Dear Blaine,
I'm trying to motion correct a T1w EPI time-series, where the acquisition order of the slices is shuffled in each repetition, such that in each volume the slices have slightly different contrasts. As you can appreciate in the gif below, this change in contrast leads to an apparent "motion", characterized by a moving "nulling/dark band".
![t1wEPI](https://user-images.githubusercontent.com/7881842/109561395-1a0d2f00-7aab-11eb-9a3b-9c02e95b9c8c.gif)
I've tried using ANTs with MI as a cost function for motion-correction, but my results are never as accurate as I would like them to be. I suppose this is because methods based on image intensities are confounded by the apparent change in contrast.
Recently, when reading a blog post on registering camera and satellite images, I learned about SIFT and decided to try those ideas to my problem. That is how I discovered your work.
Nonetheless, I haven't been able to get SIFT3D to work so far, but I suppose it's because I'm not using the CLI tools correctly.
Here's what I've tried:
- Build SIFT3D with nifti support
- Try registering two images with:
regSift3D --matches matches.csv \
--warped warped.nii \
--keys keys.nii.gz \
--transform transform.csv \
src.nii ref.nii
My output warped.nii
looks wrong. Viewing it with freeview
gives me a warning that the sform and qform of the warped.nii
are incorrect, so I don't know whether the registration failed, or whether the nifti output is wrong.
- I tried looking at transform.csv and figuring out whether I could use the values there to apply the transformation using AntsApplyTransforms.
The values in transform.csv look OK:
1.003655,0.006255,-0.005859,-0.649355
0.001542,1.000908,0.001695,-0.199589
-0.000418,-0.007386,1.009908,0.267528
I assume the first three columns represent a rotation matrix and the last column the translation coefficients. Is that correct?
Are the rotations computed with respect to the center of mass or the center of the image? Would you know the correct way to convert that output to ITK format?
Finally, regarding the SIFT parameters, in your paper you cite the original SIFT work as a starting point to gain intuition about them, but from your experience which parameters matter most for accurately defining keypoints in brain MR images?
Thank you for sharing your work!
Daniel