Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI

• URL: http://arxiv.org/abs/2305.02509v1
• Date: Thu, 4 May 2023 02:40:42 GMT
• Title: Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI
• Authors: Zhuo-Xu Cui, Congcong Liu, Chentao Cao, Yuanyuan Liu, Jing Cheng, Qingyong Zhu, Yanjie Zhu, Haifeng Wang, Dong Liang
• Abstract summary: We introduce a novel meta-learning approach that employs a teacher-student mechanism. An optimal-transport-driven teacher learns the degradation process from high-field to low-field MR images. Then, a score-based student solves the inverse problem of reconstructing a high-field-like MR image from a low-field MRI.
• Score: 22.024215676838185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic resonance imaging (MRI) is known to have reduced signal-to-noise ratios (SNR) at lower field strengths, leading to signal degradation when producing a low-field MRI image from a high-field one. Therefore, reconstructing a high-field-like image from a low-field MRI is a complex problem due to the ill-posed nature of the task. Additionally, obtaining paired low-field and high-field MR images is often not practical. We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable. To overcome these challenges, we introduce a novel meta-learning approach that employs a teacher-student mechanism. Firstly, an optimal-transport-driven teacher learns the degradation process from high-field to low-field MR images and generates pseudo-paired high-field and low-field MRI images. Then, a score-based student solves the inverse problem of reconstructing a high-field-like MR image from a low-field MRI within the framework of iterative regularization, by learning the joint distribution of pseudo-paired images to act as a regularizer. Experimental results on real low-field MRI data demonstrate that our proposed method outperforms state-of-the-art unpaired learning methods.

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