Search Results for "tractography"

Camino is a free, open-source software package for simulation, analysis and reconstruction of Diffusion MRI data. Download the latest version of the code by clicking the "Code" tab and then "Download Snapshot". The green download button will get the latest major change (updated less frequently). For tutorials and more information, visit http://camino.org.uk. CAMINO IS A RESEARCH TOOL ONLY AND SHOULD NOT BE USED FOR CLINICAL PURPOSES. In downloading the code you explicitly agree to...

Diffusion tensor magnetic resonance imaging (DTI) is the currently unique in-vivo MRI technique for probing the distribution of neural pathways and micro-structures of biological tissues, thus promising for neurology and neuroanatomy research. However, the 3-Dimensional DTI tractography (DTT) suffers from the overwhelmingly dense resulting fiber tracts in its visualizations which substantially impedes effective exploration, tracts-of-interest (TOI) selection, for example, in the practical clinical diagnoses. This project aims at a light-weighted interface for interactive DTI visualization that exhausts the benefits of simple gadgets like boxes and spheres amounted with multiple-view modality and in-situ data-centric interactions.

While visualizing the diffusion tensor MRI (DTI) dataset using streamtubes, the unit (1x1x1) resolution in full volume seeding approach has been used as default parameters and the resulting tractography contains very dense geometries that causes visual complexity impeding the exploration of DTI visualizations. However, questions like "if the unit seeding resolution is optimal for visualizing either whole brain model or tract-of-interest (TOI)", "if not, what the optimal choice of seeding should be", and especially, "how quantitatively the choice of the fiber tracking parameter, seeding resolution, connects with the informativeness and meaningfulness of DTI visualizations". ...

Diviz is a tractography software based on python and dipy ,wx-widgets vtk and some image processing algorithms