Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction

• URL: http://arxiv.org/abs/2403.09355v1
• Date: Thu, 14 Mar 2024 12:58:28 GMT
• Title: Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction
• Authors: Hanyu Chen, Zhixiu Hao, Lin Guo, Liying Xiao,
• Abstract summary: This study introduces a novel Cascaded Diffusion with Discrepancy Mitigation framework. It includes the low-quality image generation in latent space and the high-quality image generation in pixel space. It minimizes computational costs by moving some inference steps from pixel space to latent space.
• Score: 4.227116189483428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse-view Computed Tomography (CT) image reconstruction is a promising approach to reduce radiation exposure, but it inevitably leads to image degradation. Although diffusion model-based approaches are computationally expensive and suffer from the training-sampling discrepancy, they provide a potential solution to the problem. This study introduces a novel Cascaded Diffusion with Discrepancy Mitigation (CDDM) framework, including the low-quality image generation in latent space and the high-quality image generation in pixel space which contains data consistency and discrepancy mitigation in a one-step reconstruction process. The cascaded framework minimizes computational costs by moving some inference steps from pixel space to latent space. The discrepancy mitigation technique addresses the training-sampling gap induced by data consistency, ensuring the data distribution is close to the original manifold. A specialized Alternating Direction Method of Multipliers (ADMM) is employed to process image gradients in separate directions, offering a more targeted approach to regularization. Experimental results across two datasets demonstrate CDDM's superior performance in high-quality image generation with clearer boundaries compared to existing methods, highlighting the framework's computational efficiency.

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