MR image reconstruction and processing package specifically developed for PyTorch.
- Source code: https://github.com/PTB-MR/mrpro
- Documentation: https://ptb-mr.github.io/mrpro/
- Bug reports: https://github.com/PTB-MR/mrpro/issues
- Try it out:
- 2024 ISMRM QMRI Study Group Challenge, 2nd prize for Relaxometry (T2* and T1)
- ISMRMRD support MRpro supports ismrmrd-format for MR raw data.
- PyTorch All data containers utilize PyTorch tensors to ensure easy integration in PyTorch-based network schemes.
- Cartesian and non-Cartesian trajectories MRpro can reconstruct data obtained with Cartesian and non-Cartesian (e.g. radial, spiral...) sapling schemes. MRpro automatically detects if FFT or nuFFT is required to reconstruct the k-space data.
- Pulseq support If the data acquisition was carried out using a pulseq-based sequence, the seq-file can be provided to MRpro and the used trajectory is automatically calculated.
- Signal models A range of different MR signal models are implemented (e.g. T1 recovery, WASABI).
- Regularized image reconstruction Regularized image reconstruction algorithms including Wavelet-based compressed sensing or total variation regularized image reconstruction are available.
In the following, we show some code snippets to highlight the use of MRpro. Each code snippet only shows the main steps. A complete working notebook can be found in the provided link.
Read the data and trajectory and reconstruct an image by applying a density compensation function and then the adjoint of the Fourier operator and the adjoint of the coil sensitivity operator.
# Read the trajectory from the ISMRMRD file
trajectory = mrpro.data.traj_calculators.KTrajectoryIsmrmrd()
# Load in the Data from the ISMRMRD file
kdata = mrpro.data.KData.from_file(data_file.name, trajectory)
# Perform the reconstruction
reconstruction = mrpro.algorithms.reconstruction.DirectReconstruction.from_kdata(kdata)
img = reconstruction(kdata)Full example: https://github.com/PTB-MR/mrpro/blob/main/examples/direct_reconstruction.py
Quantitative parameter maps can be obtained by creating a functional to be minimized and calling a non-linear solver such as ADAM.
# Define signal model
model = MagnitudeOp() @ InversionRecovery(ti=idata_multi_ti.header.ti)
# Define loss function and combine with signal model
mse = MSE(idata_multi_ti.data.abs())
functional = mse @ model
[...]
# Run optimization
params_result = adam(functional, [m0_start, t1_start], max_iter=max_iter, lr=lr)Full example: https://github.com/PTB-MR/mrpro/blob/main/examples/qmri_sg_challenge_2024_t1.py
The trajectory can be calculated directly from a provided pulseq-file.
# Read raw data and calculate trajectory using KTrajectoryPulseq
kdata = KData.from_file(data_file.name, KTrajectoryPulseq(seq_path=seq_file.name))Full example: https://github.com/PTB-MR/mrpro/blob/main/examples/pulseq_2d_radial_golden_angle.py
We are looking forward to your contributions via Pull-Requests.
- Clone the MRpro repository
- Create/select a python environment
- Install "MRpro" in editable mode including test dependencies:
pip install -e ".[test]" - Setup pre-commit hook:
pre-commit install
Please look at our contributor guide for more information on the repository structure, naming conventions, and other useful information.