Back to Search
Start Over
End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA.
- Source :
-
Magnetic resonance in medicine [Magn Reson Med] 2021 Oct; Vol. 86 (4), pp. 1983-1996. Date of Electronic Publication: 2021 Jun 06. - Publication Year :
- 2021
-
Abstract
- Purpose: 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).<br />Methods: 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.<br />Results: 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.<br />Conclusion: 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.<br /> (© 2021 International Society for Magnetic Resonance in Medicine.)
Details
- Language :
- English
- ISSN :
- 1522-2594
- Volume :
- 86
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Magnetic resonance in medicine
- Publication Type :
- Academic Journal
- Accession number :
- 34096095
- Full Text :
- https://doi.org/10.1002/mrm.28851