Welcome to MRpro’s documentation!
MR image reconstruction and processing for PyTorch
Main Features
Standard file formats MRpro supports the ISMRMRD format for MR raw data and DICOM for image data
PyTorch integration All data containers utilize PyTorch tensors to ensure easy integration with PyTorch-based network schemes.
Cartesian and non-Cartesian trajectories MRpro can reconstruct data obtained with Cartesian and non-Cartesian sampling schemes (e.g., radial, spiral). It automatically detects whether 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, which will automatically calculate the used trajectory.
Signal models A range of MR signal models is implemented (e.g., T1 recovery, WASABI).
Regularized image reconstruction Regularized image reconstruction algorithms, including wavelet-based compressed sensing and total variation regularized image reconstruction, are available.
Content
- User Guide
- Examples
- Basics of MRpro and Cartesian reconstructions
- Different ways to obtain the trajectory
- Direct reconstruction of 2D golden angle radial data
- Iterative SENSE reconstruction of 2D golden angle radial data
- Regularized iterative SENSE reconstruction of 2D golden angle radial data
- QMRI Challenge ISMRM 2024 - \(T_1\) mapping
- \(T_1\) mapping from a continuous golden radial acquisition
- Total-variation (TV)-minimization reconstruction
- Contributor Guide
- API
- FAQ