[Freesurfer] problem with registration in preprocessing

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Note that all other parts of this tab e. This includes fieldmap-based unwarping and the preparation of these fieldmap scans. FNIRT has the potential to work much better but also the potential for significant errors.

FreeSurfer creates the matrix that FSL is trying to invert. If set to spline the format will be a 4D file of spline coefficients. We would then want to transform the putamen ROI into the functional space of each of those subjects.

Fraternity and Sorority Life

Registration Practical In this practical you will explore each of the registration steps within a standard two-step registration for functional images. We will first learn to use the registration tools within the FEAT GUI. Then we will see how to apply and invert transformations. Being able to achieve precise registrations is CRUCIAL for structural, functional and diffusion image analysis. If registrations are not accurate, further statistics at a structural or group level will not be accurate. Contents: Register functional EPI images to the individual structural image and to standard space using the FEAT GUI. This includes fieldmap-based unwarping and the preparation of these fieldmap scans. Calculate and apply transforms and inverses from linear and non-linear registration as output from FEAT. The same principles apply for registration in diffusion imaging, and the similarities and differences are highlighted here. Include an extra registration step to register low-contrast multiband data. Many of you may not need to complete this section, but for those of you collecting multiband data this may be a helpful exercise. An extra high-contrast image is added as an intermediate step when registering between a functional EPI image and a structural image. This involves multi-stage registration and fieldmap-based unwarping, and requires the images to be suitably prepared. Registration in other FSL GUIs e. Preparing the structural image We need to perform brain extraction on the structural image prior to using it for registration as explained in the lecture. To do this run BET on the image STRUCT. Check the results with FSLeyes. The second image is proportional to a map of the distortions. Have a quick look at these with FSLeyes. We will need to use both magnitude and phase images. Processing the fieldmap magnitude image We will start by brain extracting the first magnitude image. We will then erode this image shaving off one voxel from all edges as this image contains noisy partial volume voxels in the phase difference image near the edge of the brain you will see this below, whereas in practice you would look first in order to decide whether to erode or not. 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. Using the FEAT GUI With the fieldmap processed and the structural image brain extracted we are now ready to use the FEAT GUI for registration. Start the FEAT GUI by typing Feat in the terminal. 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. To begin with click on the Select 4D data button and select the image FUNC. 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. 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. Now go back to the FEAT GUI, and we are going to run a comparison registration without fieldmap unwarping. 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. While you wait The FEAT jobs will take about 15 minutes to finish. 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 standard , but 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: cd reg fsleyes highres. 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 : fsleyes standard. In this case non-linear registration FNIRT was used after affine linear transformation FLIRT for maximum accuracy. 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. Once the FEAT job is finished Firstly, look at your webpage reports for the registrations you ran. 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. This registration step was done linearly using FLIRT rather than nonlinearly using FNIRT. Compare the linear and linear+non-linear versions of this step. 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. You may need to rename these within FSLeyes to avoid confusion. The registration without fieldmap correction or non-linear registration will be markedly worse than the original registration, as both sub-optimal registration steps have now been concatenated into one step. How many times do we resample the image when we register from functional to standard space, and why? Twice - we want to make sure the brain is accurately aligned to the structural image and then to the standard template, so each registration and resampling must be performed independently. While the registration steps are performed separately, the transformation files are concatenated so that we only have to resample once into standard space. Accuracy of registration steps is maintained, while resampling degrades the image quality and should be minimized. Never - we do all of our analysis in native space, and registration is just for display purposes. Whilst some analysis is conducted in native space, many require us to resample our image into a different space, such as a template space when we want to compare individual brains in a group analysis. Once - we want to resample only into the standard space to minimize image degradation. The individual registration steps are concatenated to move from functional to structural to standard space all in one go, with resampling only happening at the very end into the standard space. The objective of this section of the practical is to become familiar with applying transformations, as well as their inverses, to move masks or images between different spaces. We will be working with the files from the Feat analysis in the previous part of the practical, however what we are doing is not restricted to functional analysis. FDT outputs similar files when analysing diffusion datasets, and the same files affine transformation matrices and warp fields are output by the fundamental tools, FLIRT and FNIRT, when doing any structural analyses. For now we will not look at the contents of these files see the exercises below for more about this but instead we will explain what each of them means. The naming convention is always from the input space to the reference space or source to destination if you prefer. For example, the file highres2standard. We have both of these because it is necessary to initialise all non-linear registrations with an affine registration that gets the head in roughly the right position and scaling to allow the non-linear registration to work well. When you want to use a transformation between these spaces, you would generally go for the warp field when it exists and ignore the initial affine registration. Note that the warp fields include the affine transformation as part of them, so you don't need to use both. Various combinations of transformations exist in this directory e. Note that in a diffusion analysis, run via FDT, the three spaces are called diff, str and standard, and all combinations of transformations are