{"ID":5675161,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-05T06:01:40.470861498Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01756","arxiv_id":"2607.01756","title":"ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising","abstract":"Low-dose computed tomography (LDCT) reduces radiation exposure but introduces stronger quantum noise, streak artifacts, and local texture degradation, which can obscure anatomical boundaries and weaken low-contrast structures. Diffusion models are promising for LDCT denoising by progressively recovering normal-dose CT (NDCT) images from degraded LDCT inputs, but existing methods often suffer from insufficient anatomical guidance, uncertain frequency-dependent recovery, and uniform reverse-process modeling. We propose ProSAC-CT, a progressive spectral-anatomical co-guided multi-stage diffusion model for image-domain LDCT denoising. ProSAC-CT integrates an anatomical-prior-guided conditioning (APGC) module, a residual frequency-domain decoupling stage (RFDDS), and a time-step-decoupling denoising decoder (TD3). APGC extracts LDCT-derived structural guidance, RFDDS enhances frequency-aware representations, and TD3 assigns them to different reverse-diffusion stages for anatomical stabilization, boundary refinement, and fine-detail recovery. Experiments on four LDCT degradation benchmarks show that ProSAC-CT improves image fidelity, structural similarity, perceptual quality, and information preservation over representative methods while better preserving boundary-sensitive anatomical details. Downstream anatomical-region classification on Mayo-2020 further indicates that ProSAC-CT retains task-relevant anatomical information, supporting its practical use for low-dose CT denoising.","short_abstract":"Low-dose computed tomography (LDCT) reduces radiation exposure but introduces stronger quantum noise, streak artifacts, and local texture degradation, which can obscure anatomical boundaries and weaken low-contrast structures. Diffusion models are promising for LDCT denoising by progressively recovering normal-dose CT...","url_abs":"https://arxiv.org/abs/2607.01756","url_pdf":"https://arxiv.org/pdf/2607.01756v1","authors":"[\"Xuepeng Liu\",\"Zetong Liu\",\"Renyiming Li\",\"Yan Li\",\"Ruiyu Li\",\"Ruili Li\",\"Jiayi Ding\",\"Eichi Takaya\"]","published":"2026-07-02T06:13:43Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}