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Scan-Specific Accelerated Mri Reconstruction Using Recurrent Neural Networks in A Regularized Self-Consistent Framework

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

Abstract

Long scan duration remains a challenge for high-resolution MRI. Several accelerated imaging strategies have been proposed based on deep learning (DL) that require databases of fully-sampled images for training. However, scan-specific training is desired where individual variability is important, e.g. in free-breathing cardiac MRI, or where such datasets are not available due to scan time constraints for acquiring fully-sampled data. Building on our earlier method called Self-consistent Robust Artificial-neural-networks for k-space Interpolation (sRAKI), we propose a scan-specific DL reconstruction method based on recurrent neural networks that combines training and reconstruction phases of sRAKI. We use self-consistency among coils in k-space and regularization in arbitrary domains, as well as consistency with acquired data, in each iteration of the recurrent network. Results on knee MRI show that this method improves upon parallel imaging and compressed sensing methods.

Original languageEnglish (US)
Title of host publication2020 IEEE 17th International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728174013
DOIs
StatePublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020 - Virtual, Online, United States
Duration: Apr 4 2020Apr 4 2020

Publication series

NameISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings

Conference

Conference17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020
Country/TerritoryUnited States
CityVirtual, Online
Period4/4/204/4/20

Bibliographical note

Funding Information:
This work was supported by NIH U01EB025144, P41EB027061; NSF CAREER CCF-1651825. Knee MRI data were obtained from the NYU fastMRI initiative database [23]. NYU fastMRI database was acquired with the relevant institutional review board approvals as detailed in [23]. A listing of NYU fastMRI investigators, subject to updates, can be found at fastmri.med.nyu.edu.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Parallel imaging
  • compressed sensing
  • deep learning
  • image reconstruction.
  • machine learning
  • neural networks

Center for Magnetic Resonance Research (CMRR) tags

  • IRP

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