TractoR: Magnetic Resonance Imaging and Tractography with R

Jonathan D. Clayden*, Susana Munoz Maniega, Amos J. Storkey, Martin D. King, Mark E. Bastin, Chris A. Clark

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalJournal of statistical software
Issue number8
Publication statusPublished - Oct 2011


  • diffusion
  • MRI
  • tractography
  • R
  • machine learning
  • image processing
  • magnetic resonance imaging


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