End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA.
coronary MRA
deep learning nonrigid motion correction
deep learning reconstruction
free-breathing cardiac MRI
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
revised:
22
03
2021
received:
20
01
2021
accepted:
29
04
2021
pubmed:
8
6
2021
medline:
3
8
2021
entrez:
7
6
2021
Statut:
ppublish
Résumé
To develop an end-to-end deep learning technique for nonrigid motion-corrected (MoCo) reconstruction of ninefold undersampled free-breathing whole-heart coronary MRA (CMRA). A novel deep learning framework was developed consisting of a diffeomorphic registration network and a motion-informed model-based deep learning (MoDL) reconstruction network. The registration network receives as input highly undersampled (~22×) respiratory-resolved images and outputs 3D nonrigid respiratory motion fields between the images. The motion-informed MoDL performs MoCo reconstruction from undersampled data using the predicted motion fields. The whole deep learning framework, termed as MoCo-MoDL, was trained end-to-end in a supervised manner for simultaneous 3D nonrigid motion estimation and MoCo reconstruction. MoCo-MoDL was compared with a state-of-the-art nonrigid MoCo CMRA reconstruction technique in 15 retrospectively undersampled datasets and 9 prospectively undersampled acquisitions. The acquisition time for ninefold accelerated CMRA was ~2.5 min. The reconstruction time was ~22 s for the proposed MoCo-MoDL and ~35 min for the conventional approach. MoCo-MoDL achieved higher peak SNR (27.86 ± 3.00 vs. 26.71 ± 2.79; P < .05) and structural similarity (0.78 ± 0.06 vs. 0.75 ± 0.06; P < .05) than the conventional approach. Similar vessel length and visual image quality score were obtained with the 2 methods, whereas improved vessel sharpness was observed with MoCo-MoDL. An end-to-end deep learning approach was introduced for simultaneous nonrigid motion estimation and MoCo reconstruction of highly undersampled free-breathing whole-heart CMRA. The rapid free-breathing CMRA acquisition together with the fast reconstruction of the proposed approach promises easy integration into clinical workflow.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1983-1996Subventions
Organisme : British Heart Foundation
ID : RG/20/1/34802
Pays : United Kingdom
Organisme : British Heart Foundation
ID : FS/CRTF/20/24011
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/L009676/1
Pays : United Kingdom
Organisme : British Heart Foundation
ID : PG/18/59/33955
Pays : United Kingdom
Informations de copyright
© 2021 International Society for Magnetic Resonance in Medicine.
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