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An example of structural analysis (FIRST) showing shape changes in AD in key subcortical structures.
An example of structural analysis (FIRST) showing shape changes in AD in key subcortical structures.

Our research focus is on developing methodology and tools for the analysis of structural MRI data.  Examples of existing tools developed include linear and non-linear registration, tissue-type segmentation, subcortical structure segmentation, atrophy quantification, grey-matter change detection and brain extraction. 

In addition, MRI physics modelling is used to enhance and test the methodologies.  For example, we have developed methods that incorporate models for artefacts such as susceptibility-induced distortions and correct for these. This extends beyond structural MRI, with impacts in both diffusion and functional MRI.  To this end a general MRI simulation engine has been developed (POSSUM) that has applications to analysis and acquisition development in structural, diffusion and functional MRI. 

Increasingly, our work in this subgroup is joining with that of other groups to exploit cross-modal integration (XMI), such as in the segmentation/parcellation of subcortical and cortical regions using structural, diffusion and functional MR images.

FSL Tools Related to Structural analysis and Physics Modeling

BET FLIRT FUGUE
FAST FNIRT POSSUM
FIRST FSLVBM SIENA/SIENAX

Selected publications

Common genetic variants influence human subcortical brain structures

Journal article

Hibar DP. et al, (2015), Nature, 520, 224 - 229

Evaluating and reducing the impact of white matter lesions on brain volume measurements

Journal article

Battaglini M. et al, (2012), Human Brain Mapping, 33, 2062 - 2071

Bayesian model of shape and appearance for subcortical brain segmentation

Journal article

Patenaude B. et al, (2011), NeuroImage, 56, 907 - 922

Development of a functional magnetic resonance imaging simulator for modeling realistic rigid‐body motion artifacts

Journal article

Drobnjak I. et al, (2006), Magnetic Resonance in Medicine, 56, 364 - 380

Fast, automated, N‐dimensional phase‐unwrapping algorithm

Journal article

Jenkinson M., (2003), Magnetic Resonance in Medicine, 49, 193 - 197

Fast robust automated brain extraction

Journal article

Smith SM., (2002), Human Brain Mapping, 17, 143 - 155

Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

Journal article

Jenkinson M. et al, (2002), NeuroImage, 17, 825 - 841

ccurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis

Journal article

Smith SM. et al, (2002), NeuroImage, 17, 479 - 489

global optimisation method for robust affine registration of brain images

Journal article

Jenkinson M. and Smith S., (2001), Medical Image Analysis, 5, 143 - 156

Normalized Accurate Measurement of Longitudinal Brain Change

Journal article

Smith SM. et al, (2001), Journal of Computer Assisted Tomography, 25, 466 - 475

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

Journal article

Zhang Y. et al, (2001), IEEE Transactions on Medical Imaging, 20, 45 - 57