Runs FitFullModelSampleAllTracts to find the optimal parameters and fit model with the optimal parameters.
Classifies streamlines into known anatomical tracts.
Fit MAPMRI model (with optional Laplacian regularization). Generates rtop, lapnorm, msd, qiv, rtap, rtpp, non-gaussian (ng), parallel ng, perpendicular ng. Either the laplacian or positivity or both must be set to True. (dipy_fit_mapmri)
Will take a dt6 and create tensor (FA,MD,AD,RD) and Westin Shape Indices (cl, cp, cs) nifti images from a dt6.mat structure.
Runs vistasoft/dtiInit to preprocess and register dwi to anat/t1
Tensor reconstruction and compute DTI metrics using weighted least-squares (dipy_fit_dti)
Provides useful information about different files used in medical imaging.
Reconstruction of the diffusion signal with the Tensor model (dipy / reconst_dti)
Runs FitFullModelSampleAllTracts with 665 parameter set and find the optimal parameters and fit model with the optimal parameters
This application will create a mask nifti file of the white matter of the T1 image using Freesurfer's aparc+aseg.mgz parcellation.
This app will fit the Neurite Orientation Dispersion and Density Imaging (NODDI; Zhang et al, 2012) model to multi-shell, normalized DWI data using the Accelerated Microstructure Imaging via Convex Optimization (AMICO; Daducci et al, 2015) toolbox. Requires normalized, multi-shell DWI data (including bvals and bvecs) and an optional brainmask of the DWI. Will output the four NODDI output files: neurite density index (ndi), orientation dispersion index (odi), isotropic volume fraction (isovf), and the directions (dirs).
This app performs automatic example-based tract segmentation using the Linear Assignment Problem (LAP) algorithm having multiple examples.
Code to convert wmc to trk and resample with the given step size.
Brainlife App (wrapper) for dMRI based fiber tracking between ROIs using parallel transport tractography
Datasets and derivatives generated for Brad Caron's Qualifying exams.
A quality check application for tractography, segmentatations, and LiFE structures. For the whole brain tractogram, provides a number of statistics associated with average streamline characteristics (i.e. count, volume occupied, avg length, length distribution). Does the same for the positively weighted streamlines of an FE structure if input. If a classification structure is input, provides a number of macrostructural statistics like stream count, volume, avg length, whole brain count/volume proportion, etc).
Code of Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation.
This app will generate endpoint maps for all tracts in an input classification structure. User can specify what sort of decay/smoothing algorithm can be used (or none).
This is a brainlife.io wrapper app for mbaComputeFibersOutliers algorithm. It takes an existing tract classification and prune classified fibers that are unlike other fibers within the same tract.
This App takes tract masks and convert them to series of numerical values that chracaterizes each masks. The numerical values are generated from the flattened output of 3D convolutional layers of the model trained to classify tract names. Output values could be used as a "shape signature" and compared against other similar shaped tracts.
This app segments tracts using pairs of (potentially multiple) specified atlas regions to output a set of tracts. Instead of running this App, we recommend running "ROI Generation" App first to convert the Atlas to ROI, then run "WMA Tract Segmentation with ROIs" to segment tracts using those ROIs.
This app segments tracts using pairs of (potentially multiple) rois from an input directory to output a set of tracts.