Improved image reconstruction from sensitivity-encoded data by wavelet denoising and tikhonov regularization

Zhi Pei Liang, Roland Bammer, Jim Ji, Norbert J. Pelc, Gary H. Glover

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

Abstract

Parallel magnetic resonance imaging through sensitivity encoding using multiple receiver coils has emerged as an effective tool to reduce imaging time. However, errors in both the estimated coil sensitivity maps and the measured data, and the ill-conditioned nature of the coefficient matrix (often associated with non-localized coils) can degrade image quality significantly, limiting speed enhancements. In this paper, we propose to use wavelet denoising to reduce noise in the coil sensitivity maps and a specially-designed TIkhonov regularization scheme to solve the ill-conditioned matrix equation. Experimental results show that these techniques produce significantly better images (with an improved signal-to-noise ratio and reduced aliasing artifacts) than conventional reconstruction methods based on matrix inversion with a diagonal regularization matrix.

Original languageEnglish (US)
Title of host publication2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings
PublisherIEEE Computer Society
Pages493-496
Number of pages4
ISBN (Electronic)078037584X
DOIs
StatePublished - Jan 1 2002
EventIEEE International Symposium on Biomedical Imaging, ISBI 2002 - Washington, United States
Duration: Jul 7 2002Jul 10 2002

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2002-January
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

OtherIEEE International Symposium on Biomedical Imaging, ISBI 2002
CountryUnited States
CityWashington
Period7/7/027/10/02

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Improved image reconstruction from sensitivity-encoded data by wavelet denoising and tikhonov regularization'. Together they form a unique fingerprint.

Cite this