RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equations

• URL: http://arxiv.org/abs/2409.13930v1
• Date: Fri, 20 Sep 2024 22:33:36 GMT
• Title: RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equations
• Authors: Jiaqi Guo, Santiago Lopez-Tapia, Wing Shun Li, Yunnan Wu, Marcelo Carignano, Vadim Backman, Vinayak P. Dravid, Aggelos K. Katsaggelos,
• Abstract summary: We propose Residual Null-Space Diffusion Differential Equations (RN-SDEs) RN-SDEs are a variant of diffusion models that characterize the diffusion process with mean-reverting differential equations. We show that by leveraging learned Mean-Reverting SDEs as a prior, RN-SDEs can restore high-quality images from severe degradation and achieve state-of-the-art performance in most LACT tasks.
• Score: 11.83356524790835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computed tomography is a widely used imaging modality with applications ranging from medical imaging to material analysis. One major challenge arises from the lack of scanning information at certain angles, leading to distorted CT images with artifacts. This results in an ill-posed problem known as the Limited Angle Computed Tomography (LACT) reconstruction problem. To address this problem, we propose Residual Null-Space Diffusion Stochastic Differential Equations (RN-SDEs), which are a variant of diffusion models that characterize the diffusion process with mean-reverting (MR) stochastic differential equations. To demonstrate the generalizability of RN-SDEs, our experiments are conducted on two different LACT datasets, i.e., ChromSTEM and C4KC-KiTS. Through extensive experiments, we show that by leveraging learned Mean-Reverting SDEs as a prior and emphasizing data consistency using Range-Null Space Decomposition (RNSD) based rectification, RN-SDEs can restore high-quality images from severe degradation and achieve state-of-the-art performance in most LACT tasks. Additionally, we present a quantitative comparison of computational complexity and runtime efficiency, highlighting the superior effectiveness of our proposed approach.

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