Robust kernel methods for sparse MR image reconstruction.

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

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 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 : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages809-816
Number of pages8
Volume10
EditionPt 1
StatePublished - 2007

Fingerprint

Computer-Assisted Image Processing
Magnetic Resonance Imaging

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Trazasko, J. D., Manduca, A., & Borisch, E. (2007). Robust kernel methods for sparse MR image reconstruction. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 10, pp. 809-816)

Robust kernel methods for sparse MR image reconstruction. / Trazasko, Joshua D; Manduca, Armando; Borisch, Eric.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 10 Pt 1. ed. 2007. p. 809-816.

Research output: Chapter in Book/Report/Conference proceedingChapter

Trazasko, JD, Manduca, A & Borisch, E 2007, Robust kernel methods for sparse MR image reconstruction. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 edn, vol. 10, pp. 809-816.
Trazasko JD, Manduca A, Borisch E. Robust kernel methods for sparse MR image reconstruction. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 ed. Vol. 10. 2007. p. 809-816
Trazasko, Joshua D ; Manduca, Armando ; Borisch, Eric. / Robust kernel methods for sparse MR image reconstruction. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 10 Pt 1. ed. 2007. pp. 809-816
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