Microstructure Diffusion Toolbox
Project description
The Microstructure Diffusion Toolbox, MDT, (formerly known as the Maastricht Diffusion Toolbox) is a framework and library for parallelized (GPU and multi-core CPU) diffusion Magnetic Resonance Imaging (MRI) modeling. MDT’s object oriented and modular design allows arbitrary user specification and combination of biophysical MRI compartment models, diffusion- and T1, T2, T2* based microstructure models, likelihood functions and optimization algorithms. MDT was designed with compatibility in mind and adheres to input, output and variable naming conventions used by other related software tools. Many diffusion and relaxometry microstructure models are included, and new models can be added dynamically. MDT can be extended to other modalities and other parametric models estimated from data volumes varying along controlled parameters (such as b-values, diffusion times, TE, TM, flip angle, etc). The parallelized accelerated computations allow for tens to hundred times faster model fitting, even on standard GPU (and/or CPU) hardware, making MDT ideal for large group studies or population studies.
Summary
HCP pipelines
Comes with CHARMED, NODDI, BinghamNODDI, NODDIDA, NODDI-DTI, ActiveAx, Ball&Sticks, Ball&Rackets, Kurtosis, Tensor and relaxometry (T1, T2) models.
Gaussian, Offset-Gaussian and Rician noise models
Powell, Levenberg-Marquardt and Nelder-Mead Simplex optimization methods
Multiple MCMC sampling methods
GUI, command line and python interfaces
Dynamically add your own models
Free Open Source Software: LGPL v3 license
Python and OpenCL based
Supports hyperpriors on parameters
Supports gradient deviations per voxel and per voxel per volume
Supports volume weighted objective function
Parallelization over voxels and over volumes
Runs on Windows, Mac and Linux operating systems
Runs on Intel, Nvidia and AMD GPU’s and CPU’s.
Links
Full documentation: http://mdt_toolbox.readthedocs.io
Project home: https://github.com/cbclab/MDT
HCP Pipeline
MDT comes pre-installed with Human Connectome Project (HCP) compatible pipelines for the MGH and the WuMinn 3T studies. To run, after installing MDT, go to the folder where you downloaded your (pre-processed) HCP data (MGH or WuMinn) and execute:
$ mdt-batch-fit . 'NODDI (Cascade)'
and it will autodetect the study in use and fit your selected model to all the subjects.
Quick installation guide
The basic requirements for MDT are:
Python 3.x
OpenCL 1.2 (or higher) support in GPU driver or CPU runtime
Linux
For Ubuntu >= 16 you can use:
sudo add-apt-repository ppa:robbert-harms/cbclab
sudo apt-get update
sudo apt-get install python3-mdt python3-pip
sudo pip3 install tatsu
For Debian users and Ubuntu < 16 users, install MDT with:
sudo apt-get install python3 python3-pip python3-pyopencl python3-numpy python3-nibabel python3-pyqt5 python3-matplotlib python3-yaml python3-argcomplete libpng-dev libfreetype6-dev libxft-dev
sudo pip3 install mdt
Note that python3-nibabel may need NeuroDebian to be available on your machine. An alternative is to use pip3 install nibabel instead.
Users of Singularity can use the recipe at https://github.com/akhanf/mdt-singularity , kindly made available by an user of MDT.
Windows
The installation on Windows is a little bit more complex and the following is only a quick reference guide. For complete instructions please view the complete documentation.
Install Anaconda Python 3.*
Install MOT using the guide at https://mot.readthedocs.io
Open an Anaconda shell and type: pip install mdt
Mac
Install Anaconda Python 3.*
Open a terminal and type: pip install mdt
Please note that Mac support is experimental due to the unstable nature of the OpenCL drivers in Mac, that is, users running MDT with the GPU as selected device may experience crashes. Running MDT in the CPU seems to work though.
For more information and full installation instructions see https://mdt_toolbox.readthedocs.org
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for mdt-0.14.10-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 253daf97471b010add19df93d316e1aaaef215c851b235ff24ee78aa9b7582c1 |
|
MD5 | 47c56d9725fbb10baf44f168c7f48a88 |
|
BLAKE2b-256 | 6459237621a743d3a6b96b9bc050c4b3e5d84c8161a1cdc0dd08734577d7b478 |