https://github.com/benny0323/diffusion-models-for-medical-imaging

Diffusion Models for Medical Imaging
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Diffusion Models for Medical Imaging
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# Diffusion-Models-for-Medical-Imaging
Diffusion Models for Medical Imaging [**[Diffusion model in projection data (PPT)]**](https://github.com/yqx7150/EDAEPRec/tree/master/Slide) 

* Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning    
[**[Paper]**](https://onlinelibrary.wiley.com/doi/10.1002/mrm.30105)

* Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data    
 [**[Paper]**](https://www.sciencedirect.com/science/article/abs/pii/S1746809421001762)              

*    
 [**[Paper]**](https://www.cttacn.org.cn/article/doi/10.15953/j.ctta.2024.316)  [**[CT- (PPT)]**](https://github.com/yqx7150/EDAEPRec/tree/master/Slide)              
      
## Learning from DAE to DSM
   

  * Highly Undersampled Magnetic Resonance Imaging Reconstruction using Autoencoding Priors  
[**[Paper]**](https://cardiacmr.hms.harvard.edu/files/cardiacmr/files/liu2019.pdf)  [**[Code]**](https://github.com/yqx7150/EDAEPRec)   [**[Slide]**](https://github.com/yqx7150/EDAEPRec/tree/master/Slide) [**[PPT]**](https://github.com/yqx7150/EDAEPRec/tree/master/Slide)

  * High-dimensional Embedding Network Derived Prior for Compressive Sensing MRI Reconstruction  
 [**[Paper]**](https://www.sciencedirect.com/science/article/abs/pii/S1361841520300815?via%3Dihub)   [**[Code]**](https://github.com/yqx7150/EDMSPRec)
 
  * Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction  
[**[Paper]**](https://www.sciencedirect.com/science/article/pii/S0925231221000990) [**[Paper]**](https://arxiv.org/ftp/arxiv/papers/1909/1909.01108.pdf)  [**[Code]**](https://github.com/yqx7150/WDAEPRec)

 * REDAEP: Robust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction  
[**[Paper]**](https://ieeexplore.ieee.org/document/9076295)   [**[Code]**](https://github.com/yqx7150/REDAEP)   [**[PPT]**](https://github.com/yqx7150/HGGDP/tree/master/Slide)  [**[PPT]**](https://github.com/yqx7150/EDAEPRec/tree/master/Slide)

* Accelerated model-based iterative reconstruction strategy for sparse-view photoacoustic tomography aided by multi-channel autoencoder priors       
[**[Paper]**](https://onlinelibrary.wiley.com/doi/10.1002/jbio.202300281)   [**[Code]**](https://github.com/yqx7150/PAT-MDAE)         
           
* Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model  
[**[Paper]**](https://ieeexplore.ieee.org/abstract/document/9703672)   [**[Code]**](https://github.com/yqx7150/EASEL)   [**[PPT]**](https://github.com/yqx7150/HGGDP/tree/master/Slide)

* Wavelet-improved Score-based Generative Model for Medical Imaging  
[**[Paper]**](https://ieeexplore.ieee.org/document/10288274)         

*       
[**[Paper]**](https://www.opticsjournal.net/Articles/OJf1842c2819a4fa2e/FigureTable)   [**[Code]**](https://github.com/yqx7150/LSGM)  [**[CIIS 2023-PPT]**](https://github.com/yqx7150/SHGM/tree/main)

* Imaging through scattering media via generative diffusion model      
[**[Paper]**](https://pubs.aip.org/aip/apl/article/124/5/051101/3176612/Imaging-through-scattering-media-via-generative)   [**[Code]**](https://github.com/yqx7150/ISDM)

* Fluorescence molecular tomography via score-based generative model      
[**[Paper]**](https://www.sciencedirect.com/science/article/pii/S0143816625000508)   [**[Code]**](https://github.com/yqx7150/FTSG)
       
