Medical Image Analysis for MRI - useR! 2011 Tutorial

Tutorial: Medical Image Analysis

Brandon Whitcher, GlaxoSmithKline Clinical Imaging Centre, United Kingdom.
Jörg Polzehl, Weierstrass Institute for Applied Analysis and Stochastics, Germany.
Karsten Tabelow, Weierstrass Institute for Applied Analysis and Stochastics, Germany.

Abstract

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 introduce packages from the Medical Imaging task view and apply them to structural and functional MRI data. A step-by-step introduction will be given using medical imaging data that will be made available for the tutorial.

Goals

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.

Outline

Data Import/Export using oro.dicom and oro.nifti
Functional MRI using fmri
Diffusion weighted imaging (DTI and beyond) using dti
Dynamic Contrast-Enhanced MRI using dcemriS4

Intended Audience

R users (statisticians, medical physicists or researchers) with an interest in the quantitative analysis of neuroscience and/or oncology imaging data.

Prerequisites

Attendees will require a basic understanding of an interpreted programming language; such as R (preferred) or Matlab. Attendees will also require 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.

The tutorial will be interactive and involve the analysis of medical imaging data in real time. In order to participate attendees are required to bring their own laptop with R installed and the packages: oro.dicom, oro.nifti, fmri, dti, dcemriS4 and their dependencies. The data will be made accessible before the conference.

Please check here for up to date tutorial resources.