Fast machine learning reconstruction of radially undersampled k-space data for low-latency real-time MRI

This repository contains code for the fast machine learning (ML) reconstruction of radially undersampled 2D k-space data for low-latency real-time MRI.

The code has been developed for the paper "Fast machine learning image reconstruction of radially undersampled k-space data for low-latency real-time MRI".

Computing environment/docker container

Docker hub

The computing environment for this repository can be obtained from docker hub:

docker pull docker.io/balthasarschachtner/radler:ML-MRI-Reconstruction

To build the container yourself, go to the docker directory and run docker build ..

Run code

This repository contains code for the training and evaluation of the proposed ML model for the fast reconstruction of undersampled MR data.

It provides code for:

1. Generating synthetic data
2. Training the machine learning model
3. Reconstructing synthetic data
4. Reconstructing MR data

Before running the code, adjust paths in a config file with the name <config_name> (e.g., config_uniform.json) in the configs/ folder.

Download the training and validation images of the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) [1, 2] dataset and move them to the directory specified in path_to_images in the config file.

We used functions provided in the twixtools repository to load MRI raw data. For this, we cloned the repository in the folder src/utils/ (and adjusted import paths if required).

1. Generating synthetic data

We used synthetic data for training the ML model and evaluating its reconstruction performance.

First, we generate complex-valued images with similar magnitude and phase characteristics as MR images from natural images (from the ILSVRC2012 dataset). The images are split into a training set with train_set_size, a validation set with validation_set_size and a test set with test_set_size images. The size of each set is defined in the config file. For generating the complex-valued images and performing the data split, we run this script:

python -m scripts.01_preprocessing_output --config_file_name <config_name>

Next, we generate k-space data from the complex-valued images with a radial trajectory (with <traj_angle>, <traj_shift> and <traj_isotropy>) and <number_of_spokes> spokes:

 python -m scripts.02_preprocessing_input --config_file_name <config_name> --num_spokes <number_of_spokes> --traj_variant 'radial' --traj_angle <traj_angle> --traj_shift <traj_shift> --traj_isotropy <traj_isotropy> --subfolder_name <subfolder_name>

To analyze the robustness of the reconstruction method to noise, we generated k-space data without additional Gaussian noise (set <subfolder_name>=standard) and with additional Gaussian noise (set <subfolder_name>=noise and set flag --noise) in this project. You can decide for which dataset k-space data should be generated by setting the corresponding flags --generate_k_space_for_train_set, --generate_k_space_for_validation_set and/or --generate_k_space_for_test_set.

2. Training the machine learning model

We train the ML model with the synthetic training data as follows:

 python -m scripts.03_ml_training --config_file_name <config_name> --num_spokes <number_of_spokes> --device <device> --batch_size <batch_size> --model_name <model_name> --optimizer_name <optimizer_name> --weight_decay <weight_decay> --folder_name_ml_model <folder_name_ml_model> --dropout <dropout> --noise_level <noise_level> --subfolder_name <subfolder_name>

3. Reconstructing synthetic data

Next, we compare the reconstruction performance of the proposed ML model on the synthetic test data

 python -m scripts.04_evaluation_synthetic_data --config_file_name <config_name> --num_spokes <number_of_spokes> --traj_variant 'radial' --traj_angle <traj_angle> --traj_shift <traj_shift> --traj_isotropy <traj_isotropy> --method 'ML' --device <reconstruction_device> --model_name <model_name> --folder_name_ml_model <folder_name_ml_model> --subfolder_name <subfolder_name> --warm_up

with the performance of an adjoint NUFFT with zero-filling

 python -m scripts.04_evaluation_synthetic_data --config_file_name <config_name> --num_spokes <number_of_spokes> --traj_variant 'radial' --traj_angle <traj_angle> --traj_shift <traj_shift> --traj_isotropy <traj_isotropy> --method 'nufft_adjoint' --device 'cpu' --subfolder_name <subfolder_name> --warm_up

and a compressed sensing (CS) approach

 python -m scripts.04_evaluation_synthetic_data --config_file_name <config_name> --num_spokes <number_of_spokes> --traj_variant 'radial' --traj_angle <traj_angle> --traj_shift <traj_shift> --traj_isotropy <traj_isotropy> --method 'CS' --device <reconstruction_device> --device <reconstruction_device> --bart_regularization_option <bart_regularization_option> --bart_regularization <bart_regularization> --maxiter <bart_maxiter> --subfolder_name <subfolder_name> --warm_up

implemented in the Berkeley advanced reconstruction toolbox (BART) [3, 4].

4. Reconstructing MR data

We reconstruct k-space measurements with the ML model as follows:

python -m scripts.05_reconstruct_measurements --config_file_name <config_name> --num_spokes <number_of_spokes> --traj_variant <traj_variant> --traj_angle <traj_angle> --traj_shift <traj_shift> --traj_isotropy <traj_isotropy> --method 'ML' --device <reconstruction_device> --filename <filename> --image_number <set_image_number> --orientation <set_orientation> --model_name <model_name> --subfolder_name <subfolder_name> --folder_name_ml_model <subfolder_name> --num_repeat <num_repeat> --warm_up

For comparison, we also reconstruct the MR data with a NUFFT

python -m scripts.05_reconstruct_measurements --config_file_name <config_name> --num_spokes <number_of_spokes> --traj_variant <traj_variant> --traj_angle <traj_angle> --traj_shift <traj_shift> --traj_isotropy <traj_isotropy> --method 'nufft_adjoint' --device 'cpu' --filename <filename> --image_number <set_image_number> --orientation <set_orientation> --subfolder_name <subfolder_name> --num_repeat <num_repeat> --warm_up

Reproduce results from paper

To reproduce the results from the paper, run the following bash scripts:

. scripts/bash_scripts/run_all_uniform_trajectory.sh config_uniform.json

and

. scripts/bash_scripts/run_all_nonuniform_trajectory.sh config_nonuniform.json

The MRI phantom data used in this study can be found in the folder data/mri_phantom_data_numpy/. This data was retrieved using the function load_radial_k_space_measurements(...) from src.evaluation.reconstruct_measurements and then element five was selected (k_radial[5, :]).

Resources

[1] Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. Imagenet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211-52.

[2] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit; 2009. p. 248-55.

[3] Uecker M, Ong F, Tamir JI, Bahri D, Virtue P, Cheng JY, et al. Berkeley advanced reconstruction toolbox. In: Proc. Intl. Soc. Mag. Reson. Med.. vol. 23; 2015. p. 2486.

[4] Uecker M, Holme C, Blumenthal M, Wang X, Tan Z, Scholand N, et al. mrirecon/bart: version 0.7.00. Zenodo. 2021 March. Available from: https://doi.org/10.5281/zenodo.4570601.

Citation

Topalis J, Dexl J, Jeblick K, Klaar R, Kurz C, Löhr T, et al. (2025) Fast machine learning image reconstruction of radially undersampled k-space data for low-latency real-time MRI. PLoS One 20(11): e0334604. https://doi.org/10.1371/journal.pone.0334604