## Learning from Image Domain to Projection Domain     
   
 
  * Homotopic Gradients of Generative Density Priors for MR Image Reconstruction  
[**[Paper]**](https://ieeexplore.ieee.org/abstract/document/9435335)   [**[Code]**](https://github.com/yqx7150/HGGDP) [**[Slide]**](https://github.com/yqx7150/HGGDP/tree/master/Slide)

  * Universal Generative Modeling for Calibration-free Parallel MR Imaging  
[**[Paper]**](https://biomedicalimaging.org/2022/)   [**[Code]**](https://github.com/yqx7150/UGM-PI)   [**[Poster]**](https://github.com/yqx7150/UGM-PI/blob/main/paper%20%23160-Poster.pdf)
    
  * WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction  
[**[Paper]**](https://arxiv.org/abs/2205.03883)  [**[Code]**](https://github.com/yqx7150/WKGM)   [**[ISMRM_2022_slideliu6]**](https://github.com/yqx7150/WKGM/blob/main/ISMRM_2022_slideliu6.pdf)  [**[ISMRM_2022_liu]**](https://submissions.mirasmart.com/ISMRM2022/Itinerary/ConferenceMatrixEventDetail.aspx?ses=WE-04)

* Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction  
[**[Paper]**](https://www.sciencedirect.com/science/article/abs/pii/S0730725X23001224)  [**[Code]**](https://github.com/yqx7150/LR-KGM)     

* Universal Generative Modeling in Dual-domain for Dynamic MR Imaging  
[**[Paper]**](https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/nbm.5011)  [**[Code]**](https://github.com/yqx7150/DD-UGM) 

* Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion         
[**[Paper]**](https://ieeexplore.ieee.org/document/10683732)      
           
* Sub-DM: Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction                              
[**[Paper]**](https://arxiv.org/pdf/2411.03758)                   
              
* Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction      
[**[Paper]**](https://ieeexplore.ieee.org/document/10233041)   [**[Code]**](https://github.com/yqx7150/GMSD)

* Multi-phase FZA lensless imaging via diffusion model      
[**[Paper]**](https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-12-20595&id=531211)   [**[Code]**](https://github.com/yqx7150/MLDM)  [**[CIIS 2023-PPT]**](https://github.com/yqx7150/SHGM/tree/main)       

* Generative model for sparse photoacoustic tomography artifact removal      
[**[Paper]**](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12745/1274503/Generative-model-for-sparse-photoacoustic-tomography-artifact-removal/10.1117/12.2683128.short?SSO=1)         

* RED: Residual Estimation Diffusion for Low-Dose PET Sinogram Reconstruction     
[**[Paper]**](https://www.sciencedirect.com/science/article/pii/S1361841525001057)       [**[Code]**](https://github.com/yqx7150/RED)

* Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration      
[**[Paper]**](https://www.sciencedirect.com/science/article/pii/S2213597923001118)       [**[Code]**](https://github.com/yqx7150/PAT-Diffusion)
              
* High-resolution iterative reconstruction at extremely low sampling rate for Fourier single-pixel imaging via diffusion model      
[**[Paper]**](https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-3-3138&id=545621)       [**[Code]**](https://github.com/yqx7150/FSPI-DM)


## Learning from Large to Small Dataset 
   
 
  * One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction  
[**[Paper]**](https://ieeexplore.ieee.org/document/10158730)   [**[Code]**](https://github.com/yqx7150/HKGM)   [**[PPT]**](https://github.com/yqx7150/HKGM/tree/main/PPT)

  * One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging  
[**[Paper]**](https://ieeexplore.ieee.org/abstract/document/10506793)   [**[Code]**](https://github.com/yqx7150/OSDM)    

  * Low-rank Angular Prior Guided Multi-diffusion Model for Few-shot Low-dose CT Reconstruction     
[**[Paper]**](https://ieeexplore.ieee.org/abstract/document/10776993)   [**[Code]**](https://github.com/yqx7150/PHD)
    
