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ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Cardiovascular Imaging
Volume 11 - 2024 |
doi: 10.3389/fcvm.2024.1408351
This article is part of the Research Topic Generative Artificial Intelligence in Cardiac Imaging and Cardiovascular Medicine View all articles
A Hybrid Deep Image Prior and Compressed Sensing Reconstruction Method for Highly Accelerated 3D Coronary Magnetic Resonance Angiography
Provisionally accepted- 1 National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- 2 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, Guangdong Province, China
- 3 Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, Shanghai Municipality, China
High-resolution whole-heart coronary magnetic resonance angiography (CMRA) often suffers from unreasonably long scan times, rendering imaging acceleration highly desirable. Traditional reconstruction methods used in CMRA rely on either hand-crafted priors or supervised learning models. Although the latter often yield superior reconstruction quality, they require a large amount of training data and memory resources, and may encounter generalization issues when dealing with outof-distribution datasets. To address these challenges, we introduce an unsupervised reconstruction method that combines deep image prior (DIP) with compressed sensing (CS) to accelerate 3D CMRA. This method incorporates a slice-by-slice DIP reconstruction and 3D total variation (TV) regularization, enabling high-quality reconstruction under a significant acceleration while enforcing continuity in the slice direction. We evaluated our method by comparing it to iterative SENSE, CS-TV, CS-wavelet, and other DIP-based variants, using both retrospectively and prospectively undersampled datasets. The results demonstrate the superiority of our 3D DIP-CS approach, which improved the reconstruction accuracy relative to the other approaches across both datasets. Ablation studies further reveal the benefits of combining DIP with 3D TV regularization, which leads to significant improvements of image quality over pure DIP-based methods. Evaluation of vessel sharpness and image quality scores shows that DIP-CS improves the quality of reformatted coronary arteries. The proposed method enables scan-specific reconstruction of high-quality 3D CMRA from a five-minute acquisition, without relying on fully-sampled training data or placing a heavy burden on memory resources.
Keywords: coronary magnetic resonance angiography, Deep image prior, compressed sensing, unsupervised learning, image reconstruction
Received: 28 Mar 2024; Accepted: 27 Aug 2024.
Copyright: © 2024 Xue, Zhu, Yang, Gao, Peng, Zou, Jin and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Chenxi Hu, National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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