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Abstract #4208

A Distributed Compressive Sensing Strategy for Non-Cartesian MRI: Applications to SWIRLS CE-MRA

Joshua D. Trzasko1, Yunhong Shu2, Armando Manduca1, John Huston III2, Matt A. Bernstein2

1Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States; 2Department of Radiology, Mayo Clinic, Rochester, MN, United States

In non-Cartesian image reconstruction, equality data constraints cannot be explicitly enforced, which limits the applicability of popular methods like projection-onto-convex-sets (POCS) to this area. In this work, we demonstrate that they can be implicitly enforced via a specific affine projection, and discuss numerical methods for efficiently imposing them. We use this construction to develop an efficient Compressive Sensing reconstruction based on block-wise redundant sparsity constraints, which results in strong reconstruction performance. We demonstrate the proposed reconstruction strategy for CE-MRA acquired using the 3D SWIRLS trajectory.

Keywords

accelerated acquisition acquisitions adhering affine aliasing anatomical applications approximate approximately approximation artifact axial baker block blocks bolus boundary cache channel clinic coil collision compressive comprises computation conditions constrained constraint constraints contrast convex core correspond cosine cross currently decomposed defined definite demanding descent dimension discrete disjoint distributed domain driven eddy efficient employed employment enforced enforcing equality example exhibit explicitly exploit extraction facilitate feasible flush free global highly identically identify improved improves injected inversion invertible iteration john joint kaiser learned least libraries machine make manageable manually matrix mayo memory minimization minute minutes model noise notice novel often operator orthonormal overlap oversampled parallelization performance physiology positive power problems processing projection proposed protocol quality realized reconstruction reconstructions reduced samples sampling scanned selected semantically semi sensing shot shots since slice soft software spaced sparse sparsity spherical spread squares strategies strategy suited suppose swirls targeted text threshold thresholding trajectory transform transforms uniform uniformly update utilization vein vessel