Model-based MR parameter mapping with sparsity constraint

Bo Zhao, Fan Lam, Wenmiao Lu, Zhi Pei Liang

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

Abstract

MR parameter mapping (e.g., T1 mapping, T2 mapping, or T2* mapping) is a valuable tool for tissue characterization. However, its practical utility has been limited due to long data acquisition time. This paper addresses this problem with a new model-based parameter mapping method, which utilizes an explicit signal model and imposes a sparsity constraint on the parameter values. The proposed method enables direct estimation of the parameters of interest from highly undersampled, noisy k-space data. An algorithm is presented to solve the underlying parameter estimation problem. Its performance is analyzed using estimation-theoretic bounds. Some representative results from T2 brain mapping are also presented to illustrate the performance of the proposed method for accelerating parameter mapping.

Original languageEnglish (US)
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Pages1-4
Number of pages4
DOIs
StatePublished - 2013
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: Apr 7 2013Apr 11 2013

Publication series

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

Other

Other2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period4/7/134/11/13

Keywords

  • model-based reconstruction
  • parameter estimation
  • parameter mapping
  • sparsity

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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