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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