Reducing the Complexity of Model-Based MRI Reconstructions via Sparsification

Alex Gutierrez, Michael Mullen, Di Xiao, Albert Jang, Taylor Froelich, Michael Garwood, Jarvis Haupt

Research output: Contribution to journalArticlepeer-review

Abstract

Model-based reconstruction methods have emerged as a powerful alternative to classical Fourier-based MRI techniques, largely because of their ability to explicitly model (and therefore, potentially overcome) moderate field inhomogeneities, streamline reconstruction from non-Cartesian sampling, and even allow for the use of custom designed non-Fourier encoding methods. Their application in such scenarios, however, often comes with a substantial increase in computational cost, owing to the fact that the corresponding forward model in such settings no longer possesses a direct Fourier Transform based implementation. This paper introduces an algorithmic framework designed to reduce the computational burden associated with model-based MRI reconstruction tasks. The key innovation is the strategic sparsification of the corresponding forward operators for these models, giving rise to approximations of the forward models (and their adjoints) that admit low computational complexity application. This enables overall a reduced computational complexity application of popular iterative first-order reconstruction methods for these reconstruction tasks. Computational results obtained on both synthetic and experimental data illustrate the viability and efficiency of the approach.

Original languageEnglish (US)
Article number9432799
Pages (from-to)2477-2486
Number of pages10
JournalIEEE Transactions on Medical Imaging
Volume40
Issue number9
DOIs
StatePublished - Sep 1 2021

Bibliographical note

Funding Information:
Manuscript received April 14, 2021; accepted May 9, 2021. Date of publication May 17, 2021; date of current version August 31, 2021. This work was supported in part by the National Institutes of Health Grant U01 EB025153 and Grant P41 EB025144, in part by the National Science Foundation (NSF) under Award CCF-1217751, and in part by the Defense Advanced Research Projects Agency (DARPA) Young Faculty Award under Grant N66001-14-1-4047. (Corresponding author: Jarvis Haupt.) This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by Institutional Review Board (IRB), and performed in line with the Declaration of Helsinki.

Publisher Copyright:
© 1982-2012 IEEE.

Keywords

  • MRI
  • frequency-swept pulses
  • model-based image reconstruction
  • nonlinear field
  • operator approximation
  • sparsification

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

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