Open-source, python and C++ tools for generative modeling of iron-induced R2* and R2 MRI relaxation rates
Different methods were proposed to determine effective transverse (R2*) and transverse (R2) MRI relaxation rates from microstructural magnetic inhomogeneities in brain tissue. In the code shown here, three of those methods were implemented: A Monte Carlo simulation of water diffusion implemented in C++, enabling to simulate MRI signal decays in the general case, as well as the static dephasing regime of negligible water diffusion, implemented in Python. These simulations are informed by 3D quantitative iron maps, for more details please refer to “Measuring the Iron Content of Dopaminergic Neurons in Substantia Nigra with MRI Relaxometry” referenced below.
Copyright (C) 2021 the original authors (Malte Brammerloh and Enrico Reimer) and the Max-Planck-Institute for Human Cognitive Brain Sciences ("MPI CBS").
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
The latest release of the code is available on GitLab.
Malte Brammerloh, Enrico Reimer (Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany)
Brammerloh, Malte, Markus Morawski, Isabel Friedrich, Tilo Reinert, Charlotte Lange, Primož Pelicon, Primož Vavpetič, et al. “Measuring the Iron Content of Dopaminergic Neurons in Substantia Nigra with MRI Relaxometry.” NeuroImage, June 11, 2021, 118255. https://doi.org/10.1016/j.neuroimage.2021.118255.
Acknowledgments and Funding
Malte Brammerloh is supported by the International Max Planck Research School NeuroCom. Nikolaus Weiskopf is supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 616905, the BMBF (01EW1711A & B) in the framework of ERA-NET NEURON, the European Union’s Horizon 2020 research and innovation program under the grant agreement No 681094.