Package: mdt
The Microstructure Diffusion Toolbox (MDT) is a framework and library for microstructure modeling of magnetic resonance imaging (MRI) data.
The aim of MDT is to provide reproducible and comparable model fitting for MRI microstructure analysis.
As such, we provide a common platform for microstructure modeling including many models that can all be processed using the same optimization routines.
For maximum performance all models and algorithms were implemented to make use of all parallel processing capabilities of modern computers.
MDT combines flexible modeling with fast processing, targeting both model developers and data analysts.SummaryGPU accelerated processingHuman Connectome Project (HCP) pipelinesIncludes CHARMED, NODDI, BinghamNODDI, NODDIDA, NODDI-DTI, ActiveAx, AxCaliber, Ball&Sticks, Ball&Rackets, Kurtosis, Tensor, VERDICT, qMT, and relaxometry (T1, T2) models.Includes Gaussian, Offset-Gaussian and Rician likelihood modelsIncludes Powell, Levenberg-Marquardt and Nelder-Mead Simplex optimization routinesIncludes multiple (adaptive) MCMC sampling algorithmsSupports hyperpriors on parametersSupports gradient deviations per voxel and per voxel per volumeSupports volume weighted objective functionSupports adding your own modelsOffers Graphical, command line and python interfacesComputations are parallelized over voxels and over volumesPython and OpenCL basedFree Open Source Software: LGPL v3 licenseRuns on Windows, Mac and Linux operating systemsRuns on Intel, Nvidia and AMD GPU’s and CPU’s.LinksFull documentation:http://mdt_toolbox.readthedocs.ioProject home:https://github.com/robbert-harms/MDTHCP PipelineMDT 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.NODDIand it will autodetect the study in use and fit your selected model to all the subjects.Quick installation guideThe basic requirements for MDT are:Python 3.xOpenCL 1.2 (or higher) support in GPU driver or CPU runtimeLinuxFor Ubuntu >= 16 you can use:sudoadd-apt-repositoryppa:robbert-harms/cbclabsudoapt-getupdatesudoapt-getinstallpython3-mdtpython3-pipsudo pip3 install tatsuFor Debian users and Ubuntu < 16 users, install MDT with:sudoapt-getinstall python3python3-pippython3-pyopenclpython3-numpypython3-nibabelpython3-pyqt5python3-matplotlibpython3-yamlpython3-argcompletelibpng-devlibfreetype6-devlibxft-devsudo pip3 install mdtNote thatpython3-nibabelmay need NeuroDebian to be available on your machine. An alternative is to usepip3 install nibabelinstead.A Dockerfile and Singularity recipe were kindly provided by Ali Khan (on github: akhanf). These dockers come with Intel OpenCL drivers pre-loaded (e.g. for containerized deployment on a CPU cluster).
For example, to install using Docker usedocker build-fcontainers/Dockerfile.intel ..WindowsThe installation on Windows is a little bit more complex and the following is only a quick reference guide.
For complete instructions please view thecomplete documentation.Install Anaconda Python 3.*Install MOT using the guide athttps://mot.readthedocs.ioOpen an Anaconda shell and type:pip install mdtMacInstall Anaconda Python 3.*Open a terminal and type:pip install mdtPlease 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 seehttps://mdt_toolbox.readthedocs.org