Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909873236606976 |
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| author | Li, Qiang Safari, Mojtaba Wang, Shansong Xie, Huiqiao Ding, Jie Wang, Tonghe Yang, Xiaofeng |
| author_facet | Li, Qiang Safari, Mojtaba Wang, Shansong Xie, Huiqiao Ding, Jie Wang, Tonghe Yang, Xiaofeng |
| contents | Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denoising approaches often require many diffusion steps, limiting real-time applicability. We propose a Regularization-Enhanced Efficient Diffusion Probabilistic Model (RE-EDPM), a rapid and high-fidelity LDCT denoising framework that integrates a residual shifting mechanism to align low-dose and full-dose distributions and performs only four reverse diffusion steps using a Swin-based U-Net backbone. A composite loss combining pixel reconstruction, perceptual similarity (LPIPS), and total variation (TV) regularization effectively suppresses spatially varying noise while preserving anatomical structures. RE-EDPM was evaluated on a public LDCT benchmark across dose levels and anatomical sites. On 10 percent dose chest and 25 percent dose abdominal scans, it achieved SSIM = 0.879 (0.068), PSNR = 31.60 (2.52) dB, VIFp = 0.366 (0.121) for chest, and SSIM = 0.971 (0.000), PSNR = 36.69 (2.54) dB, VIFp = 0.510 (0.007) for abdomen. Visual and statistical analyses, including ablation and Wilcoxon signed-rank tests (p < 0.05), confirm significant contributions from residual shifting and regularization terms. RE-EDPM processes two 512x512 slices in about 0.25 s on modern GPUs, supporting near real-time clinical use. The proposed framework achieves an optimal balance between noise suppression and anatomical fidelity, offering an efficient solution for LDCT restoration and broader medical image enhancement tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_23859 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model Li, Qiang Safari, Mojtaba Wang, Shansong Xie, Huiqiao Ding, Jie Wang, Tonghe Yang, Xiaofeng Medical Physics Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denoising approaches often require many diffusion steps, limiting real-time applicability. We propose a Regularization-Enhanced Efficient Diffusion Probabilistic Model (RE-EDPM), a rapid and high-fidelity LDCT denoising framework that integrates a residual shifting mechanism to align low-dose and full-dose distributions and performs only four reverse diffusion steps using a Swin-based U-Net backbone. A composite loss combining pixel reconstruction, perceptual similarity (LPIPS), and total variation (TV) regularization effectively suppresses spatially varying noise while preserving anatomical structures. RE-EDPM was evaluated on a public LDCT benchmark across dose levels and anatomical sites. On 10 percent dose chest and 25 percent dose abdominal scans, it achieved SSIM = 0.879 (0.068), PSNR = 31.60 (2.52) dB, VIFp = 0.366 (0.121) for chest, and SSIM = 0.971 (0.000), PSNR = 36.69 (2.54) dB, VIFp = 0.510 (0.007) for abdomen. Visual and statistical analyses, including ablation and Wilcoxon signed-rank tests (p < 0.05), confirm significant contributions from residual shifting and regularization terms. RE-EDPM processes two 512x512 slices in about 0.25 s on modern GPUs, supporting near real-time clinical use. The proposed framework achieves an optimal balance between noise suppression and anatomical fidelity, offering an efficient solution for LDCT restoration and broader medical image enhancement tasks. |
| title | Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2510.23859 |