62 Lower Bound Signal-to-noise Ratios and Sampling Durations for Accurate and Precise T1 and T2 Mapping with Magnetic Resonance Fingerprinting
Zhaohuan Zhang1,2, Zhe Wang2,3, Subashini Srinivasan2,3, Kyunghyun Sung2,3, and Daniel B. Ennis2,3
1Department of Physics & Astronomy, Shanghai Jiao Tong University, Shanghai, China, 2Department of Radiological Sciences, University of California, Los Angles, CA, United States, 3Department of Bioengineering, University of California, Los Angles, CA, United States

The objective of this study was to evaluate the accuracy and precision of pseudorandom inversion recovery balanced steady-state free precession magnetic resonance fingerprinting (MRF) relaxometry (T1 and T2) estimates over a range of SNRs and the number of acquired TRs (NTR) using Bloch equation simulations. Under the condition of perfect sampling, the Bloch simulations defined a lower-bound acquisition requirement of SNR¡Ý5 and NTR¡Ý400 for accurate and precise T1 and T2 estimates when using MRF. This work also concluded that MRF provides nearly equivalent T1 and T2 estimates.

3394.   63 Comparison of Different Approaches of Pattern Matching for MR Fingerprinting - permission withheld
Thomas Amthor1, Mariya Doneva1, Peter Koken1, Jochen Keupp1, and Peter Börnert1
1Philips Research Europe, Hamburg, Germany

We present a comparison of different pattern matching algorithms for tissue characterization based on Magnetic Resonance Fingerprinting. The applicability of a simple dot product approach and a number of machine learning algorithms is investigated for different parameter regimes. We find that, in many cases, machine learning algorithms can offer higher accuracy and faster matching.

3395.   64 Accuracy Analysis for MR Fingerprinting
Mariya Doneva1, Thomas Amthor1, Peter Koken1, Jochen Keupp1, and Peter Börnert1
1Philips Research Europe, Hamburg, Germany

In this work we demonstrate a comprehensive accuracy analysis exemplified on a bSSFP-based MRF sequence, which allows predicting the accuracy of MRF in different parameter ranges and defining confidence areas for the performance of MRF.

65 Undersampled High-frequency Diffusion Signal Recovery Using Model-free Multi-scale Dictionary Learning
Enhao Gong1, Qiyuan Tian1, John M Pauly1, and Jennifer A McNab2
1Electrical Engineering, STANFORD UNIVERSITY, Stanford, California, United States, 2Radiology, STANFORD UNIVERSITY, Stanford, California, United States

Low Signal-to-Noise Ratio (SNR), especially at high b-values, is a critical problem for Diffusion MRI (dMRI). Methods with different signal models may fail to reconstruct under-sampled data from noisy measurement. Diffusion MRI signal contains redundancy as a multi-dimensional signal in both k-space and q-space. Here we proposed a novel approach to recover signal without explicitly enforcing any physical signal model. The method is model-free but learns the multi-dimensional redundancy, including the redundancy between neighborhood voxels, different directions and low\high b-values, from training samples. A Dictionary Learning approach is used to recover under-sampled signals in q-space. Quantitative results demonstrate the method can more accurately predict high b-value signal (>3000s/mm2) from low b-value signal. Also it produces more accurate physiological metrics such as Generalized Fractional Anisotropy (GFA) and Orientation Distribution Function (ODF) that potentially help to resolve intra-voxel crossing fibers.

3397.   66 Limitations of T2-contrast 3D-Fast Spin Echo Sequences in the Differentiation of Radiation Fibrosis versus Tumor Recurrence
Andrea Vargas1, Laurent Milot2, Simon Graham1, and Philip Beatty1
1Medical Biophysics, University of Toronto, Toronto, Ontario, Canada, 2Sunnybrook Research Institute, Toronto, Canada

The use of variable flip angles for 3D fast spin echo sequences (3DFSE) have shown to alter contrast in T2-weighted images relative to conventional 2DFSE. While these alterations of contrast may be minimal in brain tissues, they can have a great consequences in body applications that encompass a wide range of T2 values. In this study we evaluate the performance of current methods that aim to correct T2-contrast in a cervix cancer application which has a wide range of T2 values (35 ms < T2 < 84 ms). We show that the differentiation between recurrent tumor and radiation fibrosis may be ambiguous at clinical echo times using 3DFSE.

