FCDM: Sparse-view Sinogram Inpainting with Frequency Domain Convolution Enhanced Diffusion Models

• URL: http://arxiv.org/abs/2409.06714v2
• Date: Fri, 22 Nov 2024 21:17:56 GMT
• Title: FCDM: Sparse-view Sinogram Inpainting with Frequency Domain Convolution Enhanced Diffusion Models
• Authors: Jiaze E, Srutarshi Banerjee, Tekin Bicer, Guannan Wang, Yanfu Zhang, Bin Ren,
• Abstract summary: We introduce a novel diffusion-based inpainting framework tailored for sinogram data. FCDM significantly outperforms existing methods, achieving SSIM over 0.95 and PSNR above 30 dB, with improvements of up to 33% in SSIM and 29% in PSNR compared to baselines.
• Score: 14.043383277622874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computed tomography (CT) is an imaging technique that uses X-ray projections from multiple rotation angles to create detailed cross-sectional images, widely used in industrial inspection and medical diagnostics. Reducing the projection data in CT scans is often necessary to decrease radiation exposure, scanning time, and computational costs. However, this reduction makes accurate image reconstruction challenging due to the incomplete sinogram. Existing RGB inpainting models struggle with severe feature overlap, while current sinogram-specific models fail to employ efficient feature extraction methods that account for the physical principles underlying the sinogram generation process. To tackle these challenges, we introduce the Frequency Convolution Diffusion Model (FCDM), a novel diffusion-based inpainting framework tailored for sinogram data. FCDM leverages frequency-domain convolutions to capture global and fine-grained structural features, effectively disentangling overlapping components across projection angles. Additionally, we propose a custom loss function that incorporates unique sinogram properties of total absorption consistency and frequency-domain consistency. Extensive experiments on synthetic and real-world datasets demonstrate that FCDM significantly outperforms existing methods, achieving SSIM over 0.95 and PSNR above 30 dB, with improvements of up to 33% in SSIM and 29% in PSNR compared to baselines.

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