mlsanityData to 14 Oct 2026

ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising

arXiv 2607.01756 ↗First posted 2 Jul 2026cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 7 of its 40 references that are in the map. First posted on arXiv 2 Jul 2026.

Builds on

7 of 40 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization4 Apr

Other references · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2023ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shiftingcited by 365
  4. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  5. 2024MambaOut: Do We Really Need Mamba for Vision?cited by 143
  6. 2024All-In-One Medical Image Restoration via Task-Adaptive Routingcited by 30

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  1. 2026TAFG-MAN: Timestep-Adaptive Frequency-Gated Latent Diffusion for Efficient and High-Quality Low-Dose CT Image Denoising
  2. 2022Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup
  3. 2026RDDM: A Residual-Driven Drifting Model for High-Fidelity Low-Dose CT Denoising
  4. 2023Diffusion Denoising for Low-Dose-CT Model
  5. 2025FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
  6. 2026DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling
  7. 2025FD-DiT: Frequency Domain-Directed Diffusion Transformer for Low-Dose CT Reconstruction
  8. 2025AST-n: A Fast Sampling Approach for Low-Dose CT Reconstruction using Diffusion Models