Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to solving the problem of long MRI imaging time. Recently, deep generative models have exhibited great potentials in algorithm robustness and usage flexibility. Nevertheless, none of existing schemes can be learned or employed to the k-space measurement directly. Furthermore, how do the deep generative models work well in hybrid domain is also worth being investigated. In this work, by taking advantage of the deep energy-based models, we propose a k-space and image domain collaborative generative model to comprehensively estimate the MR data from under-sampled measurement. Experimental comparisons with the state-of-the-arts demonstrated that the proposed hybrid method has less error in reconstruction accuracy and is more stable under different acceleration factors
@article{arxiv.2203.10776,
title = {K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction},
author = {Zongjiang Tu and Chen Jiang and Yu Guan and Shanshan Wang and Jijun Liu and Qiegen Liu and Dong Liang},
journal= {arXiv preprint arXiv:2203.10776},
year = {2022}
}
Comments
25 pages,11 figures. arXiv admin note: text overlap with arXiv:2109.03237