Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations

Dongdong Chen, Mike E. Davies, Mohammad Golbabaee*

*Corresponding author for this work

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

9 Citations (Scopus)

Abstract

Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies proposed for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we propose PGD-Net, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism. The PGD-Net adopts a compact neural proximal model for de-aliasing and quantitative inference, that can be flexibly trained on scarce MRF training datasets. Our numerical experiments show that the PGD-Net can achieve a superior quantitative inference accuracy, much smaller storage requirement, and a comparable runtime to the recent deep learning MRF baselines, while being much faster than the dictionary matching schemes. Code has been released at https://github.com/edongdongchen/PGD-Net.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer
Pages13-22
Number of pages10
ISBN (Print)9783030597122
DOIs
Publication statusPublished - 29 Sept 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention 2020 - Virtual, Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12262 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention 2020
Abbreviated titleMICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/208/10/20

Keywords

  • Compressed Sensing
  • Deep learning
  • Learned proximal gradient descent
  • Magnetic resonance fingerprinting

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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