mlsanityData to 7 Oct 2026

Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction

arXiv 2405.17167 ↗First posted 27 May 2024eess.IV cs.CVcited by 2

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Low-dose computed tomography (LDCT) plays a vital role in clinical applications by mitigating radiation risks. Nevertheless, reducing radiation doses significantly degrades image quality. Concurrently, common deep learning methods demand extensive data, posing concerns about privacy, cost, and time constraints. Consequently, we propose a few-shot low-dose CT reconstruction method using Partitioned Hankel-based Diffusion (PHD) models. During the prior learning stage, the projection data is first transformed into multiple partitioned Hankel matrices. Structured tensors are then extracted from these matrices to facilitate prior learning through multiple diffusion models. In the iterative reconstruction stage, an iterative stochastic differential equation solver is employed along with data consistency constraints to update the acquired projection data. Furthermore, penalized weighted least-squares and total variation techniques are introduced to enhance the resulting image quality. The results approximate those of normal-dose counterparts, validating PHD model as an effective and practical model for reducing artifacts and noise while preserving image quality.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 11 of its 41 references that are in the map. First posted on arXiv 27 May 2024.

Builds on

11 of 41 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction25 Nov
  2. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov

Other references · 9

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  3. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  4. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  5. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  6. 2019Data augmentation using learned transformations for one-shot medical image segmentationcited by 138
  7. 2022One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstructioncited by 11
  8. 2019Low-Dose CT with Deep Learning Regularization via Proximal Forward Backward Splittingcited by 9
  9. 2020Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Modelcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Progressive Masked Refinement Self-supervised Learning for Low-Dose CT Denoisingcited by 0
  2. 2026Projection Guided Personalized Federated Learning for Low Dose CT Denoisingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
  2. 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
  3. 2023Diffusion Denoising for Low-Dose-CT Model
  4. 2026GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization
  5. 2025FD-DiT: Frequency Domain-Directed Diffusion Transformer for Low-Dose CT Reconstruction
  6. 2022Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup
  7. 2023Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT
  8. 2025Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Data