[Paper] [Dataset] [Github] [BibTeX]
02-07-2024: Updated files for slice-, volume-, exam-level labels and their paths for T2 and Diffusion sequences in the fastMRI prostate dataset.
Classification: The classification folder contains code for training deep learning models to detect clinically significant prostate cancer. Reconstruction: The reconstruction folder contains code for training deep learning models for reconstructing diffusion MRI images from undersampled k-space.
This repository contains code to facilitate the reconstruction of prostate T2 and DWI (Diffusion-Weighted Imaging) images from raw (k-space) data from the fastMRI Prostate dataset. It includes reconstruction methods along with utilities for pre-processing and post-processing the data.
The package is intended to serve as a starting point for those who want to experiment and develop alternate reconstruction techniques.
The code requires python >= 3.9
Install FastMRI Prostate: clone the repository locally and install with
git clone https://github.com/cai2r/fastMRI_prostate.git
cd fastmri_prostate
pip install -e .
The repository is centered around the fastmri_prostate package. The following breaks down the basic structure:
fastmri_prostate: Contains a number of basic tools for T2 and DWI reconstruction
fastmri_prostate.data: Provides data utility functions for accessing raw data fields like kspace, calibration, phase correction, and coil sensitivity maps.fastmri.reconstruction.t2: Contains functions required for prostate T2 reconstructionfastmri.reconstruction.dwi: Contains functions required for prostate DWI reconstruction
fastmri_prostate_recon.py contains code to read files from the dataset and call the T2 and DWI reconstruction functions for a single h5 file.
fastmri_prostate_tutorial.ipynb walks through an example of loading a h5 file from the fastMRI prostate dataset and reconstructing T2/DW images.
To reconstruct T2/DW images from the fastMRI prostate raw data, users can download the dataset and run fastmri_prostate_recon.py with appropriate arguments, specifying the path to the root of the downloaded dataset, output path to store reconstructions, and the sequence (T2, DWI, or both).
python fastmri_prostate_recon.py \
--data_path <path to dataset> \
--output_path <path to store recons> \
--sequence <t2/dwi/both>
The reconstruction algorithms implemented in this package requires the following hardware:
- A computer with at least 32GB of RAM
- A multi-core CPU
The run time of a single T2 reconstruction takes ~15 minutes while the Diffusion Weighted reconstructions take ~7 minutes on a multi-core CPU Linux machine with 64GB RAM. A bulk of the time is spent in applying GRAPPA weights to the undersampled raw kspace data.
fastMRI_prostate is MIT licensed, as found in LICENSE file
If you use the fastMRI Prostate data or code in your research, please use the following BibTeX entry.
@article{tibrewala2024fastmri,
title={FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging},
author={Tibrewala, Radhika and Dutt, Tarun and Tong, Angela and Ginocchio, Luke and Lattanzi, Riccardo and Keerthivasan, Mahesh B and Baete, Steven H and Chopra, Sumit and Lui, Yvonne W and Sodickson, Daniel K and others},
journal={Scientific Data},
volume={11},
number={1},
pages={404},
year={2024},
publisher={Nature Publishing Group UK London}
}
The code for the GRAPPA technique was based off pygrappa, and ESPIRiT maps provided in the dataset were computed using espirit-python