Diffusion-Models-for-Medical-Imaging

Diffusion Models for Medical Imaging [Diffusion model in projection data (PPT)]

• Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning
  [Paper]

• Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data
  [Paper]

• 基于扩散模型的医学成像研究综述
  [Paper] [CT讲堂-扩散模型的理论及应用 (PPT)]

• 观测域医学成像与生成-从多分布到多任务
  [MICS交流报告 (PPT)]

Learning from DAE to DSM

• Highly Undersampled Magnetic Resonance Imaging Reconstruction using Autoencoding Priors
  [Paper] [Code] [Slide] [数学图像联盟会议交流PPT]

• High-dimensional Embedding Network Derived Prior for Compressive Sensing MRI Reconstruction
  [Paper] [Code]

• Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction
  [Paper] [Paper] [Code]

• REDAEP: Robust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction
  [Paper] [Code] [PPT] [数学图像联盟会议交流PPT]

• Accelerated model-based iterative reconstruction strategy for sparse-view photoacoustic tomography aided by multi-channel autoencoder priors
  [Paper] [Code]

• Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model
  [Paper] [Code] [PPT]

• Wavelet-improved Score-based Generative Model for Medical Imaging
  [Paper]

• 基于分数匹配生成模型的无透镜成像方法
  [Paper] [Code] [CIIS 2023-PPT]

• Imaging through scattering media via generative diffusion model
  [Paper] [Code]

• Fluorescence molecular tomography via score-based generative model
  [Paper] [Code]

Learning from Image Domain to Projection Domain

• Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
  [Paper] [Code] [Slide]

• Universal Generative Modeling for Calibration-free Parallel MR Imaging
  [Paper] [Code] [Poster]

• WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
  [Paper] [Code] [ISMRM_2022_slideliu6] [ISMRM_2022_liu]

• Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction
  [Paper] [Code]

• Universal Generative Modeling in Dual-domain for Dynamic MR Imaging
  [Paper] [Code]

• Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion
  [Paper]

• Sub-DM: Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction
  [Paper] [Code]

• Latent-k-space of refinement diffusion model for accelerated MRI reconstruction
  [Paper]

• Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
  [Paper] [Code]

• Multi-phase FZA lensless imaging via diffusion model
  [Paper] [Code] [CIIS 2023-PPT]

• Generative model for sparse photoacoustic tomography artifact removal
  [Paper]

• RED: Residual Estimation Diffusion for Low-Dose PET Sinogram Reconstruction
  [Paper] [Code]

• Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration
  [Paper] [Code]

• High-resolution iterative reconstruction at extremely low sampling rate for Fourier single-pixel imaging via diffusion model
  [Paper] [Code]

Learning from Large to Small Dataset

• One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction
  [Paper] [Code] [PPT]

• One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
  [Paper] [Code]

• Low-rank Angular Prior Guided Multi-diffusion Model for Few-shot Low-dose CT Reconstruction
  [Paper] [Code]

• Generative Modeling in Structural-Hankel Domain for Color Image Inpainting
  [Paper] [Code] [CIIS 2023-PPT]

Learning from One to Multiple Models

• Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-view CT Reconstruction
  [Paper] [Code]

• Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction
  [Paper] [Code]

• Diffusion Model based on Generalized Map for Accelerated MRI
  [Paper] [Code]

• MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction
  [Paper] [Code]

• Ordered-subsets Multi-diffusion Model for Sparse-view CT Reconstruction
  [Paper]

• Multiple diffusion models-enhanced extremely limited-view reconstruction strategy for photoacoustic tomography boosted by multi-scale priors
  [Paper] [Code]

Learning from Regular to Irregular Samples

• Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction
  [Paper] [Code]

• DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models
  [Paper] [Code]

• MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction
  [Paper] [Code]

• Diffusion Transformer Meets Random Masks: An Advanced PET Reconstruction Framework
  [Paper] [Code]

• Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked Diffusion
  [Paper] [Code]

• Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction
  [Paper] [Code]

• Distribution matching with subset-k-space embedding for multi-contrast MRI reconstruction
  [Paper]

Learning from Diffusion Model to Foundation Model

• Raw_data_generation [Code]

• PRO: Projection Domain Synthesis for CT Imaging [Paper] [Code]

• UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardization[Paper] [Code]

• ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domain [Paper] [Code]

• K-Syn: K-space Data Synthesis in Ultra Low-data Regimes [Paper]

• Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction [Paper] [Code]

Other Related Projects

• Diffusion Models for Computational Optical Imaging [Code] [Slide]

• Diffusion Models for Photoacoustic Imaging [Code] [Slide]

• MICS Presentation 2026 [Slide]

• 算法处理示例 [Code]