3398.   67 Optimization of Magnetization-Prepared Rapid Gradient-Echo (MP-RAGE) Sequence for Neonatal Brain MRI
Lili He1, Jinghua Wang2, Mark Smith3, and Nehal A. Parikh1,4
1Center for Perinatal Research, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio, United States, 2Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, United States, 3Radiology Department, Nationwide Children's Hospital, Columbus, Ohio, United States, 4Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States

Three-dimensional T1-weighted sequences such as MP-RAGE are extremely valuable to evaluate neonatal and infant brain injury/development. Yet, the lack of complete myelination and smaller head size results in comparatively lower quality images as compared to adult brains. In this study, we consider WM-GM contrast efficiency as an objective function to optimize neonatal MP-RAGE parameters under optimal k-space sampling by means of computer simulation. Quantitative analysis indicated that WM-GM contrast to noise efficiency of images acquired with our optimal parameters was 20% higher than those using parameters recommended by a published protocol; similarly, mean SNR efficiency was increased by approximately 150%.

3399.   68 T2 Shuffling: Multicontrast 3D Fast Spin Echo Imaging
Jonathan I. Tamir1, Weitian Chen2, Peng Lai2, Martin Uecker1, Shreyas S. Vasanawala3, and Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Global Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States, 3Radiology, Stanford University, Stanford, CA, United States

Fast Spin Echo (FSE) is widely used in MR imaging due to its speed and robustness to image artifacts. However, blurring due to T2 decay inhibits its use for 3D musculoskeletal imaging. By compensating for signal decay and reconstructing a time series of images, the blurring can be reduced. In this work we resample and reorder phase encodes over a longer echo train length to improve scan efficiency. We add a locally low rank constraint to improve the conditioning of the reconstruction, producing multicontrast 3D FSE images at clinically feasible scan times.

3400.   69 High contrast-to-noise ratio brain structural images using magnetization preparation and trueFISP acquisition
Yi-Cheng Hsu1, Ying-Hua Chu1, Shang-Yueh Tsai2, Wen-Jui Kuo3, and Fa-Hsuan Lin1
1Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, 2Institute of Applied Physic, National Chengchi University, Taipei, Taiwan,3Institute of Neuroscience, National Yang Ming University, Taipei, Taiwan

A MP trueFISP sequence for brain structural imaging was implemented and tested. Compared with MP RAGE using the same acquisition time, it improves the contrast from 40% to 80% with 37.8% noise increase due to a wider readout bandwidth.

3401.   70 Rapid whole brain T1 rho mapping
Bing Wu1, Nan Hong2, and Zhenyu Zhou1
1GE healthcare China, Beijing, Beijing, China, 2Peking university people's hospital, Beijing, China

T1 rho acquisition is often constrained to single slice due to the long TSL needed, which makes the cross-examination with other measurements such as resting state fMRI difficult. In this work, we develop a rapid T1-rho mapping method that utilizes single-shot EPI acquisition and multi-band excitation that completes a 2mm isotropic whole brain T1 rho mapping within 5 minutes, which allows this acquisition to be added in a Parkinson disease related clinical study.

3402.   71 Suppression of Artifacts in Simultaneous 3D T1 and T2*-weighted Dual-Echo Imaging
Won-Joon Do1, Seung Hong Choi2, Eung Yeop Kim3, and Sung-Hong Park1
1Korea Advanced Institute of Science and Technology, Daejeon, Korea, 2Department of Radiology, Seoul National University College of Medicine, Seoul, Korea,3Department of Radiology, Gachon University Gil Medical Center, Incheon, Korea

Dual-echo sequence allows us to acquire 3D T1 and T2*-weighted images simultaneously. The conflicting parameter conditions of T1and T2* contrasts can be resolved by echo-specific k-space reordering schemes. However, abrupt changes in scan conditions for the echo-specific k-space reordering can cause ringing artifacts. In this study, we propose a new approach of smooth transition in the regions of abrupt changes, to suppress the artifacts. The ringing artifacts in the echo-specific k-space reordered dual-echo sequence without the smooth transition could be effectively suppressed with the proposed approach and thus the image qualities became closer to those acquired with conventional single-echo sequences.