provided. This is because of the fieldmap-based distortion correction, which is not just a linear affine registration and so must be represented as a warp field. Creating an example mask Now let's make a mask in the standard MNI space so that we can transform it into the other spaces and use it to calculate some ROI quantities. This will open up a new panel along the bottom of the FSLeyes GUI that allows you to look at anatomical information from the atlases included with FSL. We can use these atlases to create a mask image we will choose the left hippocampus from the Harvard-Oxford subcortical atlas, but there are lots of possibilities. Click the Atlas search tab. Type hip into the search box at the top of the right section - note that as you type, all atlases, in the atlas list to the left, which contain a structure that matches the search term, are highlighted. Click the checkbox next to the Harvard-Oxford Subcortical Structural Atlas to add it as an overlay. Now we are going to select the voxels in the left hippocampus. An alternative method to create a mask will be discussed in the practical. Two new toolbars will be added to the FSLeyes window. Now we are able to select regions of voxels according to their intensity. A new image will be added to FSLeyes, with ones in the left hippocampus, and zeros everywhere else. Click on the save button alongside the mask image in the overlay list, and choose an appropriate name e. To make life a lot easier later on make sure you remove all spaces from the filename. This is a general rule to stick with, as spaces within filenames will almost always cause problems and are easily avoided. Inverting a transform We want to transform the mask we just made into the functional space in order to calculate an ROI value e. As feat does not create the required warp field, we need to create it ourselves from the transformations that it does provide. In this case we do not want to undo the distortion correction i. As this warp transforms from standard space to structural we will stick on the final transformation to functional space below , we specify highres. Note that the values at the edge of the mask lie between 0 and 1. Thresholding the Mask In order to obtain a binary mask where each voxel has a value of either 0 or 1 we need to threshold and binarise the transformed mask. This is easily done with fslmaths but the threshold used is arbitrary. If a high threshold is chosen e. This is often desirable when trying to make sure that only the structure of interest is included, but it might end up with the mask being quite small. So sometimes a threshold near 0. Or sometimes a mask is needed that does not leave out any of the structure, in which case a low threshold e. We also binarise the mask with the -bin command in fslmaths, to make all voxels within the mask have a value of 1. In this case we will choose a high threshold in order to get a mask where we are very confident that each voxel in the mask is within the hippocampus. This can be done with: fslmaths LeftHippMaskFunc -thr 0. Using the Mask There are many possible ways in which a mask can be used e. This is done with the command: fslmeants -i.. We won't do anything with this for now, but such calculations can be very useful in all sorts of situations. The main point of this exercise was to see how to transform your own masks or images, as the only difference is skipping the thresholding and binarising steps between spaces. For functional studies you can often do similar things with the tool Featquery but it is not as flexible and generally useful as being able to process things yourself. If we were interested in the functional signal in for example the amygdala within the fMRI data in a particular subject, which space would we transform a standard mask of the amygdala into for further processing? The structural space - we want to make sure we get the most accurate signal from the data, and this is when the image is in structural space Incorrect. We perform most of our fMRI analysis in distortion-corrected functional space. The accuracy does not improve in structural space, as the data was acquired in functional space. The distortion-corrected functional space - we do our task analysis in functional space, once it has been corrected for distortions Correct! The native functional space - we do our task analysis in functional space, without any registrations applied Incorrect. We perform most of our fMRI analysis in distortion-corrected functional space. A new functional imaging technique that is becoming more popular is 'multiband imaging'. Multiband imaging allows people to acquire functional images more quickly, allowing more volumes in the functional 4D file for more statistical power, or shortening functional scanning times. Many of you might be using multiband data for your own imaging projects, so here we have an optional section to demonstrate how we can optimise registration of multiband scans. Because multiband images are acquired very quickly, they can have low contrast between grey and white matter. Therefore, to aid registration we can add an additional high-contrast image to our registration between functional and standard spaces. This high-contrast image is usually one of the first few scans 'pre-saturation' scans acquired in a multiband sequence, which are usually discarded before functional analysis. In every other way it matched the functional scans used in the analysis, so it is a perfect match to the FUNC. Note that registration of multiband data does not require this step to run, but it is recommended to use this intermediate contrast image for improved registration of multiband images. The second transforms from the high-constrast volume to highres. Now go to the Pre-stats tab and click the B0 unwarping button. All other parts of this tab can be left with their default settings. In the Registration tab, now click on the Expanded functional image button. Note that registration does not require this step to run, but it is recommended to use this intermediate contrast image for registration of multiband data. Make sure you turn the non linear option OFF so that you save some time here. Check all your options and press the Go button at the bottom again. This should start up a new web browser tab with the progress of your new FEAT run. Note how the extra registration step is now included in the report. Feel free to load any of the registration steps into FSLeyes to explore these in more detail.

Note that the pan fields include the affine transformation as part of them, so you don't need to fsl registration problems both. Because multiband images are acquired very quickly, they can have low contrast between grey and white matter. If it was not used the back-projected skeleton may no longer be continous. It should also be social that the resolution will always be an integer multiple of the voxel size. Is there anything else I should be trying to see if I can get my heads on straight. Please refer to the following websites for updated installation methods. Many argue that solo body 6 degree of freedom is most appropriate for registering a T1 with a diffusion scan, for example; I generally follow that convention.