Tutorial: Medical image analysis for structural and functional MRI
Brandon Whitcher, GlaxoSmithKline, Clinical Imaging Centre,
Hammersmith Hospital, United Kingdom Pierre Lafaye de Micheaux, Department of Mathematics and Statistics,
Universite de Montreal, Canada Bradley Buchsbaum, Rotman Research Institute, Canada Jörg Polzehl, Stochastic Algorithms and Nonparametric Statistics, Weierstrauss Institute for Applied Analysis and Stochastics, Germany
The field of medical imaging covers a vast range of disciplines and
applications. There is a growing collection of open-source software
(OSS) solutions for all aspects of data management, processing,
analysis and visualization. This tutorial will provide four specific R
packages from the Medical Imaging Task View and apply them to
structural and functional MRI data.
By the end of the tutorial attendees will be able to:
Read and write medical imaging data in standard formats.
Manipulate and visualize medical imaging data.
Apply summary statistics and statistical models to medical imaging data.
Know where to find medical imaging resources in the R community.
Data Import/Export using AnalyzeFMRI by Pierre Lafaye de Micheaux
Data import/export using the Analyze format
Data import/export using the NIFTI format
Conversion from Analyze to NIFTI
From voxel indices to spatial coordinates
Quaternions, rotations and the like
Visualization of images using the GUI
Spatial/temporal ICA using the GUI
Functional MRI using Neuroimage by Bradley Buchsbaum
Data Structures for 3D and 4D images
Statistical modelling of fMRI data
Diffusion tensor imaging using dti by Jörg Polzehl
Diffusion tensor model and derived characteristics
Tensor mixture models
Dynamic Contrast-enhanced MRI using dcemri by Brandon Whitcher
Introduction to DCE-MRI and data visualization
T1 estimation from multiple flip angles
Estimating gadolinium concentration
AIF extraction and modelling
Kinetic parameter estimation
Overview of the Medical Imaging Task View and general discussion
Statisticians, medical physicists and researchers with an interest in
neuroscience and/or oncology are encouraged to attend.
Attendees should have a basic understanding of an interpreted
programming language, such as R (preferred) or Matlab. Attendees should
also have a basic understanding of statistical methodology, such as
summary statistics, hypothesis tests, linear regression, non-linear
regression, etc. Basic knowledge of medical imaging (specifically
MRI) is an advantage but not necessary.