Rethinking the optimization process for self-supervised model-driven MRI reconstruction

• URL: http://arxiv.org/abs/2203.09724v1
• Date: Fri, 18 Mar 2022 03:41:36 GMT
• Title: Rethinking the optimization process for self-supervised model-driven MRI reconstruction
• Authors: Weijian Huang, Cheng Li, Wenxin Fan, Yongjin Zhou, Qiegen Liu, Hairong Zheng and Shanshan Wang
• Abstract summary: K2Calibrate is a K-space adaptation strategy for self-supervised model-driven MR reconstruction optimization. It can reduce the network's reconstruction deterioration caused by statistically dependent noise. It achieves better results than five state-of-the-art methods.
• Score: 16.5013498806588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction. Recently, various supervised deep learning-based MRI reconstruction methods have been developed. Despite the achieved promising performances, these methods require fully sampled reference data, the acquisition of which is resource-intensive and time-consuming. Self-supervised learning has emerged as a promising solution to alleviate the reliance on fully sampled datasets. However, existing self-supervised methods suffer from reconstruction errors due to the insufficient constraint enforced on the non-sampled data points and the error accumulation happened alongside the iterative image reconstruction process for model-driven deep learning reconstrutions. To address these challenges, we propose K2Calibrate, a K-space adaptation strategy for self-supervised model-driven MR reconstruction optimization. By iteratively calibrating the learned measurements, K2Calibrate can reduce the network's reconstruction deterioration caused by statistically dependent noise. Extensive experiments have been conducted on the open-source dataset FastMRI, and K2Calibrate achieves better results than five state-of-the-art methods. The proposed K2Calibrate is plug-and-play and can be easily integrated with different model-driven deep learning reconstruction methods.

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