Parallel Imaging  




Time 
Prog # 

11:00  4. 
Practical Considerations for GRAPPAAccelerated ReadoutSegmented EPI in DiffusionWeighted Imaging Samantha J. Holdsworth^{1}, Stefan Skare^{1}, Rexford D. Newbould^{1}, Anders Nordell^{2}, Roland Bammer^{1} ^{1}Stanford University, Palo Alto, California , USA; ^{2}Karolinska University Hospital, Stockholm, Sweden Readout mosaic segmentation (RSEPI) has been suggested as an alternative approach to EPI for high resolution diffusionweighted imaging (DWI) with minimal geometric distortions. In this abstract, peripherally cardiac gated and nongated RSEPIDW images are acquired with the use of parallel imaging. The methods used to phase correct and reconstruct the partial Fourier GRAPPAaccelerated RSEPIDW data are described. It is shown that patient handling can be simplified with the use of nongated acquisitions and minimallyoverlapping blinds. The efficient acquisition of high resolution RSEPI images makes this sampling strategy a useful alternative to other navigated methods used for DW imaging. 

11:12  5. 
AutoCalibrated Parallel Imaging Reconstruction Using KSpace Sparse
Matrices (KSPA) Chunlei Liu^{1}, Jian Zhang^{1}, Michael E. Moseley^{1} ^{1}Stanford University, Stanford, California , USA A noniterative parallel imaging reconstruction algorithm that utilizes kspace sparse matrix (kSPA) was recently introduced for arbitrary sampling patterns. The kSPA algorithm computes a sparse reconstruction matrix in kspace. This algorithm was shown to be particularly useful for a wide range of applications including 3D imaging, functional MRI (fMRI), perfusionweighted imaging, diffusion tensor imaging (DTI) and massive parallel imaging, where a large number of images have to be reconstructed. The original algorithm requires the acquisition of lowresolution coil sensitivity maps. We present an autocalibrated kSPA algorithm for arbitrary trajectories that does not require the explicit estimation of coil sensitivities. 

11:24  6. 
WholeHeart Imaging Using Undersampled Radial Phase Encoding and a
32Channel Cardiac Coil Redha Boubertakh^{1},^{ 2}, Philip G. Batchelor^{1}, Sergio Uribe^{1}, Thomas S. Sørensen^{2}, Michael S. Hansen^{2}, Reza S. Razavi^{1}, Tobias Schaeffter^{1} ^{1}King's College London, London, UK; ^{2}University College London, London, UK We present a new 3D acquisition for wholeheart imaging that combines radial kspace phase encoding in the kykz plane and Cartesian readout sampling. Fully sampled data were acquired on a volunteer using a 32channel cardiac coil. The raw data were undersampled offline with different acceleration factors (R = 8 and 12). The images were reconstructed using gridding and iterative SENSE techniques. When compared to the fully sampled volume, iterative SENSE provides good quality images where artifact levels are strongly reduced compared to gridding. This would lead to a significant decrease in the scan time. 

11:36  7. 
Reconstruction of Undersampled NonCartesian Data Using
GROGFacilitated Random Blipped Phase Encoding Nicole Seiberlich^{1}, Philipp Ehses^{1}, Felix A. Breuer^{2}, Martin Blaimer^{2}, Peter M. Jakob^{1},^{ 2}, Mark A. Griswold^{3} ^{1}University of Wuerzburg, Wuerzburg, Germany; ^{2}Research Center Magnetic Resonance Bavaria (MRB), Wuerzburg, Germany; ^{3}University Hospitals of Cleveland, Cleveland, Ohio, USA It has been shown that the Generalized Sampling Theorem of Papoulis can be exploited to reduce measurement time by acquiring points blipped in the phase encoding direction and applying a conjugate gradient (CG) reconstruction. Such a blipped trajectory can also be mimicked using a standard trajectory in conjunction with the GRAPPA Operator Gridder (GROG) to shift kspace points; a subsequent CG reconstruction results in an unaliased image even when the Nyquist criterion has not been met in all portions of kspace. The acceleration of in vivo radial, spiral, and rosette images is demonstrated using GROG to generate random blipped points. 

11:48  8. 
Direct
Virtual Coil (DVC) Reconstruction for DataDriven Parallel Imaging Philip James Beatty^{1}, Wei Sun^{2}, Anja C. S. Brau^{1} ^{1}GE Healthcare, Menlo Park, California , USA; ^{2}GE Healthcare, Waukesha, Wisconsin, USA A method is proposed for improving the computational efficiency of datadriven parallel imaging reconstruction, while maintaining good image quality. The proposed method forgoes the computationally expensive ‘coilbycoil’ approach introduced by GRAPPA in favor of directly synthesizing ‘virtual coil’ data. Results show that the proposed method is able to achieve similar SNR to coilbycoil approaches and offers similar resiliency to phase cancellation artifacts, while reducing the data synthesis computation by a factor of 20X for 32channel arrays and over 100X for 128channel arrays. 

