QSIPrep
QSIPrep is a powerful, freely available dMRI preprocessing pipeline.
You can activate it on MASSIVE by calling module load qsiprep.
An example of how to set out your script:
Hypothetically, all you really need is BIDSified data for QSIPrep to work*.
It is intended that QSIPrep read your directory structure and the kinds of data you have available, and then change the processing steps to suit your particular circumstances.
*A special note here: just because this is how it is intended to work, does not mean that you should treat QSIPrep like a black box (as many others do!). If you set things out incorrectly and still get output (error free output does not necessarily mean that your results are appropriate!), you will not know that there has been an issue.
QSIPrep is always being updated. I highly recommend consulting the “What’s new” documentation to ensure that you are using the most up to date version prior to analyses: https://qsiprep.readthedocs.io/en/latest/changes.html.
Checking the output:
The nice thing about QSIPrep is that it will spit out a nice .html doc for your subjects following analyses. You should comb thru these documents to ensure that there is nothing out of the ordinary. You should check that, in general, there are not any obvious discrepancies or errors in the:
- Brain mask and brain tissue segmentation of the T1w: just make sure the lines are in general tracing things correctly without going into the skull or space outside the head
- Spatial normalisation of the T1w ref to MNI: just make sure that the images are well aligned in the same space (hover your mouse over the image and it will toggle)
- Surface recon: just make sure the lines are in general tracing things correctly without going into the skull or space outside the head
- T2 map: just make sure the lines are in general tracing things correctly without going into the skull or space outside the head
- Alignment of functional and anatomical MRI data: just make sure the lines are in general tracing things correctly without going into the skull or space outside the head
- Q-space sampling scheme: ensure that the post-processing gif indicates some uniformity; “This is useful to confirm that the gradients have indeed been rotated and that head motion correction has not disrupted the scheme extensively.¶”
You should also read thru the boilerplate in the Methods section to ensure that it looks like ithas executed steps that make sense for your data. For example, if you included fieldmap data for SDC, does it run SDC using that data (per what is desired?).
Finally, scroll to the bottom of the document - and make sure that there are no errors to report in the Errors section.
If you get stuck, it is defs worth checking out NeuroStars and Github - there may be existing forums which can point you in the right direction.
After you run QSIPrep, you can then run QSIrecon - which reconstructs the dMRI using QSIprep output. This is made available thru qsiprep.
An example below, where I am resampling to 1.3mm and specifying where already Freesurfered data exists.
You will need to choose a recon algorithm that is appropriate for your data/interests: e.g. from below:
You can discern whether your data is multi or single shell by consulting the bval documents made available during the DICOM to nifti conversion. For example, if the bval doc has info such as 3000 3000 3000 3000 0 3000 3000 3000 3000 0 it is a single shell - it only toggles between 0 and 3000. More bvals indicate more shells.
Maybe QSIPrep does not handle something you would like it to: maybe a different program would be better. Consult the comparisons to other pipelines page.