GA-HQS: MRI reconstruction via a generically accelerated unfolding approach

• URL: http://arxiv.org/abs/2304.02883v1
• Date: Thu, 6 Apr 2023 06:21:18 GMT
• Title: GA-HQS: MRI reconstruction via a generically accelerated unfolding approach
• Authors: Jiawei Jiang, Yuchao Feng, Honghui Xu, Wanjun Chen, Jianwei Zheng
• Abstract summary: We propose a Generically Accelerated Half-Quadratic Splitting (GA-HQS) algorithm that incorporates second-order gradient information and pyramid attention modules for the delicate fusion of inputs at the pixel level. Our method surpasses previous ones on single-coil MRI acceleration tasks.
• Score: 14.988694941405575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep unfolding networks (DUNs) are the foremost methods in the realm of compressed sensing MRI, as they can employ learnable networks to facilitate interpretable forward-inference operators. However, several daunting issues still exist, including the heavy dependency on the first-order optimization algorithms, the insufficient information fusion mechanisms, and the limitation of capturing long-range relationships. To address the issues, we propose a Generically Accelerated Half-Quadratic Splitting (GA-HQS) algorithm that incorporates second-order gradient information and pyramid attention modules for the delicate fusion of inputs at the pixel level. Moreover, a multi-scale split transformer is also designed to enhance the global feature representation. Comprehensive experiments demonstrate that our method surpasses previous ones on single-coil MRI acceleration tasks.

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