3403.   72 2D Reduced Field of View Spiral Inversion Recovery Sequence for High Resolution Multiple Inversion Time Imaging in a Single Breath Hold - permission withheld
Galen D Reed1, Reeve Ingle1, Ken O Johnson1, Juan M Santos1, Bob S Hu2, and William R Overall1
1Heartvista, Menlo Park, California, United States, 2Cardiology, Palo Alto Medical Foundation, Menlo Park, California, United States

High resolution inversion recovery imaging of myocardium within small breath hold durations is challenging due to the need for segmented acquisitions and short readout windows. By combining the efficiency of parallel spiral imaging with a 2-dimensional field-of-view reduction, we designed a sequence that acquires 1.7 mm in-plane resolution images in a 7 heartbeat breath hold. The short acquisition window enabled repeating the sequence to obtain a series of images with different inversion times. The efficacy of multiple TI imaging with and without 2D outer volume suppression was demonstrated.

Monday 1 June 2015
Exhibition Hall 17:30 - 18:30

  Computer #  
3404.   73 An Approach to Improve the Effectiveness of Wavelet and Contourlet Compressed Sensing Reconstruction
Paniz Adipour1 and Michael R. Smith1,2
1Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada, 2Radiology, University of Calgary, Calgary, Alberta, Canada

Truncation artifacts appear in DFT reconstructions through discontinuities across the ends of the data set which mathematically is cyclic in k-space. A suggestion indicates that similar position dependent distortions will be present in CS reconstructions which repeatedly use the DFT. A comparison is made between standard Wavelet and Contourlet CS reconstructions and proposed high k-space extrapolation enabled (Hi-KEE) variants of these approaches. The CS-Contourlet outperforms the common CS-Wavelet in providing a better sparse representation of contour-shaped objects and detailed textures. The Hi-KEE-CS-Contourlet is shown to outperform the CS-Contourlet by providing a better position independent resolution solution.

3405.   74 Enhanced reconstruction of compressive sensing MRI via cross-domain stochastically fully-connected random field model
Edward Li1, Mohammad Javad Shafiee1, Audrey Chung1, Farzad Khalvati2, Alexander Wong1, and Masoom A Haider3
1Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada, 2Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada, 3Sunnybrook Health Sciences Center, Toronto, Ontario, Canada

Compressive sensing reduces MRI acquisition times but requires advanced sparse reconstruction algorithm to produce high-quality MR images. We propose a novel sparse reconstruction method using a cross-domain stochastically fully-connected random field (CD-SFCRF) for improved reconstruction from compressive sensing MRI data. Peak-to-peak signal-to-noise ratio (PSNR) analysis of CD-SFCRF and other methods using a prostate training phantom demonstrate that CD-SFCRF has the highest PSNR across all under-sampling ratios of radial MRI acquisitions. A visual comparison using real patient cases illustrate that CD-SFCRF can improve fine tissue detail and contrast preservation while eliminating under-sampling artifacts.

3406.   75 Overcoming the Image Position-Dependent Resolution Inherent in DFT and CS Reconstructions
Michael R. Smith1,2, Jordan Woehr1, Mathew E. MacDonald2,3, and Paniz Adipour1
1Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada, 2Radiology, University of Calgary, Calgary, Alberta, Canada, 3Seaman MR Family Research Centre, University of Calgary, Calgary, Alberta, Canada

Truncated k-space data sets provide higher temporal resolution but compromise spatial resolution during DFT reconstruction. Compressed sensing, using under-sampled data, is used to improve spatial resolution while retaining temporal resolution. Certain Fourier domain properties can produce MRI CS reconstruction with resolutions that are dependent on the position of an object in the final reconstructed image. We demonstrate this position dependent resolution and propose two approaches to overcome it: Fourier Shift (FS) and Area Specific Additional Truncation (ASAT) image resolution enhancement pre-processing techniques.

3407.   76 Simultaneuos Magnitude and Phase Regularization in MR Compressed Sensing using Multi-frame FREBAS Transform
Satoshi Ito1, Mone Shibuya1, Kenji Ito1, and Yoshifumi Yamada1
1Utsunomiya University, Utsunomiya, Tochigi, Japan

It is difficults to apply CS to images with rapid spatial phase variations, since not only the magnitude but also phase regularization is required in the CS framework. An iterative MRI reconstruction with separate magnitude and phase regularization was proposed for applications where magnitude and phase maps are both of interest. Since this method requires the approximation of phase regularizer to cope with phase unwrapping problem, it is roughly 10 times slower than conventional CS and the convergence is not guaranteed. In this article we propose a novel image reconstruction scheme for CS-MRI in which phase regularizer or symmetrical sampling trajectory are not required in the rather standard CS reconstruction scheme, but highly robust to rapid phase changes. The proposed method uses multi-frame complex transforms to introduce sparseness for the complex image data.

