Dear Frank,
I hope this message finds you well.
I need your assistance. I'm currently conducting a study with approximately 45 patients, specifically focusing on correlational tractography.
For my metrics, I am primarily interested in FA and MD (from DTI), as they are commonly used in similar studies, and secondarily on QA and ISO (from QSDR), for novel findings :)
I have some acquisition "limitations": Non-isotropic spatial resolution (1.8 x 1.8 x 6.5); 1 low b-value (1000); insufficient diffusion sampling direction (48 directions); i dont have reverse phase encoding.
Question 1)
[Step T2 ] Pre-processing / Reconstruction
The choice of optimal pre processing parameters is tricky. I have analyzed different combinations and guided myself based on how high the R-square is.
Is this approach correct? I feel like I might be adding bias related to R square values as the ultimate quality recontruction parameter. When in reality R is just telling us how good was the alignment/registration into template space, right?
This were the results:
[Step T2] [Image flip z] [Corrections][EDDY]
[Step T2b(1)]=QSDR
[Step T2b(1)][QSDR resolution]=1.79 (the voxel output resolution matches acquistion in-plane resolution. By default 3.0mm decreased the R value. What's the harm of using 1.79 instead of 2 mm ?)
Question 2)
[Step C] Correlational tractography
I want to keep only "strong" findings so I used an high T-threshold (3), as well as no FDR (unchecked) and the default length (15). The final results were interesting.
One of my goals is to specify the white matter tracts that are related to the study variable.
I have bad anatomy knowledge so I am using the Automatic Recognition [Trac Misc] [Tract Regcongition].
You have said we need to be careful interperting the output of automatic recongition, as there can be errors. But since I am using QSDR reconstructed tracts (aka in the MNI stereotaxic space), isn't it a lot more safe to interpret the automatic findings?
Thank you in advance,
I hope I have communicated my doubts in the best way,
Best regards,
Manuel