|Imaging in the Post-Nyquist Era|
Dynamic Functional Volumetric Magnetic Resonance
K-Space Inverse Imaging of Human Visual System
Fa-Hsuan Lin1, 2, Thomas Witzel1, 3, Graham Wiggins1, Lawrence Wald1, John Belliveau1
1Massachusetts General Hospital, Charlestown, Massachusetts, USA; 2National Taiwan University, Taipei, Taiwan; 3Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USA
We propose a K-space magnetic resonance Inverse Imaging (K-InI) approach to use a highly parallel radio-frequency coil array to achieve high temporal resolution MRI. K-InI solves an under-determined linear system using regularization in parallel MRI reconstruction. K-InI uses auto-calibration technique to estimate the reconstruction coefficients and it can provide coil-by-coil reconstruction to allow for more flexible combination of different channels in the coil array. We demonstrate K-InI using a 3D visual fMRI experiment to achieve 100 ms temporal resolution.
High Spatial High Temporal Resolution MR-Encephalography
Using Constraint Reconstruction Based on Regularization with Arbitrary
Thimo Grotz1, Benjamin Zahneisen1, Arsène Ella1, Jürgen Hennig1
1University Hospital Freiburg, Freiburg, Germany
MREG, also called inverse imaging, was introduced as a new approach to measure activation related MR-signal changes in the brain, with very high temporal resolu-tion. We present a constraint reconstruction based on regularization with arbitrary projections to localize the activation. The results demonstrate that COBRA with very low number of projections can be used to acquire activation maps with reasonable spatial resolution at very high temporal resolution. Signal time courses show excel-lent contrast-to-noise for the observed BOLD response.
Quantitative 23-Sodium and 17-Oxygen MR
Imaging in Human Brain at 9.4 Tesla Enhanced by Constrained K-Space
Ian C. Atkinson1, Keith R. Thulborn1, Aiming Lu1, Justin Haldar2, X J. Zhou1, Ted Claiborne1, Zhi-Pei Liang2
1University of Illinois-Chicago, Chicago, Illinois, USA; 2University of Illinois-Urbana-Champaign, Urbana, Illinois, USA
The sensitivity of ultra-high field MRI enables quantitative imaging of non-proton species such as 23-sodium and 17-oxygen. Constrained k-space reconstruction techniques can be used to improve the spatial resolution of the acquired data without compromising the ability to quantify the final image. This approach of enhanced image reconstruction combined with the improved sensitivity of high field broadens the human applications of metabolic MR imaging by minimizing otherwise long acquisition times to achieve adequate spatial resolution for the anatomy and SNR performance for quantification.
Highly Undersampled 3D Golden Ratio Radial Imaging
with Iterative Reconstruction
Mariya Doneva1, Holger Eggers2, Jürgen Rahmer2, Peter Börnert2, Alfred Mertins1
1University of Luebeck, Luebeck, Germany; 2Philips Research Europe, Hamburg, Germany
We illustrate the feasibility of Compressed Sensing for 3D dynamic imaging using highly undersampled 3D radial acquisition with golden ratio profile ordering. Image reconstruction from a low number of measurements could be very useful for dynamic 3D imaging, to reduce the often long acquisition times and thus improve temporal resolution in 3D MRI. Using CS, the aliasing artifacts were significantly reduced and a high frame rate was achieved, allowing dynamic imaging with good temporal resolution. The described approach could be particularly useful for dynamic studies of joint motion.
Three-Dimensional Compressed Sensing for Dynamic MRI
Ali Bilgin1, 2, Ted P. Trouard1, Maria I. Altbach1, Natarajan Raghunand1
1University of Arizona, Tucson, Arizona , USA
Compressed Sensing (CS) theory illustrates that a small number of linear measurements can be sufficient to reconstruct sparse or compressible signals. we introduce a CS theory based method for reconstruction of time-varying radial k-space data by exploiting the spatio-temporal sparsity of Dynamic Contrast Enhanced (DCE) MRI images. The proposed method significantly reduces undersampling artifacts and can provide high temporal and spatial resolution.
