End-to-End Deep Learning of Non-rigid Groupwise Registration and Reconstruction of Dynamic MRI.
Authors
Yang, Junwei
Küstner, Thomas
Hu, Peng
Liò, Pietro
Qi, Haikun
Publication Date
2022Journal Title
Front Cardiovasc Med
ISSN
2297-055X
Publisher
Frontiers Media SA
Volume
9
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Yang, J., Küstner, T., Hu, P., Liò, P., & Qi, H. (2022). End-to-End Deep Learning of Non-rigid Groupwise Registration and Reconstruction of Dynamic MRI.. Front Cardiovasc Med, 9 https://doi.org/10.3389/fcvm.2022.880186
Abstract
Temporal correlation has been exploited for accelerated dynamic MRI reconstruction. Some methods have modeled inter-frame motion into the reconstruction process to produce temporally aligned image series and higher reconstruction quality. However, traditional motion-compensated approaches requiring iterative optimization of registration and reconstruction are time-consuming, while most deep learning-based methods neglect motion in the reconstruction process. We propose an unrolled deep learning framework with each iteration consisting of a groupwise diffeomorphic registration network (GRN) and a motion-augmented reconstruction network. Specifically, the whole dynamic sequence is registered at once to an implicit template which is used to generate a new set of dynamic images to efficiently exploit the full temporal information of the acquired data via the GRN. The generated dynamic sequence is then incorporated into the reconstruction network to augment the reconstruction performance. The registration and reconstruction networks are optimized in an end-to-end fashion for simultaneous motion estimation and reconstruction of dynamic images. The effectiveness of the proposed method is validated in highly accelerated cardiac cine MRI by comparing with other state-of-the-art approaches.
Keywords
Cardiovascular Medicine, dynamic MR imaging, deep learning, reconstruction, registration, multi-task learning
Identifiers
External DOI: https://doi.org/10.3389/fcvm.2022.880186
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337062
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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