12:00  9. 
Parallel Reconstruction Using Null Operations (PRUNO) Jian Zhang^{1},^{ 2}, Chunlei Liu^{1}, Michael Moseley^{1} ^{1}Stanford University, Stanford, California , USA A new GRAPPA based iterative Cartesian parallel reconstruction method is proposed which is called Parallel Reconstruction Using Null Operations (PRUNO). In PRUNO, some local null operators are applied on all kspace locations to formulate the reconstruction problem as linear equations. We also demonstrate that it can be solved efficiently and accurately with a conjugate gradient method. According to our preliminary simulation and in vivo results, PRUNO can be used to improve the accuracy of image reconstruction compared to GRAPPA, especially at high image acceleration rate. Besides, since we usually use merely small local operators in PRUNO, only a small number of ACS lines are required, independent of the exact reduction rate. 

12:12 
10. 
A General Formulation for Quantitative GFactor Calculation in GRAPPA
Reconstructions Felix A. Breuer^{1}, Martin Blaimer^{1}, Nicole Seiberlich^{2}, Peter M. Jakob^{1},^{ 2}, Mark A. Griswold^{3} ^{1}Research Center Magnetic Resonance Bavaria, Würzburg, Germany; ^{2}University of Würzburg, Würzburg, Germany; ^{3}University Hospitals of Cleveland, Cleveland, USA In this work, equivalent to the gfactor in SENSE reconstructions, a theoretical description for quantitative estimation of the noise enhancement in GRAPPA reconstructions is described. The Grappa gfactor is derived directly from the GRAPPA reconstruction weights. In addition, the procedure presented here allows the calculation of quantitative gfactor maps for both the uncombined and combined accelerated GRAPPA images. 

12:24  11. 
A Prospective Error Measure for KT SENSE Shaihan J. Malik^{1}, Jo V. Hajnal^{1} ^{1}Hammersmith Hospital, Imperial College London, London, UK In parallel imaging the 'gfactor' provides a vital prospective measure of noise amplification. For dynamic undersampled techniques such as kt SENSE, in addition to typical noise amplification from parallel imaging, errors can arise from temporal filtering due to the prior information (training data). We define an analogous quantity to g termed g^{kt} which includes both effects, and investigate its correlation with reconstruction error and its spatiotemporal distribution. Results from retrospectively undersampled cardiac images and numerical phantoms indicate that g^{kt} is a useful tool for relative comparison between different undersample strategies given an object and receiver coil setup. 

12:36  12. 
Influence of Regularization on Noise Amplification in Iterative SENSE
Reconstruction Holger Eggers^{1}, Peter C. Mazurkewitz^{1} ^{1}Philips Research Europe, Hamburg, Germany Results of previously performed statistical estimations of the noise amplification in nonCartesian sensitivity encoding imaging with Monte Carlo simulations remain questionable due to their dependence on the number of iterations after which the reconstruction is stopped. In this work, the use of explicit regularization is advocated, and it is demonstrated to stabilize the convergence of the reconstruction and to virtually eliminate this dependence. Calculated maps of the noise amplification are thus rendered more reliable and better comparable between different sampling patterns. Potential advantages of nonCartesian acquisitions, like more homogeneous and lower maximum noise amplification, are confirmed and substantiated with this approach. 

12:48  13. 
SENSE Regularization Using Bregman Iterations Bo Liu^{1}, Kevin F. King^{2}, Michael C. Steckner^{3}, Lei Ying^{1} ^{1}University of WisconsinMilwaukee, Milwaukee, Wisconsin, USA; ^{2}GE Healthcare, Waukesha, Wisconsin, USA; ^{3}Toshiba Medical Research Institute USA, Inc., Cleveland, Ohio, USA The illconditioning problem has been addressed by Tikhonov regularization inCartesian SENSE with some success. However, a highquality regularization image is needed to preserve the details, and otherwise the reconstruction is overlysmooth. In this abstract, we propose a new regularization technique usingBregman iteration. Without any need for regularization images, the methoditeratively refine the total variation (TV) regularization such that theregularized image has more fine details than using TV regularization alone. The proposed method is shown to address the oversmooth problem in Tikhonov regularization and the blocky artifacts in TV regularization. 
