FLIRT/UserGuide - FslWiki
from hidden-facts.infoaces import fsl >>> from hidden-facts.infog import example_data >> > aw = fsl. Currently just a light wrapper around FLIRT, with no modifications . to 4D fMRI data flag: F mutually_exclusive: functional, reduce_bias, robust, padding t2_guided robust: (a boolean) robust brain centre estimation (iterates BET. This code registers linearly and non-linearly Diffusion Weighted Magnetic Resonance Images (DW-MRIs) by extending FLIRT (linear. Currently just a light wrapper around FLIRT, with no modifications import hidden-facts.info as fsl >>> from hidden-facts.infog import example_data . to 4D fMRI data argument: ``-F`` mutually_exclusive: functional, reduce_bias, robust, boolean) robust brain centre estimation (iterates BET several times) argument: ``- R``.
What is the format of the matrix used by FLIRT, and how does it relate to the transformation parameters? There are many reasons why a registration may not work well. Here is a general checklist of things to test and try in order to improve the registration results please do not post a query to the FSL email list about registration results until you have gone through this list: For 2D images single slices you must use one of the valid 2D degrees of freedom options or -2D and appropriate schedules from the command line - see below If there is large bias field slow intensity variation - especially near the end slices then try using fast to create a restored image one with no bias field and then register using the restored image.
If there are relatively small errors in some crucial region of interest e. To do this a weighting image must be made which has the value of 1. Using this weighting volume in either the GUI or command line registration calls should improve the fit in this region. This works in either registration mode where it is finding the transformation that aligns the input and reference images and also in applyxfm mode where it is applying a saved transformation to the input image.
Note that only in registration mode does it use the intensity information from the reference image. To apply saved transformations, the GUI ApplyXFM can also be used which provides the option of specifying the number of voxels and voxel size directly.
There are two main types of cost function: If you are registering two images of different modality then you must use an inter-modal cost function, whereas for images of the same modality either can be used, although the intra-modal options may be more accurate. Within each category there is not much to choose from - it is a practical, experience-based decision. The recommended options to try first are: If the scanner voxel size may have changed due to calibration shifts then it is appropriate to use 7 DOF instead of 6 or 4 instead of 3 to compensate for global scale changes.
Note that for difficult registrations there is a translation only schedule file which is effectively 3 DOF, but only includes x,y,z translations. This is useful for obtaining initial position estimates when matching small FOV to large FOV, and can then be further refined.
Transforming masks with FSL requires a little extra care. To steps are needed: Masks can be transformed from one space to another by using either one of the command line tools flirt or applywarp, or the ApplyXFM GUI. The threshold used with fslmaths should be set depending on the intended use of the output mask. These guidelines should help in determining the correct value to use: For example, if the mask is being used to quantify values within an ROI and it is important to not include contamination from surrounding areas, then a high threshold should be used.
Alternatively, if the mask is being used to define an exclusion ROI then it may be better to have a liberal border, which can be achieved with a low threshold. Thresholding and binarising is done by fslmaths in the second call. Registrations with different numbers of DOF or different combinations of parameters can only be achieved using schedule files and the command line version of flirt.
If the input images are 2D it is still necessary to use the -2D option as well. For more details see the section on available schedule files. Note that when the FOV is limited, but still 3D multiple slices then 2D or limited DOF transformations are normally required in order for the registration to be robust. In the registration stage it tries to find the transformation that best aligns the images, using a customised global optimisation technique that operates over multiple resolutions.JOHN BOYEGA VS GEEKS & GAMERS: THE HEAVY HITTER ON TWITTER!!
Once the best transformation has been found the original input image is resampled, using the transformation found previously, to match the 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.
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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.
DW-MRI registration in FSL
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. 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. 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.