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Existing denoising approaches often require extensive diffusion steps that impede real\u2011time clinical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address this challenge, we propose a regularization\u2010enhanced efficient diffusion probabilistic model (RE\u2010EDPM), a rapid and high\u2010fidelity denoising framework that incorporates residual guidance between low dose and full dose CT scans and employs hybrid perceptual and total variation (TV) regularization to preserve anatomical fidelity and diagnostic quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>RE\u2010EDPM incorporates a residual\u2010shifting mechanism into the forward diffusion process to better align the LDCT and FDCT distributions, followed by four reverse diffusion steps using a Swin\u2010based U\u2010Net backbone. A composite loss function combining pixel\u2010level reconstruction, perceptual similarity (LPIPS), and spatially TV is used to suppress spatially varying noise while preserving fine structural details. We evaluated RE\u2010EDPM on a public LDCT benchmark dataset across different dose levels and anatomical sites. Quantitative performance was assessed using structural similarity index measure (SSIM), peak signal\u2010to\u2010noise ratio (PSNR), and visual information fidelity (VIFp), and RE\u2010EDPM was compared against several state\u2010of\u2010the\u2010art methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      On public LDCT benchmarks at 10% dose for chest and 25% dose for abdomen, RE\u2010EDPM achieved SSIM of 0.879\u00a0\u00b1\u00a00.068, PSNR of 31.60\u00a0\u00b1\u00a02.52\u00a0dB, and VIFp of 0.366\u00a0\u00b1\u00a00.121 for chest images, and SSIM of 0.971\u00a0\u00b1\u00a00.000, PSNR of 36.69\u00a0\u00b1\u00a02.54\u00a0dB, and VIFp of 0.510\u00a0\u00b1\u00a00.007 for abdominal images. Visualizations of residual and difference maps confirmed superior noise suppression and structural fidelity. Ablation studies and Wilcoxon signed\u2010rank tests (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05) validated the significant contributions of the residual\u2010shifting mechanism and each regularization component. RE\u2010EDPM processes two 512\u00a0\u00d7\u00a0512 slices in approximately 0.25\u00a0s (\u2248\u00a00.125\u00a0s per slice) on modern high\u2010performance hardware, supporting near\u2010real\u2010time clinical application.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>RE\u2010EDPM offers robust LDCT denoising with minimal inference time, achieving an optimal balance between noise reduction and anatomical preservation. Its efficiency and high performance make it a strong candidate for real\u2010time clinical deployment and for broader applications in transforming low\u2010quality to high\u2010quality medical imaging tasks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70626","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T05:26:42Z","timestamp":1786080402000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Low\u2010dose CT imaging using a regularization\u2010enhanced efficient diffusion probabilistic model"],"prefix":"10.1002","volume":"53","author":[{"given":"Qiang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mojtaba","family":"Safari","sequence":"additional","affiliation":[{"name":"Department of 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