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Deep Learning for Rapid MRI

        I have been actively exploring the direction of deep learning for rapid MRI, particularly rapid quantitative MRI, together with my collaborator Dr. Fang Liu, who has pioneered the use of Artificial Intelligence for many aspects of MRI research. We recently proposed a sampling-augmented deep image reconstruction strategy for improved reconstruction performance and robustness against sampling discrepancy. This is achieved by extensively varying undersampling scheme during network training, so that the trained network can recognized various undersampling artifact patterns and thus enables better removal of undersampling artifact during the reconstruction process. It turns out that this framework is well-suited for golden-angle radial image reconstruction since golden-angle imaging features non-repeating sampling, which can be treated as a nature augmentation strategy. We also proposed a framework for model-based MR parameter mapping based on deep-learning, which enables efficient, highly-accelerated and accurate parameter mapping as demonstrated in our first feasibility study. This framework is currently under extension for rapid quantification of other MR parameters with the incorporation of corresponding signal models. We have recently written a paper together to review the application of deep learning for rapid quantitative MRI.
      Magn Reson Med. 2019 Nov;82(5):1890-1904. doi: 10.1002/mrm.27827
      Magn Reson Med. 2019 Jul;82(1):174-188. doi: 10.1002/mrm.27707
       NMR Biomed. 2020 Oct 15;e4416. doi: 10.1002/nbm.4416. Online ahead of print
      Magn Reson Imaging. 2020 Sep 25;74:152-160

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