3408.   77 Extended Phase Graphs: Understanding a Common Misconception of the Framework which Leads to the Failure of Programming It Correctly
Matthias Weigel1
1Radiological Physics, Dept. of Radiology and Nuclear Medicine, University of Basel Hospital, Basel, Switzerland

The extended phase graph (EPG) concept is a favorite approach for the rapid quantitation of magnetization response. However, users frequently have problems to properly program the framework. One major reason may be that care has to be taken with the complex Fourier domains of the transverse magnetization and their inherent symmetry relations. The present educational abstract depicts these issues and shows how RF pulses and gradients act differently on the magnetization components. Solutions to overcome the described issues are presented and discussed. Additionally, the author provides representative EPG software demonstrating the solutions.

3409.   78 Acquisition strategy for limited support Compressed Sensing
Pavan Poojar1, Bikkemane Jayadev Nutandev1, Amaresha Sridhar Konar1, Rashmi R Rao1, Ramesh Venkatesan2, and Sairam Geethanath1
1Medical Imaging Research Centre, Dayananda Sagar Institutions, Bangalore, Karnataka, India, 2Wipro-GE Healthcare, Bangalore, Karnataka, India

Cardiac MRI scans demands rapid acquisition of images to avoid motion artifacts. Region of interest (ROI) selected will be sparse and leads to arbitrary k-space shape. Active contour in combination with convex optimization leads to new ROI based acquisition strategy which gives arbitrary k-space trajectories and optimized gradients based on the constraints for given ROI. Retrospective studies were carried out on six cardiac datasets for different accelerations (3x, 4x, 5x and 10x) and Normalized Root Mean Square Error was calculated. Future work includes reconstruction of image using ROI Compressed Sensing.

3410.   79 MRI Constrained Reconstruction without Tuning Parameters Using ADMM and Morozov's Discrepency Principle
Weiyi Chen1, Yi Guo1, Ziyue Wu2, and Krishna S. Nayak1,2
1Electrical Engineering, University of Southern California, Los Angeles, CA, United States, 2Biomedical Engineering, University of Southern California, Los Angeles, CA, United States

We propose a method for MRI constrained reconstruction using ADMM framework that is data-driven, and does not require manual selection of tuning parameters. We use Morozov's discrepancy principle as a criterion to iteratively determine the tuning parameter. Tests with T2w brain data show that the reconstruction quality is comparable with reconstructions using manually selected parameter.

3411.   80 A fast algorithm for tight frame-based nonlocal transform in compressed sensing MRI - video not available
Xiaobo Qu1, Yunsong Liu1, Jing Ye1, Di Guo2, Zhifang Zhan1, and Zhong Chen1
1Department of Electronic Science, Xiamen University, Xiamen, Fujian, China, 2School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, Fujian, China

Compressed sensing magnetic resonance imaging (CS-MRI) is to reconstruct MR images from undersampled k-space data by enforcing the sparsity of MR images. Patch-based nonlocal operator (PANO) is proposed as a linear operator to exploit the nonlocal self-similarity of MR images to further sparsify them. However, the original PANO is a frame and its numerical algorithm for CS-MRI problem is solved by the alternating direction minimization with continuation (ADMC). These two aspects lead the reconstruction to be time consuming. In this work, we first convert the PANO into a tight frame, and then applied the alternating direction method of multipliers (ADMM) algorithm to accelerate the image reconstruction. The empirical convergence demostrates that the new approach significantly accelerate the image reconstruction in compressed sensing MRI and can accomplish the reconstruction of one 256256 within several seconds.