  * Generative Modeling in Structural-Hankel Domain for Color Image Inpainting  
[**[Paper]**](http://arxiv.org/abs/2211.13857)   [**[Code]**](https://github.com/yqx7150/SHGM)    [**[CIIS 2023-PPT]**](https://github.com/yqx7150/SHGM/tree/main)
  
     

## Learning from One to Multiple Models 
   
 
  * Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-view CT Reconstruction  
[**[Paper]**](https://ieeexplore.ieee.org/abstract/document/10403850)   [**[Code]**](https://github.com/yqx7150/SWORD)

  * Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction  
[**[Paper]**](https://ieeexplore.ieee.org/document/10577271)   [**[Code]**](https://github.com/lizrzr/DCDS-Dual-domain_Collaborative_Diffusion_Sampling)

  * Diffusion Model based on Generalized Map for Accelerated MRI  
[**[Paper]**](https://doi.org/10.1002/nbm.5232)   [**[Code]**](https://github.com/yqx7150/GM-SDE)   
  
  * MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction  
[**[Paper]**](https://arxiv.org/pdf/2405.05763)   [**[Code]**](https://github.com/yqx7150/MSDiff)

  * Ordered-subsets Multi-diffusion Model for Sparse-view CT Reconstruction      
[**[Paper]**](https://arxiv.org/abs/2505.09985)

 * Multiple diffusion models-enhanced extremely limited-view reconstruction strategy for photoacoustic tomography boosted by multi-scale priors  
[**[Paper]**](https://www.sciencedirect.com/science/article/pii/S2213597924000636)   [**[Code]**](https://github.com/yqx7150/MSDM-PAT)          

## Learning from Regular to Irregular Samples 
   

  * Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction  
[**[Paper]**](https://arxiv.org/abs/2309.00853)   [**[Code]**](https://github.com/yqx7150/CM-DM)

  * DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models  
[**[Paper]**](https://iopscience.iop.org/article/10.1088/1361-6560/add83a/meta)   [**[Code]**](https://github.com/yqx7150/DP-MDM)
     
  * MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction  
[**[Paper]**](https://arxiv.org/pdf/2405.05763)   [**[Code]**](https://github.com/yqx7150/MSDiff)
           
  * Diffusion Transformer Meets Random Masks: An Advanced PET Reconstruction Framework  
[**[Paper]**](https://arxiv.org/pdf/2503.08339)   [**[Code]**](https://github.com/yqx7150/DREAM)

  * Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked Diffusion  
[**[Paper]**](https://arxiv.org/abs/2501.09935)   [**[Code]**](https://github.com/yqx7150/SWARM) 

   * Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction  
[**[Paper]**](https://arxiv.org/pdf/2506.18270)   [**[Code]**](https://github.com/yqx7150/AMDM) 

## Learning from Diffusion Model to Foundation Model 
![](https://github.com/yqx7150/Raw_data_generation-/blob/main/images/high_resolution3.png)

  * Raw_data_generation  [**[Code]**](https://github.com/yqx7150/Raw_data_generation)

  * PRO: Projection Domain Synthesis for CT Imaging  [**[Paper]**](https://arxiv.org/pdf/2506.13443)   [**[Code]**](https://github.com/yqx7150/PRO)
       
  * UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardization[**[Paper]**](https://arxiv.org/abs/2508.17816)   [**[Code]**](https://github.com/yqx7150/UniSino)
     
## Other Related Projects
    
  * Diffusion Models for Computational Optical Imaging  [**[Code]**](https://github.com/yqx7150/Diffusion-Models-for-Computational-Optical-Imaging)   [**[Slide]**](https://github.com/yqx7150/Diffusion-Models-for-Computational-Optical-Imaging/tree/main/CITA2024.pptx)

  * Diffusion Models for Photoacoustic Imaging  [**[Code]**](https://github.com/yqx7150/Diffusion-Models-for-Photoacoustic-Imaging)   [**[Slide]**](https://github.com/yqx7150/Diffusion-Models-for-Photoacoustic-Imaging/blob/main/SXL-NCU0629.pdf)