[FSL] [TitleIndex] [WordIndex

FSL Tools used

This section lists 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.



SIENA - Two-Time-Point Estimation

Usage

A default SIENA analysis is run by typing:

siena <input1> <input2>

The input filenames must not contain directory names - i.e. all must be done within a single directory.

Other options are:

-o <output-dir> : set output directory (the default output is <input1>_to_<input2>_siena)

-d : debug (don't delete intermediate files)

-B "bet options" : if you want to change the BET defaults, put BET options inside double-quotes after using the -B flag. For example, to increase the size of brain estimation, use: -B "-f 0.3"

-2 : two-class segmentation (don't segment grey and white matter separately) - use this if there is poor grey/white contrast

-t2: tell FAST that the input images are T2-weighted and not T1

-m : use standard-space masking as well as BET (e.g. if it is proving hard to get reliable brain segmentation from BET, for example if eyes are hard to segment out) - register to standard space in order to use a pre-defined standard-space brain mask

-t <t>: ignore from t (mm) upwards in MNI152/Talairach space - if you need to ignore the top part of the head (e.g. if some subjects have the top missing and you need consistency across subjects)

-b <b>: ignore from b (mm) downwards in MNI152/Talairach space; b should probably be -ve

-S "siena_diff options" : if you want to send options to the siena_diff program (that estimates change between two aligned images), put these options in double-quotes after the -S flag. For example, to tell siena_diff to run FAST segmentation with an increased number of iterations, use -S "-s -i 20"

-V                    : run ventricle analysis VIENA

-v <mask image>       : optional user-supplied ventricle mask (default is $FSLDIR/bin/MNI152_T1_2mm_VentricleMask)

What the script does

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 registration). 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. (In order to improve slightly the accuracy of the siena_diff program, a self-calibration script siena_cal, described later, is run before this.) siena_diff carries out the following steps:

The files created in the SIENA output directory are:

Ventricular extension - VIENA



SIENAX - Single-Time-Point Estimation

Usage

A default SIENAX analysis is run by typing:

sienax <input>

The input filename must not contain directory names - i.e. all must be done within the current directory.

Other options are:

-o <output-dir> : set output directory (the default output is <input>_sienax)

-d : debug (don't delete intermediate files)

-B "bet options" : if you want to change the BET defaults, put BET options inside double-quotes after using the -B flag. For example, to increase the size of brain estimation, use: -B "-f 0.3"

-2: two-class segmentation (don't segment grey and white matter separately) - use this if there is poor grey/white contrast

-t2: tell FAST that the input images are T2-weighted and not T1

-t <t>: ignore from t (mm) upwards in MNI152/Talairach space - if you need to ignore the top part of the head (e.g. if some subjects have the top missing and you need consistency across subjects)

-b <b>: ignore from b (mm) downwards in MNI152/Talairach space; b should probably be -ve

-r: tell SIENAX to estimate "regional" volumes as well as global; this produces peripheral cortex GM volume (3-class segmentation only) and ventricular CSF volume

-lm <mask>: use a lesion (or lesion+CSF) mask to remove incorrectly labelled "grey matter" voxels

-S "FAST options" : if you want to change the segmentation defaults, put FAST options inside double-quotes after using the -S flag. For example, to increase the number of segmentation iterations use: -S "-i 20"

What the script does

sienax carries out the following steps:

The main files created in the SIENAX output directory are:


Voxelwise SIENA Statistics

We have extended SIENA to allow the voxelwise statistical analysis of atrophy across subjects. This takes a SIENA-derived edge "flow image" (edge displacement between the timepoints) for each subject, warps these to align with a standard-space edge image and then carries out voxelwise cross-subject statistical analysis to identify brain edge points which, for example, are signficantly atrophic for the group of subjects as a whole, or where atrophy correlates significantly with age or disease progression.

In order to carry out voxelwise SIENA statistics, do the following:


CategorySIENA


2017-04-20 13:30