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TractoR: Magnetic Resonance Imaging and Tractography with R

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Author(s): Jonathan D. Clayden | Susana Muñoz Maniega | Amos J. Storkey | Martin D. King | Mark E. Bastin | Chris A. Clark

Journal: Journal of Statistical Software
ISSN 1548-7660

Volume: 44;
Issue: 08;
Date: 2011;
Original page

Keywords: magnetic resonance imaging | diffusion | tractography | image processing | machine learning | R.

ABSTRACT
Statistical techniques play a major role in contemporary methods for analyzing magnetic resonance imaging (MRI) data. In addition to the central role that classical statistical methods play in research using MRI, statistical modeling and machine learning techniques are key to many modern data analysis pipelines. Applications for these techniques cover a broad spectrum of research, including many preclinical and clinical studies, and in some cases these methods are working their way into widespread routine use. In this manuscript we describe a software tool called TractoR (for “Tractography with R”), a collection of packages for the R language and environment, along with additional infrastructure for straightforwardly performing common image processing tasks. TractoR provides general purpose functions for reading, writing and manipulating MR images, as well as more specific code for fitting signal models to diffusion MRI data and performing tractography, a technique for visualizing neural connectivity.

Tango Jona
Tangokurs Rapperswil-Jona

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