Qingyong Zhu, Bei Liu, Zhuo-Xu Cui, Chentao Cao, Xiaomeng Yan, Yuanyuan Liu, Jing Cheng, Yihang Zhou, Yanjie Zhu, Haifeng Wang, Hongwu Zeng, Dong Liang
Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, eliminating the explicit training procedure but often struggling to remove noise and artifacts under significant degradation. This work introduces a novel self-supervised accelerated parallel MRI approach called PEARL, leveraging a multiple-stream joint deep decoder with two cross-fusion schemes to accurately reconstruct one or more target images from compressively sampled k-space...
December 26, 2023: IEEE Journal of Biomedical and Health Informatics