Welcome to PyQMRI’s documentation!

3D model based parameter quantification for MRI.

PyQMRI is a Python module to quantify tissue parameters given a set of MRI measurements, specifically desinged to quantify the parameter of interest. Examples include T1 quantification from variable flip angle or inversion-recovery Look-Locker data, T2 quantification using a mono-exponential fit, or Diffusion Tensor quantification.

In addition, a Genereal Model exists that can be invoced using a text file containing the analytical signal equation.

For a real world usage example have a look at the Quickstart Guide. The example can also be run interactively using GoogleColab.

Sample Data

In-vivo datasets used in the original publication (doi: [10.1002/mrm.27502]) can be found at zenodo. As these data sets are from an older release, the coil sensitivity profiles saved within the .h5 files need to be deleted prior to reconstruction. This invokes a new conputation of coil sensitivity profiles, matching the data within the fitting.


v1.1 Added the first iteration of CPU support. Tested on Intel CPUS using pocl on Ubuntu and Arch Linux.


If using the toolbox, please consider citing: “Maier et al., (2020). PyQMRI: An accelerated Python based Quantitative MRI toolbox. Journal of Open Source Software, 5(56), 2727, https://doi.org/10.21105/joss.02727

Also consider citing “Oliver Maier, Matthias Schloegl, Kristian Bredies, and Rudolf Stollberger; 3D Model-Based Parameter Quantification on Resource Constrained Hardware using Double-Buffering. Proceedings of the 27th meeting of the ISMRM, 2019, Montreal, Canada” if using parts of the software, specifically the PyOpenCL based NUFFT and the double buffering capabilities, in your work.

Older Releases:

You can find the code for

Maier O, Schoormans J,Schloegl M, Strijkers GJ, Lesch A, Benkert T, Block T, Coolen BF, Bredies K, Stollberger R
Rapid T1 quantification from high resolution 3D data with model‐based reconstruction.
Magn Reson Med., 2018; 00:1–16 doi: [10.1002/mrm.27502]

at [v0.1.0]