SRAKI-RNN: Accelerated MRI with scan-specific recurrent neural networks using densely connected blocks

Seyed Amir Hossein Hosseini, Chi Zhang, Kâmil Ugurbil, Steen Moeller, Mehmet Akcąkaya

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

6 Scopus citations

Abstract

This study aims to improve upon Self-consistent Robust Artificial-neural-networks for k-space Interpolation (sRAKI), which is a deep learning-based parallel imaging technique for accelerated MRI reconstruction. The proposed technique, called sRAKI-RNN, combines the calibration and reconstruction phases of sRAKI into a single step that jointly learns the self-consistency rule and performs iterative reconstruction using recurrent neural networks (RNN). Similar to sRAKI, sRAKI-RNN supports arbitrary undersampling patterns and is a databasefree technique that is trained on autocalibrating signal (ACS) data from the same scan. Densely connected blocks are used in each iteration of the RNN to improve the convergence during the learning phase. sRAKI-RNN was evaluated on targeted right coronary artery (RCA) MRI. The results indicate that sRAKI-RNN further improves the noise resilience of sRAKI in a shorter running time and also considerably outperforms its linear counterpart, SPIRiT, in suppressing reconstruction noise.

Original languageEnglish (US)
Title of host publicationWavelets and Sparsity XVIII
EditorsDimitri Van De Ville, Dimitri Van De Ville, Manos Papadakis, Yue M. Lu
PublisherSPIE
ISBN (Electronic)9781510629691
DOIs
StatePublished - 2019
EventWavelets and Sparsity XVIII 2019 - San Diego, United States
Duration: Aug 13 2019Aug 15 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11138
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceWavelets and Sparsity XVIII 2019
Country/TerritoryUnited States
CitySan Diego
Period8/13/198/15/19

Bibliographical note

Funding Information:
This work was supported by NIH, Grant numbers: NSF, Grant number: CAREER CCF-1651825

Publisher Copyright:
© 2019 SPIE.

Keywords

  • Accelerated MRI
  • coronary MRI
  • deep learning
  • dense neural networks
  • parallel imaging
  • recurrent neural networks

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