Camino (diffusion MRI toolkit)

Camino is a free, open source diffusion magnetic resonance imaging (MRI) image reconstruction toolkit written in Java. It includes all major diffusion reconstruction techniques such as diffusion tensor imaging (DTI), q-ball and PAS-MRI, as well as white matter tractography algorithms.

The name Camino is derived from the Spanish word Camino meaning route or track. Unfortunately, the same word in Italian means fireplace, which has little to do with diffusion image reconstruction.

The software is designed for Unix-like systems such as Linux, Cygwin (a Unix-style interface for Windows) and Mac OS X. The Java commands are wrapped with Bash shell scripts, which are documented by man pages. The various tools are designed to facilitate a pipeline approach to diffusion image processing.

Some of Camino's features are:


 * Reconstruction techniques including:
 * Fitting the Diffusion Tensor (DT) to diffusion-weighted MRI data.
 * Standard scalar measures, such as FA and Tr(D).
 * Fitting 2 and 3-tensor models.
 * Advanced reconstruction algorithms including RESTORE, q-ball, and maximum-entropy spherical deconvolution (including PAS-MRI).


 * Data synthesis
 * Generate synthetic data from standard diffusion tensors.
 * Generate synthetic data from full diffusion tensor images.
 * Generate synthetic data from other models of diffusion within restricting media.
 * Generate synthetic data by Monte-Carlo simulation of diffusion within restricting geometries.


 * Deterministic and probabilistic tractography (PICo), including:
 * Tractography and connectivity mapping with single and multiple tensor models.
 * Waypoints and multiple-ROI processing.
 * Output connection probability maps, or save streamlines in raw binary or OOGL (GeomView) format.
 * PAS-PICo and Q-Ball PICo

Diffusion MRI is an MR imaging modality which is capable of measuring the bulk diffusive motion of water in biological systems non-invasively. Although primarily used as a research tool, Diffusion MRI is slowly finding a niche in clinical environments. Since the invention of Diffusion Tensor Imaging (DTI), the field has grown rapidly to encompass a large number of applications, particularly in neuroimaging.
 * DT image warping
 * Preservation of principal directions (PPD)
 * Finite strain approximation
 * Useful sets of gradient directions for diffusion MRI acquisition protocols.
 * Electrostatic point sets
 * Ordered point sets for improved realignment and partial acquisition.
 * Full documentation via
 * Unix man pages
 * A variety of tutorials illustrating common tasks
 * Standard javadoc for the source code.

In order to address shortcomings of the original DTI technique, several novel reconstruction algorithms have been proposed capable of resolving more complex microstructure from diffusion scan data. Additionally, tractography techniques based on these reconstruction methods have multiplied in number and complexity. Camino is an attempt to unify this important body of work under a single framework and enable researchers to investigate these new techniques.

The Camino Team
Camino is developed and maintained by a team of researchers at University College London's Centre for Medical Image Computing. The Camino team is led by Daniel Alexander. Other Camino developers are Phil Cook, Matt Hall, Kiran Seunarine, Shahrum Nedjati-Gilani, Bai Yu, Phillip Batchelor and Laura Panagiotaki.