FMRIB Software Library v Created by the Analysis Group, FMRIB, Oxford, UK. FSL is a comprehensive library of analysis tools for FMRI. FAST (FMRIB's Automated Segmentation Tool) segments a 3D image of the . use prior probability maps for initialisation (must specify FLIRT. Two main tools: FNIRT & FLIRT. (FMRIB's Non-Linear/Linear Image. Registration Tool). Both tools used by FMRI and Diffusion tools. (FEAT, MELODIC & FDT).
The result is a registered image which will be saved to the location specified in the Output image box. The second mode of operation is a two stage registration which takes an input Low res image and two target images.
It initially registers the low res image to a High res image and then registers this high res image to the final Reference image. The two resulting transformations are concatenated and then applied to the original low res image to create an Output image that is a version of the low res image transformed resliced to the reference image space.
For 3D to 3D mode the DOF can be set to 12 affine9 traditional7 global rescale or 6 rigid body. Advanced Options The four categories of options are: Search - select the angular range over which the initial optimisation search stage is performed. Cost Functions - select the desired cost function from a range of inter- and intra-modal functions. Interpolation - select the interpolation method to be used in the final reslice transformation it is not used for the estimation stage - trilinear interpolation is always used for the estimation of the transformation.
The options for this final interpolation method are: If Sinc is chosen, further window parameters type of windowing function and window width can also be specified. The weighting images must be the same size as the image they are weighting e. Therefore, by setting weights to zero, some areas of the image can be effectively ignored, which is useful in masking out pathologies so that they do not affect the registration.
FLIRT/UserGuide - FslWiki
In this way very accurate registrations can be made between pathological and "normal" images. This cannot be achieved by masking the images prior to registration, as that induces artificial boundaries which bias the registration. Furthermore, some areas can be given extra weighting such as the ventricles so that the registration is most accurate near these structures, but still uses information from the rest of the image e. A description of the available GUI interfaces is also available.
For each of the programs described here, a usage message which describes the full list of available options can be obtained by running the program with no options. See also the list of common example usages.
The main options are: For these usages the reference volume must still be specified as this sets the voxel and image dimensions of the resulting volume. Cost Function Weighting Weighting volumes can be specified using -refweight, -inweight or both.
Note that this is different from masking the original images, as masking introduces artificial boundaries whereas weighting does not. OK, that's everything we need to register the functional image to standard space. So double-check that the Pre-stats and Registration tabs look correct and when you are happy press the Go button at the bottom. This should start up a web browser showing the progress of FEAT - although it may take a minute for this to appear.
So go to the Pre-stats tab and de-select the B0 unwarping button. Everything else stays the same, and once you are happy with all the setting press the Go button again. Change to this directory, and open the webpage report for the registration: Do these registrations seem accurate to you? Note that you should not trust borders with signal loss areas as these are not true anatomical boundaries but artificial borders.
It is also highly recommended to use FSLeyes to look in more detail. We can look at each of the two registration steps separately functional to structural, and structural to standardbut remember when these two steps are combined to produce a functional to standard transformation, the functional image is only resampled ONCE into standard space. Let's first look at the initial registration step functional to structural in FSLeyes. Load the structural image into FSLeyes using the following command: Change the colour map of this wmedge image, by selecting in the image list at the bottom left, and then selecting Red in the colour map drop down list at the top of the FSLeyes window.
Click around the image to see where the registration is particularly good as the red edges derived from the structural should align with the changes the in greyscale intensities of the functional image. Feel free to look at other images in this reg subdirectory or in the unwarp subdirectory inside this.
Now open another FSLeyes session without closing the old one from the terminal to view the second registration step structural to standard: Use the same FSLeyes tools to check the registration of the structural image to the standard image. Leave both of these FSLeyes sessions open for the moment, as we are now going to compare the registrations you just ran to these given examples. Can you spot any noticable differences compared to the example registration webpage report?
We will now compare these registrations carefully using FSLeyes. Go back to the FSLeyes window where you were looking at the first registration step functional to structural.
How does this registration compare to the original? It should be identical, or at least very, very similar. Use the FSLeyes tools you practised earlier to compare the registrations with and without fieldmaps Which areas likely benefit the most from fieldmap distortion correction? The prefrontal cortex often suffers from large distortions and drop-out, which cannot be corrected for using fieldmaps due to its proximity to sinuses etc.
The thalamus is very central within the brain, and is not particularly susceptible to distortions in EPI imaging. Now go to the FSLeyes window where you were looking at the second registration step structural to standard.FSL Tutorial 1: Introduction to FSL
Now add to FSLeyes the second brain example we have provided, using both linear and non-linear registration to the standard space: Can you think why this might have been the case? This brain registered pretty well using linear only registration, although it was slightly improved with non-linear registration. This brain was from a young adult, whose brain was a much closer match to the MNI template it was registered to.
This brain was very badly registered to the standard using linear only registration.
This brain was from an older adult with much larger ventricles and local brain differences to the MNI template, hence linear registration performed very poorly. Note that this option is not used for multi-channel segmentation. Now select the Output image s basename. Output images will have filenames derived from this basename. For example, the main ouput, the Binary segmentation: If multi-channel segmentation is carried out, some of the optional outputs will have basenames derived instead from the input names but into the directory of the outputbasename.
For example, the main segmentation output will be as described above, but the restored images one for each input image will be named according to the input images. Now choose the Number of classes to be segmented.
FMRIB Software Library - Wikipedia
Also, if you are segmenting T2-weighted images, you may need to select 4 classes so that dark non-brain matter is processed correctly this is not a problem with T1-weighted as CSF and dark non-brain matter look similar.
The various output images are: A non-binary partial volume image for each class, where each voxel contains a value in the range that represents the proportion of that class's tissue present in that voxel.
This is the default output. This is the "hard" binary segmentation, where each voxel is classified into only one class.