Accelerated 4D Respiratory Motion-Resolved Cardiac MRI with a Model-Based Variational Network

Haikun Qi*, Niccolo Fuin, Thomas Kuestner, René Botnar, Claudia Prieto

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)

Abstract

Respiratory motion and long scan times remain major challenges in free-breathing 3D cardiac MRI. Respiratory motion-resolved approaches have been proposed by binning the acquired data to different respiratory motion states. After inter-bin motion estimation, motion-compensated reconstruction can be obtained. However, respiratory bins from accelerated acquisitions are highly undersampled and have different undersampling patterns depending on the subject-specific respiratory motion. Remaining undersampling artifacts in the bin images can influence the accuracy of the motion estimation. We propose a model-based variational network (VN) which reconstructs motion-resolved images jointly by exploiting shared information between respiratory bins. In each stage of VN, conjugate gradient is adopted to enforce data-consistency (CG-VN), achieving better enforcement of data consistency per stage than the classic VN with proximal gradient descent step (GD-VN), translating to faster convergence and better reconstruction performance. We compare the performance of CG-VN and GD-VN for reconstruction of respiratory motion-resolved images for two different cardiac MR sequences. Our results show that CG-VN with less stages outperforms GD-VN by achieving higher PSNR and better generalization on prospectively undersampled data. The proposed motion-resolved CG-VN provides consistently good reconstruction quality for all motion states with varying undersampling patterns by taking advantage of redundancies among motion bins.

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 Science and Business Media Deutschland GmbH
Pages427-435
Number of pages9
ISBN (Print)9783030597245
DOIs
Publication statusPublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - 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)
Volume12266 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/20208/10/2020

Keywords

  • Model-based Deep-learning
  • Motion-resolved MRI reconstruction
  • Variational network

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