Dmipy

Dmipy implements modular multi-compartment (MC) modeling of diffusion MRI (dMRI) to estimate tissue microstructural features by representing measured diffusion signals as linear combinations of distinct tissue compartments.

Key Features:

• Modular MC-Modeling: Building-block architecture to construct MC-models representing microstructural characteristics such as diffusivity, orientation, volume fractions, axon orientation dispersion, and axon diameter distribution.
• Versatile Acquisition Support: Compatibility with PGSE-based dMRI schemes including single-shell, multi-shell, multi-diffusion time, and multi-TE acquisitions.
• Comprehensive Model Library: Implementations of IVIM, AxCaliber, NODDI(x), Bingham-NODDI, spherical mean-based SMT, MC-MDI, and single- and multi-tissue CSD.
• Advanced Modeling Techniques: Support for Constrained Spherical Deconvolution with voxel-varying kernels and single-shell 3-tissue CSD via parameter cascading between models.

Scientific Applications:

• Neuroimaging microstructure characterization: Quantitative characterization of brain tissue microstructure from dMRI data.
• Studies of neurological conditions and development: Application of MC-modeling to investigate neurological conditions, developmental processes, and brain structure and function.

Methodology:

Construction and fitting of modular multi-compartment models to PGSE-based dMRI data, representation of signals as linear combinations of tissue compartments, parameter estimation and recovery, parameter cascading between models, and Constrained Spherical Deconvolution with voxel-varying kernels.