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Below we give a brief summary of research areas covered by the FMRIB Analysis Group, and some research-related resources that we have generated.

  • Structural MRI We have developed a number of robust tools for the analysis of structural MRI data. SUSAN is a tool for nonlinear noise reduction. BET segments brain from non-brain in structural and functional data. FAST segments a brain image into different tissue types, and provides bias field correction. FLIRT gives robust affine (linear) inter- and intra-modal registration, and FNIRT gives robust nonlinear registration. SIENA estimates brain volume and temporal volumetric change (e.g. for estimating brain atrophy).FIRST segments subcortical structures, modelling each with a surface mesh model.
    Associated Research Areas: Structural Modelling and AnalysisMultimodal Analysis 
  • Diffusion MRI We have developed new methods for analysing diffusion imaging data. FDT - FMRIB's Diffusion Toolbox - includes diffusion tensor fitting (including FA estimation), Bayesian fibre tract direction analysis and probabilistic tractography. Also part of this is TBSS - Tract-Based Spatial Statistics; this is an approach to carrying out voxel-based multi-subject statistics on diffusion (e.g. FA) data.
    Associated Research AreasDiffusion Modelling and AnalysisConnectivity ModellingMultimodal Analysis 
  • MRI Artefact and Physics Modelling We are also working on MR analysis research that is closely tied in with understanding the MR physics behind the data. FUGUE is a tool for unwarping EPI data on the basis of a B0 fieldmap. POSSUM is a project developing a full MRI data simulator from the basic physics, allowing the study of (e.g.) various MRI and FMRI artefacts.
    Associated Research AreasStructural Modelling and AnalysisDiffusion Modelling and Analysis;Functional MRI Modelling and Analysis 
  • Statistical Inference We have worked on spatial mixture modelling - a way of modelling a mixture of distributions (activation-nonactivation, different tissue types, etc.) with adaptive spatial classification regularisation, for use as an inference tool in FMRI etc.
    We are also working on cluster-like thresholding (Threshold-Free Cluster Enhancement), without having to define an initial cluster-forming threshold.
    Associated Research AreasBig Data, Imaging Genetics and StatisticsMultimodal Analysis