Constrained Compressed Sensing for Fast 3D
Visualization of Active Catheters
Carsten Oliver Schirra1, 2, Sascha Krueger3, Steffen Weiss3, Reza Razavi1, Tobias Schaeffter1, Sebastian Kozerke2
1King's College London, London, UK; 2University and ETH Zurich, Zurich, Switzerland; 3Philips Medical Systems, Hamburg, Germany
With standard dynamic 3D imaging methods sufficient spatial resolution is difficult to achieve at the required temporal rates when visualizing interventional devices. Active catheters lend themselves well to undersampling methods given their confined sensitivity volume. Compressed Sensing allows exploiting the image sparseness inherent to images acquired with active catheter antennae, however the associated iterative reconstruction algorithms are time-expensive. In this work, the feasibility of using Compressed Sensing for accelerating 3D imaging of active catheters is investigated. Dedicated constraints are introduced taking into account the known catheter length and position in order to minimize the number of iterations in reconstruction.
HYPR-Constrained Compressed Sensing
Reconstruction for Accelerated Time Resolved Imaging
Huimin Wu1, Walter F. Block1, Alexey A. Samsonov1
1University of Wisconsin-Madison, Madison, USA
Constrained reconstruction methods have been shown to produce significant accelerations to date, but suffer some temporal inaccuracy when vessels with different temporal behaviors are nearby or as the sparsity of the image volume decreases. We present simulated comparisons of a single pass reconstruction method (Highly constrained Projection Local Reconstruction or HYPRLR) and an iterative constrained reconstruction method termed HYPR Reconstruction by Iterative Estimation (HYPRIT). We demonstrate increased temporal accuracy for HYPRIT relative to HYPR LR, but also demonstrate how HYPRIT’s performance improves when using the HYPR LR image as a constraining image. Finally, rapid CE-MRA capabilities are demonstrated.
A Comparison of L1 and L2 Norms as Temporal
Constraints for Reconstruction of Undersampled Dynamic Contrast Enhanced
Cardiac Scans with Respiratory Motion
Ganesh Adluru1, 2, Edward VR DiBella1
1University of Utah, Salt Lake City, USA
Constrained reconstruction methods can be used to accelerate the acquisition of cardiac dynamic contrast-enhanced MRI data. The temporal constraint term is important for determining the quality of reconstructions from undersampled data. Here we compare and evaluate reconstructions obtained by using an L2-norm and an L1-norm as temporal constraints. The reconstructions were compared using data with simulated undersampling and using actual undersampled radial data acquired from the scanner. Using an L1-norm in the temporal constraint helps in obtaining better reconstructions as compared to using an L2-norm in the temporal constraint especially when there is respiratory motion in the data.
Accelerated Dynamic Imaging by Reconstructing Sparse
Differences Using Compressed Sensing
André Fischer1, 2, Felix Breuer2, Martin Blaimer2, Nicole Seiberlich1, Peter Michael Jakob1, 2
1University of Wuerzburg, Wuerzburg, Germany; 2Research Center for Magnetic Resonance Bavaria e.V., Wuerzburg, Germany
The concept of Compressed Sensing offers a new perspective for accelerated magnet resonance imaging. We demonstrate the use of CS in connection with dynamic imaging. The proposed method reconstructs the differences between a certain timeframe and the composite image of a dynamic dataset. By choosing a radial trajectory, the artifacts in the undersampled image are incoherent, and, therefore, beneficial for the CS algorithm. We achieved good reconstructions with as less as 14 projections (192 x 192 matrix size). Hence, this technique is promising for future real-time dynamic applications.
MRI Compressed Sensing Via Sparsifying Images
Alexey Samsonov1, Youngkyoo Jung1, Andrew L. Alexander1, Walter F. Block1, Aaron S. Field1
1University of Wisconsin, Madison, Wisconsin, USA
Recently, there has been an emerging interest to accelerate MRI through iterative reconstruction of undersampled data based on compressed sensing theory. We extend the compressed sensing framework via sparsifying images. The new method utilizes the recent idea in HYPR methods to use sliding window composite images to constrain reconstruction. At the same time, such enhancement is done within the compressed sensing framework. We demonstrate that the new method, HighlY constrained back PRojection by Iterative esTimation (HYPRIT), may be a powerful tool for image reconstruction from highly undersampled data. We demonstrate its potential for accelerated radial diffusion tensor imaging.