3412.   81 A novel non convex sparse recovery method for single image super-resolution, denoising and iterative MR reconstruction
Nishant Zachariah1, Johannes M Flake2, Qiu Wang3, Boris Mailhe3, Justin Romberg1, Xiaoping Hu4, and Mariappan Nadar3
1Department of Electrical and Computer Engineering, Georgia Institute of Technoloy, Atlanta, GA, United States, 2Department of Mathematics, Rutgers University, New Brunswick, NJ, United States, 3Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, United States, 4Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States

Increasing MR image resolution, decreasing MR instrumentation noise and reconstructing high quality MR images from under sampled measurements are open challenges. In this paper we tackle these three problems under a novel non convex framework. We show that our method out performs state of the art techniques (quantitatively and qualitatively) for image super-resolution, denoising and under sampled reconstruction. In addition, we are able to recover regions of clinical interest with greatest fidelity thereby substantially aiding the clinical diagnostic process. Our powerful generic framework lends itself to tackling additional future applications such as image in-painting and blind de-convolution.

3413.   82 Momentum optimization for iterative shrinkage algorithms in parallel MRI with sparsity-promoting regularization
Matthew J. Muckley1, Douglas C. Noll1, and Jeffrey A. Fessler2
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 2Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

MRI scan times can be accelerated by combining parallel MRI with sparse models. These models give rise to optimization problems that are traditionally minimized with variable splitting algorithms that require tuning of penalty parameters. We review a new algorithm, BARISTA, that circumvents penalty parameter tuning while preserving convergence speed. We then propose a new optimized momentum update term for BARISTA that gives a theoretically-predicted factor of 2 increase in convergence speed of the cost function, terming the new algorithm OMBARISTA. Our optimization experiments agreed with the theory predictions, and we propose using OMBARISTA in place of BARISTA in general settings.

3414.   83 Parameter-Free Sparsity Adaptive Compressive Recovery (SCoRe)
Rizwan Ahmad1, Philip Schniter1, and Orlando P. Simonetti2
1Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, United States, 2Internal Medicine and Radiology, The Ohio State University, Columbus, Ohio, United States

Redundant dictionaries are routinely used to exploit rich structure in MR images. When using a redundant dictionary, however, the level of sparsity may vary across different groups of atoms, i.e., across “subdictionaries.” In this work, we propose a method, called Sparsity Adaptive Compressive Recovery (SCoRe), that adapts to the inherent level of sparsity in each subdictionary. Moreover, the proposed adaptation is data-driven and does not introduce any tuning parameters. For validation, results from digital phantom and real-time cine are presented.

3415.   84 Graph-based compressed sensing MRI image reconstruction: View image patch as a vertex on graph
Zongying Lai1,2, Yunsong Liu1, Di Guo3, Jing Ye1, Zhifang Zhan1, Zhong Chen1, and Xiaobo Qu1
1Department of Electronic Science, Xiamen University, Xiamen, Fujian, China, 2Department of Communication Engineering, Xiamen University, Fujian, China,3School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, Fujian, China

Compressed sensing MRI can speed up imaging by undersampling k-space data. However, the sparse representation of magnetic resonance images affects the quality of reconstructed images. In this work, a graph-based compressed sensing MRI image reconstruction method is proposed. This method views an image patch as a vertex on graph and reorders the pixel to be smooth by traveling this graph with shortest path. Image reconstruciong from compressively sampled data shows that the proposed reconstruction method outperforms conventional wavelets in terms of visual quality and evaluation criteria.

85 MR Image Reconstruction with Optimized Gaussian Mixture Model for Structured Sparsity
Zechen Zhou1, Niranjan Balu2, Rui Li1, Jinnan Wang2,3, and Chun Yuan1,2
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, WA, United States, 3Philips Research North America, Briarcliff Manor, NY, United States

Parallel Imaging (PI) and Compressed Sensing (CS) enable accelerated MR imaging. However, the actual PI-CS reconstruction performance is usually limited by noise amplification and image boundary/structure blurring particularly at high reduction factor. In this work, a Gaussian Mixture Model (GMM) was optimized to promote structured sparsity and it was further merged into the SPIRiT framework as a regularization constraint. The proposed algorithm has demonstrated its improved performance for image boundary and detail structure preservation in accelerated 3D high resolution brain imaging.

