Robust kernel methods for sparse MR image reconstruction

Authors:
Joshua Trzasko;Armando Manduca;Eric Borisch
Affiliations:
Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, MN;Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, MN;Magnetic Resonance Research Lab, Mayo Clinic College of Medicine, Rochester, MN
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

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Abstract

A major challenge in contemporary magnetic resonance imaging (MRI) lies in providing the highest resolution exam possible in the shortest acquisition period. Recently, several authors have proposed the use of L1-norm minimization for the reconstruction of sparse MR images fromhighly-undersampled k-space data. Despite promising results demonstrating the ability to accurately reconstruct images sampled at rates significantly below the Nyquist criterion, the extensive computational complexity associated with the existing framework limits its clinical practicality. In this work, we propose an alternative recovery framework based on homotopic approximation of the L0-norm and extend the reconstruction problemto a multiscale formulation. In addition to several interesting theoretical properties, practical implementation of this technique effectively resorts to a simple iterative alternation between bilteral filtering and projection of themeasured k-space sample set that can be computed in a matter of seconds on a standard PC.