Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints

Qiang Ning, Chao Ma, Zhi Pei Liang

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

Abstract

This paper addresses the long-standing spectral quantitation problem in magnetic resonance spectroscopic imaging (MRSI). Although a large body of work has been done to develop robust solutions to the problem for practical MRSI applications, the problem remains challenging due to low signal-to-noise ratio (SNR) and model nonlinearity. Building on the existing work on the use of prior knowledge (in the form of spectral basis) for spectral estimation, this paper reformulates spectral quantitation as a joint estimation problem, and utilizes a regularization framework to enforce spatial constraints (e.g., spatial smoothness or transform sparsity) on the spectral parameters. Simulation and experimental results show that the proposed method, by exploiting both the spatial and spectral characteristics of the underlying signals, can significantly improve the estimation accuracy of the spectral parameters over state-of-the-art methods.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages1482-1485
Number of pages4
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Keywords

  • Cramér-Rao bound
  • MRSI
  • sparsity constraint
  • spatial regularization
  • spectral estimation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Ning, Q., Ma, C., & Liang, Z. P. (2015). Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (pp. 1482-1485). [7164157] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7164157