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RUN‐UP: Accelerated multishot diffusion‐weighted MRI reconstruction using an unrolled network with U‐Net as priors
Author(s) -
Hu Yuxin,
Xu Yunyingying,
Tian Qiyuan,
Chen Feiyu,
Shi Xinwei,
Moran Catherine J.,
Daniel Bruce L.,
Hargreaves Brian A.
Publication year - 2021
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28446
Subject(s) - computer science , mean squared error , artificial intelligence , iterative reconstruction , similarity (geometry) , noise (video) , aliasing , signal to noise ratio (imaging) , diffusion mri , pipeline (software) , image quality , algorithm , root mean square , computer vision , pattern recognition (psychology) , mathematics , magnetic resonance imaging , image (mathematics) , physics , undersampling , statistics , medicine , radiology , telecommunications , quantum mechanics , programming language
Purpose To accelerate and improve multishot diffusion‐weighted MRI reconstruction using deep learning. Methods An unrolled pipeline containing recurrences of model‐based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot‐to‐shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single‐direction data as input. In vivo brain and breast experiments were performed for evaluation. Results The proposed method achieves a reconstruction time of 0.1 second per image, over 100‐fold faster than a shot locally low‐rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal‐to‐noise ratio of 35.3 dB, a normalized root‐mean‐square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low‐rank reconstruction (2.9 dB higher peak signal‐to‐noise ratio, 29% lower normalized root‐mean‐square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion‐weighted imaging, and fine‐tuning further reduces aliasing artifacts. Conclusion A proposed data‐driven approach enables almost real‐time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.