MRI-Image-Priors / README.md
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metadata
language: en
tags:
  - MRI image priors
  - Generative models
  - Diffusion models
  - TensorFlow
  - PixelCNN

Generative pretrained models on MRI images.

The prior distribution of MRI images learned with generative models has proven to be effective in MRI image reconstruction. Here, we include four PixelCNN models and two diffusion models, one is SMLD and the another one is DDPM. These models are trained with spreco. For more details on how these models were trained, please find them in our paper and the related codes.

How to use

The Berkeley Advanced Reconstruction Toolbox, (BART), provides many functionalities for MRI image reconstruction. It introduced the application of the TensorFlow graph as regularization in this paper. You can try it on colab. Open In Colab

Prior Model Phase Size Contrast Subscript
PSC\texttt{P}_\mathrm{SC} PixelCNN preserved 1000 T1, T2, T2-FLAIR, T2βˆ—\texttt{T}^*_\mathrm{2} SC - Small, complex
PSM\texttt{P}_\mathrm{SM} PixelCNN unknown 1000 T1, T2, T2-FLAIR, T2βˆ—\texttt{T}^*_\mathrm{2} SM - Small, magnitude
PLM\texttt{P}_\mathrm{LM} PixelCNN unknown ~20000 MPRAGE LM - Large, magnitude
PLC\texttt{P}_\mathrm{LC} PixelCNN generated ~20000 MPRAGE LC - Large, complex
DSC\texttt{D}_\mathrm{SC} Diffusion generated ~80000 MPRAGE SC - SMLD, complex
DPC\texttt{D}_\mathrm{PC} Diffusion generated ~80000 MPRAGE PC - DDPM, complex

Citation

  1. Luo, G, Blumenthal, M, Heide, M, Uecker, M. Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models. Magn Reson Med. 2023; 1-17
  2. Blumenthal, M, Luo, G, Schilling, M, Holme, HCM, Uecker, M. Deep, deep learning with BART. Magn Reson Med. 2023; 89: 678- 693.
  3. Luo, G, Zhao, N, Jiang, W, Hui, ES, Cao, P. MRI reconstruction using deep Bayesian estimation. Magn Reson Med. 2020; 84: 2246-2261.