Samantha J. Holdsworth1, Stefan Skare1, Rexford D. Newbould1, Anders Nordell2, Roland Bammer1
1Stanford University, Palo Alto, California , USA; 2Karolinska University Hospital, Stockholm, Sweden
Readout mosaic segmentation (RS-EPI) has been suggested as an alternative approach to EPI for high resolution diffusion-weighted imaging (DWI) with minimal geometric distortions. In this abstract, peripherally cardiac gated and non-gated RS-EPI-DW images are acquired with the use of parallel imaging. The methods used to phase correct and reconstruct the partial Fourier GRAPPA-accelerated RS-EPI-DW data are described. It is shown that patient handling can be simplified with the use of non-gated acquisitions and minimally-overlapping blinds. The efficient acquisition of high resolution RS-EPI images makes this sampling strategy a useful alternative to other navigated methods used for DW imaging.
Auto-Calibrated Parallel Imaging Reconstruction Using K-Space Sparse
Chunlei Liu1, Jian Zhang1, Michael E. Moseley1
1Stanford University, Stanford, California , USA
A non-iterative parallel imaging reconstruction algorithm that utilizes k-space sparse matrix (kSPA) was recently introduced for arbitrary sampling patterns. The kSPA algorithm computes a sparse reconstruction matrix in k-space. This algorithm was shown to be particularly useful for a wide range of applications including 3D imaging, functional MRI (fMRI), perfusion-weighted 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 low-resolution coil sensitivity maps. We present an auto-calibrated kSPA algorithm for arbitrary trajectories that does not require the explicit estimation of coil sensitivities.
Whole-Heart Imaging Using Undersampled Radial Phase Encoding and a
32-Channel Cardiac Coil
Redha Boubertakh1, 2, Philip G. Batchelor1, Sergio Uribe1, Thomas S. Sørensen2, Michael S. Hansen2, Reza S. Razavi1, Tobias Schaeffter1
1King's College London, London, UK; 2University College London, London, UK
We present a new 3D acquisition for whole-heart imaging that combines radial k-space phase encoding in the ky-kz plane and Cartesian readout sampling. Fully sampled data were acquired on a volunteer using a 32-channel 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.
Reconstruction of Undersampled Non-Cartesian Data Using
GROG-Facilitated Random Blipped Phase Encoding
Nicole Seiberlich1, Philipp Ehses1, Felix A. Breuer2, Martin Blaimer2, Peter M. Jakob1, 2, Mark A. Griswold3
1University of Wuerzburg, Wuerzburg, Germany; 2Research Center Magnetic Resonance Bavaria (MRB), Wuerzburg, Germany; 3University 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 k-space points; a subsequent CG reconstruction results in an unaliased image even when the Nyquist criterion has not been met in all portions of k-space. The acceleration of in vivo radial, spiral, and rosette images is demonstrated using GROG to generate random blipped points.
Virtual Coil (DVC) Reconstruction for Data-Driven Parallel Imaging
Philip James Beatty1, Wei Sun2, Anja C. S. Brau1
1GE Healthcare, Menlo Park, California , USA; 2GE Healthcare, Waukesha, Wisconsin, USA
A method is proposed for improving the computational efficiency of data-driven parallel imaging reconstruction, while maintaining good image quality. The proposed method forgoes the computationally expensive ‘coil-by-coil’ 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 coil-by-coil approaches and offers similar resiliency to phase cancellation artifacts, while reducing the data synthesis computation by a factor of 20X for 32-channel arrays and over 100X for 128-channel arrays.
Parallel Reconstruction Using Null Operations (PRUNO)
Jian Zhang1, 2, Chunlei Liu1, Michael Moseley1
1Stanford 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 k-space 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.
A General Formulation for Quantitative G-Factor Calculation in GRAPPA
Felix A. Breuer1, Martin Blaimer1, Nicole Seiberlich2, Peter M. Jakob1, 2, Mark A. Griswold3
1Research Center Magnetic Resonance Bavaria, Würzburg, Germany; 2University of Würzburg, Würzburg, Germany; 3University Hospitals of Cleveland, Cleveland, USA
In this work, equivalent to the g-factor in SENSE reconstructions, a theoretical description for quantitative estimation of the noise enhancement in GRAPPA reconstructions is described. The Grappa g-factor is derived directly from the GRAPPA reconstruction weights. In addition, the procedure presented here allows the calculation of quantitative g-factor maps for both the uncombined and combined accelerated GRAPPA images.
A Prospective Error Measure for K-T SENSE
Shaihan J. Malik1, Jo V. Hajnal1
1Hammersmith Hospital, Imperial College London, London, UK
In parallel imaging the 'g-factor' provides a vital prospective measure of noise amplification. For dynamic undersampled techniques such as k-t 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 gkt 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 gkt is a useful tool for relative comparison between different undersample strategies given an object and receiver coil setup.
Influence of Regularization on Noise Amplification in Iterative SENSE
Holger Eggers1, Peter C. Mazurkewitz1
1Philips Research Europe, Hamburg, Germany
Results of previously performed statistical estimations of the noise amplification in non-Cartesian 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 non-Cartesian acquisitions, like more homogeneous and lower maximum noise amplification, are confirmed and substantiated with this approach.
SENSE Regularization Using Bregman Iterations
Bo Liu1, Kevin F. King2, Michael C. Steckner3, Lei Ying1
1University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA; 2GE Healthcare, Waukesha, Wisconsin, USA; 3Toshiba Medical Research Institute USA, Inc., Cleveland, Ohio, USA
The ill-conditioning problem has been addressed by Tikhonov regularization inCartesian SENSE with some success. However, a high-quality 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.