How to coregister MRI data into MNI template-space
Table of Contents
I. Summary of Coregistration Steps for Neuroimaging:
A) Register functional data to anatomic data using a 6-parameter (rigid-body) fit.
B) Create a study-specific average anatomical image.
C) Register all subjects to the study-specific average image.
D) Register the average image to MNI template space.
E) Cumulate the transforms to bring functional data/results into MNI space.
The following discussion assumes you have acquired data for a standard fMRI study. The types of image data mentioned below include:
III. Suggested program calling sequences.
The following example is just that, an example. The example is tailored to the type and quality of data typically acquired in my lab, the Waisman Center Brain Imaging Lab. All of the software mentioned here is installed in our lab. There is a nearly infinite variety of approaches, especially as embellishments are added. The following provides a starting point using mostly FSL programs. It is a fairly standard approach which should not raise any flags for reviewers. FSL's "flirt" was selected to demonstrate a specific analysis pathway because it has a few well-defined, easy to use programs with a small number of input parameters.
The most important step is not explicitly included: visually inspect the results. You cannot do this often enough! Or more to the point, your graduate student cannot do this often enough!
III.A) Register functional data to anatomic data using a 6-parameter (rigid-body) fit.
III.A) Register coplanar T2 to hi-res T1:
flirt -cost normmi -dof 6 -omat /fullpath/T2_coplanar_2_T1.mat -in /fullpath/T2_coplanar.img -ref /fullpath/T1_hires.img -out /fullpath/T2_coplanar_2_T1.img
III.B) Create a study-specific average anatomical image.
James Gee presented a very elegant approach to this when he visited us in early October. AIR has a nice program that implements this, but it may be tricky to use. For the software we have at hand, template creation is a 2-step process.
III.B.1) Register each of the hi-res T1 images to a representative image.
The choice of "representative image" is frought with minefields. Common choices include:
Let's assume we chose "a single subject from the study, AC/PC-aligned", and the subject is sub_001. For each subject NNN, do the following:
flirt -cost normmi -dof 12 -interp sinc -omat /fullpath/subNNN_2_sub001ACPCaligned.mat -in /fullpath/T1_subNNN.img -ref /fullpath/T1_sub001_ACPCaligned.img -out /fullpath/T1_subNNN_2_sub001ACPCaligned.img
III.B.2) Create a sum-image of the registered images.
Following is a list of solutions, from least to most desireable. For me, most desireable implies the least number of questions provoked...
III.B.2.a) Spamalize can create a sum-image either via the GUI:
Commands->File Manip->ANALYZE->Sum (avg.) Files
or using the Spamalize program in a larger IDL program:
spam_sum_img_file (sums 3D volumes in a 4D image file)
spam_sum3d_img_file (Sum 2 or more 3D files to create a 3D sum-image file.)
III.B.2.b) In Python, you can use a module that transparently calls an IDL routine, spam_sum3d_img_file.pro, to sum two or more 3D files to create a 3D sum-image file:
III.B.2.c) In FSL, try iteratively using a Python script like this, where the filenames for the coregistered images (from step II.B.1) are in "file_list":
avwmaths file_list -add file_list sum.img
for filename in file_list[2:-1]
avwmaths sum.img -add filename sum.img
III.B.2.d) AFNI provides an easy tool for this:
3dmerge -gmean T1_sub001.img T1_sub002.img T1_sub003.img ... T1_sub999.img
If anyone has other suggestions please let me know.
Let's assume you chose one of these, and now have a sum-image compoased of brains which are AC/PC aligned and otherwise match sub001ACPCaligned.img. This will be designated:
III.C) Register all subjects to the study-specific average image.
For each subject (including sub001):
flirt -cost normmi -dof 12 -interp sinc -omat /fullpath/subNNN_2_sum.mat -in /fullpath/T1_subNNN.img -ref /fullpath/T1_sum.img -out /fullpath/T1_subNNN_2_sum.img
III.D) Register the average image to MNI template space.
flirt -cost normmi -dof 12 -interp sinc -omat /fullpath/T1sum_2_MNI.mat -in /fullpath/T1_sum.img -ref /apps/linux/fsl/etc/standard/avg152T1.img -out /fullpath/T1sum_2_MNI.img
III.E) Cumulate the transforms to bring functional data/results into MNI space.
For each subject, do the following:
III.E.1) Cumulate the transforms for coplanar->T1->sum->MNI
convert_xfm -omat /fullpath/subNNN_coplanar_2_sum.mat -concat /fullpath/subNNN_2_sum.mat /fullpath/T2_coplanar_2_T1.mat
convert_xfm -omat /fullpath/subNNN_coplanar_2_MNI.mat -concat /fullpath/subNNN_coplanar_2_sum.mat /fullpath/T1sum_2_MNI.mat
The order of the transform matrices is important, as is the order in which the commands are put together. Some trial and error is usually needed to get it correct.
III.E.2) Apply the cumulated transform to the EPI data:
applyxfm4D /fullpath/EPI.img /apps/linux/fsl/etc/standard/avg152T1.img /fullpath/EPI_2_MNI.img /fullpath/subNNN_coplanar_2_MNI.mat -singlematrix
III.E.2) Apply the cumulated transform to the T1 data:
flirt -applyxfm -init /fullpath/T1_subNNN_2_MNI.mat -interp sinc -out /fullpath/T1_subNNN_2_MNI.img -in /fullpath/T1_subNNN.img -ref /apps/linux/fsl/etc/standard/avg152T1.img
V. Comparison of Coregistration Software
VI. References for Coregistration (chronological order)
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Pietrzyk U, Herholz K, Fink G, Jacobs A, Mielke R, Slansky I, Wuerker M, Heiss WD, "An interactive technique for
three-dimensional image registration: Validation for PET, SPECT, MRI and CT brain studies", J. Nucl. Med., 35:2011-
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Woods RP, Grafton ST, Holmes CJ, Cherry SR, Mazziotta JC, "Automated image registration I: General methods and
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Woods RP, Grafton ST, Watson JDG, Sicotte NL, Mazziotta JC, "Automated image registration I: Intersubject
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VI. Questions or Comments? Contact Terry Oakes: troakes - at - wisc.edu
last updated 2008-09-09