SIENA - Structural Brain Change Analysis - User Guide
(Structural Image Evaluation, using Normalisation, of Atrophy)

SIENA Version 1.5

Introduction

SIENA is a package for both single-time-point ("cross-sectional") and two-time-point ("longitudinal") analysis of brain change, in particular, the estimation of atrophy (volumetric loss of brain tissue). SIENA has already been used in many clinical studies.

siena estimates percentage brain volume change (PBVC) betweem two input images, taken of the same subject, at different points in time. It calls a series of image analysis programs (supplied with FSL) to strip the non-brain tissue from the two images, register the two brains (under the constraint that the skulls are used to hold the scaling constant during the registration) and analyse the brain change between the two time points.

sienax estimages total brain tissue volume, from a single image, after registration to standard (Talairach) space. It calls a series of FSL programs: It first strips non-brain tissue, and then uses the brain and skull images to estimate the scaling between the subject's image and Talairach space. It then runs tissue segmentation to estimate the volume of brain tissue, and multiplies this by the estimated scaling factor, to reduce head-size-related variability between subjects.

Contributors: There have been many contributions of various kinds from members of the FMRIB analysis group and collaborators mentioned on the FSL page.

For more detail on SIENA and updated journal references, see the SIENA web page. If you use SIENA in your research, please quote the journal references listed there.


FSL Tools used

This section briefly describes the generic FSL programs that SIENA uses.

bet - Brain Extraction Tool. This automatically removes all non-brain tissue from the image. It can optionally output the binary brain mask that was derived during this process, and output an estimate of the external surface of the skull, for use as a scaling constraint in later registration.

pairreg, a script supplied with FLIRT - FMRIB's Linear Image Registration Tool. This script calls FLIRT with a special optimisation schedule, to register two brain images whilst at the same time using two skull images to hold the scaling constant (in case the brain has shrunk over time, or the scanner calibration has changed). The script first calls FLIRT to register the brains as fully as possible. This registration is then applied to the skull images, but only the scaling and skew are allowed to change. This is then applied to the brain images, and a final pass optimally rotates and translates the brains to get the best final registration.

fast - FMRIB's Automated Segmentation Tool. This program automatically segments a brain-only image into different tissue types (normally background, grey matter, white matter, CSF and other). It also corrects for bias field. It is used in various ways in the SIENA scripts. Note that both siena and sienax allow you to choose between segmentation of grey matter and white matter as separate classes or a single class. It is important to choose the right option here, depending on whether there is or is not reasonable grey-white contrast in the image.


Two-Time-Point Estimation

The script siena (see usage) is run simply by typing
siena <input1_fileroot> <input2_fileroot>
where the two input fileroots are analyze images without the .hdr or .img extensions.

siena carries out the following steps:

Run bet on the two input images, producing as output, for each input: extracted brain, binary brain mask and skull image. If you need to call BET with a different threshold than the default of 0.5, use -f <threshold>.

Run siena_flirt, a separate script, to register the two brain images. This first calls the FLIRT-based registration script pairreg (which uses the brain and skull images to carry out constrained regrstration). It then deconstructs the final transform into two half-way transforms which take the two brain images into a space halfway between the two, so that they both suffer the same amount of interpolation-related blurring. Finally the script produces a multi-slice gif picture showing the registration quality, with one transformed image as the background and edges from the other transformed image superimposed in red.

The final step is to carry out change analysis on the registered brain images. This is done using the program siena_diff. (However, in order to improve slightly the accuracy of the siena_diff program, a self-calibration script siena_cal is run first. This is described later in this section.) siena_diff carries out the following steps:

Note that all output is in the same directory as the input, so this must be writable by the user. The output files are (assuming the input images are called "A" and "B"): After completion, many of these files are deleted unless siena was called with the -d option.

Single-Time-Point Estimation

The script sienax (see usage) is run simply by typing
sienax <input_fileroot>
where the input fileroot is an analyze image without the .hdr or .img extension.

sienax carries out the following steps:

Note that all output is in the same directory as the input, so this must be writable by the user. The output files are (assuming the input image is called "A"): After completion, many of these files are deleted unless sienax was called with the -d option.

Manual Segmentation Correction

If you want to manually edit the segmentation image, to correct for any segmentation errors, you should follow the steps below. The following assumes the use of MEDx to edit images. The main steps described are for correcting sienax, followed by a description of how to adapt this procedure for siena.

If you want to amend the segmentation carried out within siena, carry out the above (without bothering with any of the statistics steps) and save the image, overwriting the original segmentation output. Now if you re-run siena the edited segmentation result will be used (as siena doesn't re-run segmentation if a segmentation output image is already in existence).


Copyright © 2000, University of Oxford. Written by S. Smith.