Low-resolution Prior Equilibrium Network for CT Reconstruction

• URL: http://arxiv.org/abs/2401.15663v2
• Date: Thu, 18 Apr 2024 10:48:15 GMT
• Title: Low-resolution Prior Equilibrium Network for CT Reconstruction
• Authors: Yijie Yang, Qifeng Gao, Yuping Duan,
• Abstract summary: We present a novel deep learning-based CT reconstruction model, where the low-resolution image is introduced to obtain an effective regularization term for improving the networks robustness. Experimental results on both sparse-view and limited-angle reconstruction problems are provided, demonstrating that our end-to-end low-resolution prior equilibrium model outperforms other state-of-the-art methods in terms of noise reduction, contrast-to-noise ratio, and preservation of edge details.
• Score: 3.5639148953570836
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The unrolling method has been investigated for learning variational models in X-ray computed tomography. However, it has been observed that directly unrolling the regularization model through gradient descent does not produce satisfactory results. In this paper, we present a novel deep learning-based CT reconstruction model, where the low-resolution image is introduced to obtain an effective regularization term for improving the network`s robustness. Our approach involves constructing the backbone network architecture by algorithm unrolling that is realized using the deep equilibrium architecture. We theoretically discuss the convergence of the proposed low-resolution prior equilibrium model and provide the conditions to guarantee convergence. Experimental results on both sparse-view and limited-angle reconstruction problems are provided, demonstrating that our end-to-end low-resolution prior equilibrium model outperforms other state-of-the-art methods in terms of noise reduction, contrast-to-noise ratio, and preservation of edge details.

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