User Guide

MRpro is a MR image reconstruction and processing framework specifically developed to work well with pytorch. The data classes utilize torch tensors for storing data such as MR raw data or reconstructed image data. Where possible batch parallelisation of pytorch is utilized to speed up image reconstruction.

MRpro is designed to work directly from MR raw data using the MRD data format.

A basic pipeline would contain the following steps:

  • Reading in raw data

  • Preparation for reconstruction

  • Data reconstruction

  • Image processing

The following provides some basic information about these steps. For more detailed information please have a look at the notebooks in the examples folder. You can easily start a binder session via the badge in the README and give the notebooks a try without having to install anything.

Reading in raw data

Reading in raw data from a MRD file works by creating a KData object and using the class method from_file. KData contains the raw k-space data, the header information obtained from the MRD file and the k-space trajectory. To ensure the trajectory is calculated correctly, a KTrajectoryCalculator needs to be provided. The trajectory can either be calculated based on MRpro functionality (e.g. for a 2D radial sampling scheme), read out from MRD or calculated from a pulseq file.

Note

The trajectory is expected to be defined within the space of the encoding_matrix, e.g. if the encoding_matrix is defined as (z=1, y=256, x=256), then a fully sampled Cartesian trajectory without partial echo or partial Fourier is expected to be within [-128, 127] along both readout and phase encoding.

Preparation for reconstruction

MRpro provides a range of functionality to prepare the data for image reconstruction such as:

  • Noise prewhiting

  • Removal of oversampling along readout direction

  • Calculation of the density compensation function

  • Estimation of coil sensitivity maps

  • …

Data reconstruction

As a first step for the reconstruction an acquisition model consisting of linear and non-linear operators needs to be created. A simply acquisition model could consist of a SensitivityOp describing the effect of different receiver coils and FourierOp describing the transform from image space to k-space taking the sampling scheme (trajectory) into account. Additional operators describing transformations due to physiological motion or MR signal models can be added. All operators take one or more tensors as input and return a tuple of one or more tensors as output. Operators can be chained using @ to form a full acquisition model.

Based on the acquisition model a suitable minimization function and reconstruction algorithm needs to be selected.

Depending on the choices made above the reconstruction algorithms provides images (IData) or quantitative parametric maps (QData).

Image processing

Further processing of the reconstructed data such as quantitative parameter estimation is available. The examples folder also contains notebooks which show how to carry out motion estimation from reconstructed dynamic images.