Hello,
My team has been using QSIPrep and now we’re moving on to QSIRecon. I’ve been able to successfully process a subject with QSIRecon, but before I continue with more subjects my team has a couple of questions that we can’t seem to find the answer to on the documentation page.
How exactly do we use a custom atlas? I saw the section for it on the documentation page, but it’s still not exactly clear to me how we can use a custom atlas (say from Freesurfer). Also, the link in that section does not seem to work.
Is there a way to use our own response functions? My team is interested in creating average response functions from a set of controls and then using those response functions for our entire set of subjects. Is this possible?
Is there a workflow that generates FA and ADC/MD maps?
No, not at the moment, but you can use QSIRecon just for response function generating step and then average after. (see next posts)
Several. DIPY DKI uses the tensor component of a kurtosis model (multishell compatible). DSI Studio GQI uses an ordinary least squares and TORTOISE uses a weighted linear least squares approach. You may consider only using b-values less than some cut off such as 1200 unless using the multishell DKI. You can see an example of all approaches being combined here: qsirecon/qsirecon/data/pipelines/abcd_recon.yaml at main · PennLINC/qsirecon · GitHub.
Just to follow up on @Steven’s post: technically QSIRecon expects atlases to follow an older version of the BEP. BIDS has since merged the BEP with some changes, so QSIRecon expects something that isn’t actually BIDS-compliant.
In the dataset_description.json, you should have DatasetType set to atlas. Since, in that example, the atlas dataset is located at /path and the name of the atlas (from atlas-<label>) is carpet, you could pass that in as --datasets carpet=/path --atlases carpet.
We plan to overhaul atlas organization to match BIDS in the near future, but we don’t have a timeline for it yet.
We did add the ability to ingress custom response functions not that long ago. You could run QSIRecon to generate the subject-wise response functions, then average them with your own code, and then incorporate those into a new pipeline.
This is a very new addition so we don’t have any good documentation on it. We do have a pair of pipelines set up for testing that you could use as a template:
The first pipeline estimates the response functions:
The second one uses averaged response functions stored in --recon-spec-aux-files with the names wm.txt, gm.txt, and csf.txt: