Robust Kernel methods for sparse MR image reconstruction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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 from highly-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 problem to 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 the measured k-space sample set that can be computed in a matter of seconds on a standard PC.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages8
EditionPART 1
ISBN (Print)9783540757566
StatePublished - 2007
Event10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007 - Brisbane, Australia
Duration: Oct 29 2007Nov 2 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4791 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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