3417.   86 Partial discreteness: a new type of prior knowledge for MRI reconstruction
Gabriel Ramos-Llordén1, Hilde Segers1, Willem Jan Palenstijn1, Arnold J. den Dekker1,2, and Jan Sijbers1
1iMinds Vision-Lab, University of Antwerp, Antwerp, Antwerp, Belgium, 2Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands

In MRI reconstruction, undersampled data sets lead to ill-posed reconstruction problems. To regularize these problems, prior knowledge is commonly exploited. In this work, we introduce a new type of prior knowledge, partial discreteness, where part of the image is assumed to be homogeneous and can be well represented by a constant magnitude. We introduce this prior in the common algebraic reconstruction problem and propose an iterative algorithm to approximately solve it. It combines a penalized least squares reconstruction with an internal Bayesian segmentation. Results with synthetic data demonstrate that more detailedly restored images are obtained when partial discreteness is exploited

3418.   87 Novel Non-Local Total Variation Regularization for Constrained MR Reconstruction
Andres Saucedo1,2, Stamatios Lefkimmiatis3, Stanley Osher3, and Kyunghyun Sung1,2
1Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States, 2Biomedical Physics Interdepartmental Graduate Program, University of California Los Angeles, Los Angeles, California, United States, 3Department of Mathematics, University of California Los Angeles, Los Angeles, California, United States

This study introduces a novel constrained reconstruction technique that exploits both the local correlation of image data across multiple coils and the inherent non-local self-similarity property of images. Our approach is based within a non-local total variation regularization framework. The proposed method is applicable to both compressed sensing and parallel imaging, and demonstrates substantial advantages with regard to high levels of noise.

3419.   88 Highly Undersampling MR Image Reconstruction Using Tree-Structured Wavelet Sparsity and Total Generalized Variation Regularization
Ryan Wen Liu1, Lin Shi2, Simon C.H. Yu1, and Defeng Wang1,3
1Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, 2Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, 3Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

In this study, we propose to combine L0 regularized tree-structured wavelet sparsity (TsWS) and second-order total generalized variation (TGV2) to reconstruct MR image from highly undersampled k-space data. In particular, the L0 regularized TsWS could better represent the measure of sparseness in wavelet domain. TGV2 is capable of maintaining trade-offs between artefact suppression and tissue feature preservation. To achieve solution stability, the corresponding minimization problem is decomposed into several simpler subproblems. Each of these subproblems has a closed-form solution or can be efficiently solved using existing optimization algorithms. Experimental results have demonstrated the superior performance of our proposed method.

3420.   89 META: Multiple Entangled denoising and Thresholding Algorithms for suppression of MR image reconstruction artifacts
Johannes F. M. Schmidt1 and Sebastian Kozerke1,2
1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 2Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom

A statistical approach to combine multiple denoising algorithms in MR image reconstruction to suppress reconstruction artifacts.

3421.   90 Double Smoothing Method-based Algorithm for MR Image Reconstruction with Partial Fourier Data
Xiaohui Liu1, Jinhong Huang1, Wufan Chen1, and Yanqiu Feng1
1Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China

Undersampled MRI reconstruction techniques based on Compressed Sensing (CS) exploiting sparsity which is implicit in MR images can provide significant help in reducing the scan time during clinical period, but remains challenging due to the requirement of high reconstruction accuracy. A novel algorithm is developed and tested in vivo for solving the MR image reconstruction problem due to Nesterov¡¯s smoothing scheme and convex conic optimization.

3422.   91 MR Image Reconstruction from under-sampled measurements using local and global sparse representations
MingJian Hong1, MengRan Lin1, Feng Liu2, and YongXin Ge1
1ChongQing University, ChongQing, ChongQing, China, 2ITEE, The University of Queensland, QLD, Australia

This work presented a new model by enforcing both local and global sparsity, which captures both the patch-level and global sparse structures of the anatomical images. Using a model split approach, the image reconstruction quality can be iteratively further improved. Our simulation results demonstrate that, the proposed method outperform those existing methods using only the patch-level or global sparse structure.

