mlsanityData to 14 Oct 2026

FD-DiT: Frequency Domain-Directed Diffusion Transformer for Low-Dose CT Reconstruction

arXiv 2506.23466 ↗First posted 30 Jun 2025eess.IV cs.CV physics.med-phcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Low-dose computed tomography (LDCT) reduces radiation exposure but suffers from image artifacts and loss of detail due to quantum and electronic noise, potentially impacting diagnostic accuracy. Transformer combined with diffusion models has been a promising approach for image generation. Nevertheless, existing methods exhibit limitations in preserving finegrained image details. To address this issue, frequency domain-directed diffusion transformer (FD-DiT) is proposed for LDCT reconstruction. FD-DiT centers on a diffusion strategy that progressively introduces noise until the distribution statistically aligns with that of LDCT data, followed by denoising processing. Furthermore, we employ a frequency decoupling technique to concentrate noise primarily in high-frequency domain, thereby facilitating effective capture of essential anatomical structures and fine details. A hybrid denoising network is then utilized to optimize the overall data reconstruction process. To enhance the capability in recognizing high-frequency noise, we incorporate sliding sparse local attention to leverage the sparsity and locality of shallow-layer information, propagating them via skip connections for improving feature representation. Finally, we propose a learnable dynamic fusion strategy for optimal component integration. Experimental results demonstrate that at identical dose levels, LDCT images reconstructed by FD-DiT exhibit superior noise and artifact suppression compared to state-of-the-art methods.

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 22 of its 54 references that are in the map. First posted on arXiv 30 Jun 2025.

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22 of 54 references.

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Same direction, newest first

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

Other references · 21

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9 more
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  2. 2023Diffusion Denoising for Low-Dose-CT Model
  3. 2026TAFG-MAN: Timestep-Adaptive Frequency-Gated Latent Diffusion for Efficient and High-Quality Low-Dose CT Image Denoising
  4. 2022Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup
  5. 2026ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising
  6. 2024Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction
  7. 2026FSP-Diff: Full-Spectrum Prior-Enhanced DualDomain Latent Diffusion for Ultra-Low-Dose Spectral CT Reconstruction
  8. 2025AST-n: A Fast Sampling Approach for Low-Dose CT Reconstruction using Diffusion Models