/MRI_Sampling_Diffusion

Code for "Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models"

Primary LanguagePythonMIT LicenseMIT

Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models

Code for "Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models". Our pre-print can be found at https://arxiv.org/abs/2306.03284.

Setup

First, set up a Conda environment using conda env create -f conda_env.yml.

Download the model checkpoints and fastMRI metadata from: https://drive.google.com/file/d/18n2QUN30qrBbM9rcxS3HIjIWImSbkJ-2/view?usp=sharing

Structure

  • algorithms: algorithms for solving inverse problems
  • configs: yaml config files for running experiments
  • datasets: PyTorch dataset classes
  • learners: the main control classes for gradient-based meta-learning
  • problems: defines forward operators as classes for re-usability
  • utils: useful functions for experiment logging, metrics, and losses
  • main.py: program to invoke for running meta-learning from command line

How to run

Here is an example command for training and evaluating a sampling mask:

python3 main.py --config PATH_TO_CONFIG --doc NAME_OF_EXPERIMENT

Here is a command for evaluating a baseline mask on test data:

python3 main.py --config PATH_TO_CONFIG --doc NAME_OF_EXPERIMENT --baseline

Submodule initialization

git submodule update --init --recursive