A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

• URL: http://arxiv.org/abs/2204.03804v1
• Date: Fri, 8 Apr 2022 01:35:19 GMT
• Title: A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis
• Authors: Wanyu Bian, Qingchao Zhang, Xiaojing Ye, Yunmei Chen
• Abstract summary: We propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI. The output of our model includes reconstructed images of the source modalities and high-quality image synthesized in the target modality.
• Score: 4.056490719080639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source modalities as inputs. The output of our model includes reconstructed images of the source modalities and high-quality image synthesized in the target modality. Our proposed model is formulated as a variational problem that leverages several learnable modality-specific feature extractors and a multimodal synthesis module. We propose a learnable optimization algorithm to solve this model, which induces a multi-phase network whose parameters can be trained using multi-modal MRI data. Moreover, a bilevel-optimization framework is employed for robust parameter training. We demonstrate the effectiveness of our approach using extensive numerical experiments.

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