Meeting Banner
Abstract #2809

Accelerating Non-Cartesian, Sparsity-Promoting Image Reconstruction Via Line Search FISTA

Matthew J. Muckley1, Jeffrey A. Fessler2, and Marcelo V. W. Zibetti1

1Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States, 2Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

Iterative reconstruction algorithms for non-Cartesian MRI can have slow convergence due to the nonuniform density of k-space samples. Convergence speed can be improved by including the density compensation function into the algorithm, but current techniques for doing so can lead to SNR penalties or algorithm divergence. Here, we combine the use of density compensation with a line search under the MFISTA framework. The method has the convergence guarantees of MFISTA while gaining the speed improvements of using the density compensition function. The algorithm generalizes further to any FISTA algorithm.

This abstract and the presentation materials are available to members only; a login is required.

Join Here