Quantitative measurement of change in brain size and shape (for example, in order to estimate atrophy) is an important tool for analysis of clinical imaging data. SIENA [34,35] (Structural Image Evaluation, using Normalisation, of Atrophy) is a fully automated method for finding temporal brain change, taking as input two MR images taken at different points in time, and giving as output a ``change'' image, along with a global estimate of percentage brain volume change. SIENA uses BET to segment brain from non-brain in each image, and also estimate the external surface of the skull in each. Next, the two brain images are registered with FLIRT, using the skull images to constrain scaling and skew; this corrects for changes in imaging geometry over time. Brain surface points (including at the ventricle edges) are then found with FAST, and the surface motion (between the two time points) estimated at these points, to subvoxel accuracy. The mean perpendicular edge motion across the entire brain surface can then be converted into a percentage brain volume change estimate. In a range of validations, SIENA has been shown to be accurate to better than 0.2% brain volume change.
SIENA has also been extended to a single-time-point method (SIENAX ) which estimates atrophy state rather than atrophy rate. SIENAX uses brain extraction and tissue-type segmentation to find brain volume and then brain-and-skull-based registration (similar to that used by SIENA) to normalise (for head size) to standard space (using the skull image for the scaling), to reduce inter-subject variability. SIENAX has been shown to be accurate to better than 1% error in normalised brain volume.
More recently , SIENA has been extended to allow voxelwise statistical analysis of brain change across multiple subjects. This allows, for example, local estimation of group-wise atrophy, or even local estimation or where atrophy correlates with other variables such as age, drug treatment or disease duration.
A similar method has been proposed for voxelwise statistical analysis of magnetisation transfer ratio (MTr) images . Because MTr is dependent on tissue type (as well as factos of interest, such as pathology), it is not possible to simply transform MTr images into standard space to carry out voxelwise statistics across subjects. Instead, the raw MTr image is split into (sparse) grey and white MTr images (using the output from FAST), which are dilated to ``fill in the gaps''. These are then transformed into standard space before being masked by standard grey and white matter masks and recombined. Thus for any given standard space grey (or white) voxel, the MTr value at this point is taken from the subject's MTr value at the nearest true grey (or white) voxel. Voxelwise cross-subject statistics are then possible.