Gallery#

Runnable Python examples, each with its code, what it prints and the images it makes. Each example is one script that runs on its own and is explained by its comments. The examples on synthetic data need nothing else; those on a brain use the simulated data of the ISMRM 2015 Tractography Challenge, which larmor.data downloads once (see Example data).

Preprocessing#

Denoising diffusion-weighted images and removing Gibbs ringing.

Denoising a DWI with MP-PCA

Denoising, then removing Gibbs ringing

Models#

Fitting the diffusion tensor (DTI) and diffusion kurtosis (DKI) models, and reading the maps they give.

Fitting DKI to a real brain

Fitting DKI to a synthetic signal

DTI of a simulated brain

Fitting DTI to a synthetic tensor

Spherical deconvolution and peaks#

Estimating fibre orientation distributions (FODs) by spherical deconvolution, and finding their peaks.

Fitting FODs to a synthetic signal

FODs, peaks and asymmetric FODs of a simulated brain

Finding the peaks of FODs

Tractography#

Tracking streamlines, writing tractograms, selecting bundles, track density images, structural connectomes and tract profiles.

Structural connectomes

Bringing an atlas region into diffusion space

Track density images

Whole-brain tractography of a simulated brain

Tracking streamlines through crossing bundles

Tract profiles: FA along a bundle

Writing a tractogram batch by batch

Files#

Reading and writing images, gradient tables and tractograms, and converting between file formats.

Reading and writing files

Reading MRIToolkit results from MATLAB files

Viewer#

Driving BrainViewer from Python. These examples draw off screen, with no window, and save screenshots of the views; they need the [viewer] extra and a GPU or a software renderer, but no display.