One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
On the map: Diffusion models → Medical imaging and segmentation
Abstract (arXiv)
Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. However, lowering the radiation dose will significantly degrade the image quality. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. Therefore, we propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction. To extract sufficient prior information from single sample, the Hankel matrix formulation is employed. Besides, the penalized weighted least-squares and total variation are introduced to achieve superior image quality. Specifically, we first train a score-based generative model on one sinogram by extracting a great number of tensors from the structural-Hankel matrix as the network input to capture prior distribution. Then, at the inference stage, the stochastic differential equation solver and data consistency step are performed iteratively to obtain the sinogram data. Finally, the final image is obtained through the filtered back-projection algorithm. The reconstructed results are approaching to the normal-dose counterparts. The results prove that OSDM is practical and effective model for reducing the artifacts and preserving the image quality.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 15 of its 51 references that are in the map. First posted on arXiv 7 Dec 2022.
Builds on
15 of 51 references.
Closest ancestors · 2
Same direction, newest first
- 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov
- 2021Score-based diffusion models for accelerated MRI8 Oct
Other references · 13
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
- 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
- 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
- 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
- 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
- 2016A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstructioncited by 87
- 2022Conversion Between CT and MRI Images Using Diffusion and Score-Matching Modelscited by 69
- 2018Deep-neural-network based sinogram synthesis for sparse-view CT image reconstructioncited by 47
- 2018Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scanscited by 24
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.
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- 2024Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction
- 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
- 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
- 2025Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Data
- 2024CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates
- 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
- 2022Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction
- 2024Integrating Deep Unfolding with Direct Diffusion Bridges for Computed Tomography Reconstruction