A Unifying Multi-sampling-ratio CS-MRI Framework With Two-grid-cycle Correction and Geometric Prior Distillation

• URL: http://arxiv.org/abs/2205.07062v1
• Date: Sat, 14 May 2022 13:36:27 GMT
• Title: A Unifying Multi-sampling-ratio CS-MRI Framework With Two-grid-cycle Correction and Geometric Prior Distillation
• Authors: Xiaohong Fan, Yin Yang, Ke Chen, Jianping Zhang, Ke Dong
• Abstract summary: We propose a unifying deep unfolding multi-sampling-ratio CS-MRI framework, by merging advantages of model-based and deep learning-based methods. Inspired by multigrid algorithm, we first embed the CS-MRI-based optimization algorithm into correction-distillation scheme. We employ a condition module to learn adaptively step-length and noise level from compressive sampling ratio in every stage.
• Score: 7.643154460109723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: CS is an efficient method to accelerate the acquisition of MR images from under-sampled k-space data. Although existing deep learning CS-MRI methods have achieved considerably impressive performance, explainability and generalizability continue to be challenging for such methods since most of them are not flexible enough to handle multi-sampling-ratio reconstruction assignments, often the transition from mathematical analysis to network design not always natural enough. In this work, to tackle explainability and generalizability, we propose a unifying deep unfolding multi-sampling-ratio CS-MRI framework, by merging advantages of model-based and deep learning-based methods. The combined approach offers more generalizability than previous works whereas deep learning gains explainability through a geometric prior module. Inspired by multigrid algorithm, we first embed the CS-MRI-based optimization algorithm into correction-distillation scheme that consists of three ingredients: pre-relaxation module, correction module and geometric prior distillation module. Furthermore, we employ a condition module to learn adaptively step-length and noise level from compressive sampling ratio in every stage, which enables the proposed framework to jointly train multi-ratio tasks through a single model. The proposed model can not only compensate the lost contextual information of reconstructed image which is refined from low frequency error in geometric characteristic k-space, but also integrate the theoretical guarantee of model-based methods and the superior reconstruction performances of deep learning-based methods. All physical-model parameters are learnable, and numerical experiments show that our framework outperforms state-of-the-art methods in terms of qualitative and quantitative evaluations.

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