I have been given a 3D image volume after normalization in MNI space. The dimensions of the image volume are (in MNI space, 3.125x3.125x6 mm voxels, 50x61x23 voxels) as per the README file.
It is a correlation functional image volume in text format. I would like to display this image volume overlaid on an anatomic image (the standard MNI anatomic image, since I did not get a separate anatomic volume). Is there a good way of doing this?
I looked at this here:
and it is fairly straightforward in some sense, but I can not figure out how to put in the voxel dimensions, and how to then display using an overlay of the MNI anatomic image.
Many thanks for any help you can provide!
To use to3d for reading in text files, consider this example where we dump some numbers as text into a file from a dataset and then read them back in with to3d.
resample the dataset to a very coarse dataset just for an example here
3dresample -prefix TT_N27_junk_8.nii.gz -dxyz 8 8 8 -input TT_N27+tlrc
dump the values of the dataset out to a text file
3dmaskdump -noijk TT_N27_junk_8.nii.gz > TT_N27_junk_8_ascii.txt
get the number of rows, columns and slices to use for to3d
read the data back in. The geomparent gives the orientation and origin of the data
to3d -prefix TT_N27_junk_copy.nii.gz -geomparent TT_N27_junk_8.nii.gz “3As:20:24:19:TT_N27_junk_8_ascii.txt”
Also consider using 3dUndump to do this (with data in the form of “x y z value” or “i j k value”). See this recent post for more information:
Thank you for your response and your example!
It works as advertised.
However, from what I understand, in your example, you use the -geomparent on TT_N27_junk_8.nii.gz to get the slice thickness, etc for the file to be created. I do not have this file. I only have my ascii files and I know that my values are correlations and of dimensions 50 x 61 x 23 with voxel dimensions : 3.5 x 3.5 x 6 (mm). How do I get this information in?
Let me know if I am not explaining myself.
Many thanks and best wishes,
You can leave out the geomparent option then and supply “-xFOV 120L-R -yFOV 120P-A -zFOV 64S-I” where you would replace the numbers with half the distance in each direction (in your case 25,30.5,11.5)and putting in the appropriate orientation for Left-Right, Posterior-Anterior and Superior-Inferior, where the directions could be reversed, depending on the orientation of the dataset.