FLIRT/FAQ - FslWiki
Two main tools: FNIRT & FLIRT. (FMRIB's Non-Linear/Linear Image. Registration Tool). Both tools used by FMRI and Diffusion tools. (FEAT, MELODIC & FDT). Oct 4, FLIRT (FMRIB's Linear Image Registration Tool) is a fully automated robust and accurate tool for linear (affine) intra- and inter-modal brain. Structural image (animesost.info) Linear Registration (FLIRT) Nonlinear Registration (FNIRT) Standard Template: e.g. MNI (animesost.info) Linear.
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.
- FMRIB Software Library v6.0
- Registration Practical
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.
Note that it does not perform any search in 2D mode, and cannot deal with 2D to 3D registrations. Interpolation Methods This includes Nearest Neighbour, a family of Sinc-based methods three window types - rectangular, Hanning and Blackman with configurable window width, and spline a highly efficient method, with similar output characteristics to sinc.
The interpolation is only used for the final transformation and in applyxfmnot in the registration calculations. In addition, there is the BBR cost function which utilises a segmentation of the reference image to define a boundary, and it is the intensity differences in the input image, across the transformed boundary, that contribute to the cost.
The pre-requisites to use this method are: This script will either use an existing white-matter segmentation of the structural image, or create one itself, to define a white-matter boundary. We recommend that the structural image is bias-corrected separately beforehand if there is obvious bias field present. The script is also capable of using fieldmaps to perform simultaneous registration and EPI distortion-correction.
The inputs echospacing and pedir both refer to the EPI image not the fieldmap and are the same as required for FEATbut be careful to use the correct units. It can read and write ascii 4x4 matrices. In addition, it can be used to concatenate two transforms using -concat with the second transform or to find the inverse transformation using -inverse.
If the option -mm is used then both input and output coordinates will be in mm coordinates, otherwise with -vox both coordinates will be in voxel coordinates. For conversion between voxel and mm coordinates it is necessary to use either img2stdcoord or std2imgcoord see below. Note that the source coordinates can either be input via a file or via a pipe and for the latter the "-" symbol is used as the filename.
The format in either case is three numbers per line, space separated. To avoid this use the pipe input format or suppress the final line: The coordinates for the source image can be either in voxel coordinates default, or by explicitly using -vox or in mm coordinates using -mm. This utility, and std2imgcoord are therefore useful for converting between voxel and mm coordinates within the same image, as well as for mapping coordinates between spaces.
Converting from voxel to mm coordinates within the same image can be done with the command: It works the same way but transfers coordinates from "standard space" to the other image IMG space. It can also convert between mm and voxel coordinates within the same image. See the entry on img2stdcoord above. Sinc interpolation is used internally. Appropriate options -applyxfm and -init to FLIRT can be used to apply transformations to single volumes with other interpolation methods.
A reference volume is required in order to determine the matrix size and FOV for the final volume. That is, it compares two transformations normally two possible registrations of the same volume pair to see how much they differ.
This is useful to compare alternative registrations. Check what the results look like in FSLeyes: Note that the "lost" voxels will have fieldmap values filled in by extrapolating from the nearest voxel inside the mask, which is quite accurate for fieldmaps as the field variations are quite smooth you cannot see this in these images as it happens in the later processing.
For this we need the phase difference image, the brain extracted and eroded magnitude image and the difference in the echo times of the fieldmap acquisitions. This latter value is 2. Therefore it is important to record this echo time difference when you scan your scanner operator will be able to give you the value, and although it can usually be determined later on, it is much easier to record it at the time when the scanner operator is present.
Note that these images come from a Siemens scanner. From the drop-down list in the top right corner select Preprocessing. We now need to set up the GUI to run our registration with unwarping. Once this is done, click on the file browser for the Output directory and make sure the directory is set to somewhere in your home directory.
Now go to the Pre-stats tab and click the B0 unwarping button. Note that all other parts of this tab e. Now go through and populate the relevant parts: Go to the Registration tab and click on the Main structural image button.
Make sure the options underneath are set to Normal search and BBR. This reference image is part of FSL. We will not select the Nonlinear button in the Standard space section for this part of the practical in order to save time, but you normally would use this setting as it gives the best results. 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.
FLIRT - FslWiki
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.
FMRIB Software Library
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. 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.
Finally, de-select or remove the highres2standard images within the FSLeyes session.