3423.   92 Balanced sparse MRI model: Bridge the analysis and synthesis sparse models in compressed sensing MRI
Yunsong Liu1, Jian-Feng Cai2, Zhifang Zhan1, Di Guo3, Jing Ye1, Zhong Chen1, and Xiaobo Qu1
1Department of Electronic Science, Xiamen University, Xiamen, Fujian, China, 2Department of Mathematics, University of Iowa, Iowa City, Iowa, United States, 3School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, Fujian, China

Compressed sensing (CS) has shown to be promising to accelerate magnetic resonance imaging (MRI). There are two different sparse models in CS-MRI: analysis and synthesis models with different assumptions and performance when a redundant tight frame is used. A new balance model is introduced into CS-MRI that can achieve the solutions of the analysis model, synthesis model and some in between by tuning the balancing parameter. It is found in this work that the typical balance model has a comparable performance with the analysis model in CS-MRI. Both of them achieve lower reconstructed errors than the synthesis model no matter what value the balancing parameter is. These observations are consistent for different tight frames used CS-MRI.

3424.   93 Joint MR-PET reconstruction using vector valued Total Generalized Variation
Florian Knoll1,2, Martin Holler3, Thomas Koesters1,2, and Daniel K Sodickson1,2
1Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, United States, 2Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, United States, 3Department of Mathematics and Scientific Computing, University of Graz, Graz, Austria

It was recently shown that simultaneously acquired data from state-of-the-art MR-PET systems can be reconstructed simultaneously using the concept of joint sparsity, yielding benefits for both MR and PET reconstructions. In this work we propose a new dedicated regularization functional for multi-modality imaging that exploits common structures of the MR and PET images. The two modalities are treated as single multi-channel images and an extension of the second order Total Generalized Variation functional for vector valued data is used as a dedicated multi-modality sparsifying transform.

94 A New Region Based Volume Wised Method for PET-MR Imaging Using Artificial Neural Network
Chenguang Peng1, Rong Guo1, Yicheng Chen1, Yingmao Chen2, Quanzheng Li3, Georges El Fakhr3, and Kui Ying1
1Key Laboratory of Particle and Radiation Imaging, Ministry of Education, Department of Engineering, Beijing, China, 2Department of Nuclear Medicine, The general hospital of Chinese People's Liberation, Beijing, China, Beijing, China, 3Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Harvard Medical School, Boston, United States

PET is a practical medical imaging technique for brain function diagnosis. However, the low spatial resolution limits the use of PET in neurology and disease like Alzheimer's disease. With the help of MRI-PET, people can use high resolution MRI to provide anatomical information to correct partial volume effect of PET image which is a great cause for low resolution. Nevertheless, traditional partial volume effect correction method requires an accurate MRI segmentation and PVE model estimation which are not usually applicable. In this work, we proposed a method that is insensitive to PVE model estimation error and segmentation error.

3426.   95 Reliability of MR sequences used for attenuation correction in PET/MR - video not available
Mathias Lukas1, Anne Kluge2, Jorge Cabello1, Christine Preibisch2,3, and Stephan Nekolla1
1Department of Nuclear Medicine, Klinikum rechts der Isar, TU München, Munich, Germany, 2Department of Neuroradiology, Klinikum rechts der Isar, TU München, Munich, Germany, 3Department of Neurology, Klinikum rechts der Isar, TU München, Munich, Germany

Attenuation correction (AC) in quantitative PET/MR is affected by SNR and CNR of underlying MR sequences. In this work, the quality of MR data currently used for attenuation correction in PET (UTE, DIXON, MPRAGE) was observed in-vivo under changing clinical conditions over 3 months to investigate the reliability and robustness for in-house established MR based AC methods. In spite of its semi quantitative character, all sequences were found to be very invariant in SNR and CNR and can be used without any concerns.

3427.   96 PET attenuation correction for PET/MR by combining MR segmentation and selective-update joint estimation
Lishui Cheng1, Sangtae Ahn1, Dattesh Shanbhag2, Florian Wiesinger3, Sandeep Kaushik2, and Ravindra Manjeshwar1
1GE Global Research, Niskayuna, NY, United States, 2GE Global Research, Bangalore, India, 3GE Global Research, Munich, Germany

Attenuation correction is critical to accurate PET quantitation. In PET/MR, MR-based attenuation correction (MR-AC) has challenges in bone, air, lung and implant regions. To address the problem, we combined 1) a segmentation-based MR-AC method, which works well in soft-tissue regions, and 2) a selective-update joint estimation approach, which reconstructs both attenuation and activity from PET emission data, to resolve the attenuation in the challenging regions. The method was evaluated on clinical data from a PET/MR scanner with TOF information and it was demonstrated that the method can distinguish between abdominal air and spinal implant/bone regions, otherwise hidden in MR.