Hi!
I have another question that I would love your opinion on. I am
analyzing perfusion data from a multi subject project. First, I
ran my raw images through Soren Christensen's Matlab "pgui" program
that calculated CBF, CBV, MTT, k1,k2 and delay. My next task was to
co-register these images to the highest degree of freedom while
keeping the integrity of the images. I have tried many different
methods. I came to the conclusion that my best output so far was to:
1. Create a mean image of my perfusion dataset (2x2x4 dimensions) and
flirt with 12dof to (2x2x2) MNI_152 brain
2. Register each image, using nreg, to the mean and flirted template
image by using CC and -ds 80 then subdivide (so -ds 40) and then
subdivide again (-ds 20)
My ultimate goal is to use randomise for the analysis. But I am not
sure what the best input is going to be for randomise. I have
uploaded (using the link you sent me) the 4-D dataset after step 2
above was completed. The code is 310438. Would you have a look and give
me your thoughts?
They still look quite different to me and when I ran this
through randomise the first time I didn't really get any significant
results; however, the project is looking at differences pre and post
altitude. I know that CBF should go up at altitude; so I know there
should be a difference in this dataset. (Paired t-test)
randomise -i 4D_CBF_pre_post -o 4D_CBF_20 -v 10 -V -m min_4D.nii.gz -d
21pair.mat -t 21pair.con -c 3
I used the minimum image of the dataset to mask the 4-D image so that
they all have the same dimensions.
Because the mean image is inherently smoothed and has less
information, I have recently begun running the CBF image registration
all to all using tbss_2_reg hoping that this might give me a more
accurate result...but this takes a while=)
I have read the paper on how tbss works for the FA data. I think this
methodology is brilliant and I am planning to use it for my diffusion
dataset that corresponds with the perfusion data that I discussed
above. I understand why it works so well with the white matter
tracts.
I am a beginning masters student and have only recently begun to
educate myself on MR imaging, so my knowledge is very limited in this
subject. This may be a stupid question but Have you ever explored
trying to make a similar program that fine tunes the coregistration of
perfusion (like CBF or MTT) data? I understand that the real values
that you find in FA data are not present in the perfusion data...but
maybe normalizing to a certain value set depending on max and min or
time would provide this?...
Thanks so much for your help. I really am enjoying learning FSL and
how robust a package it is! I look forward to your reply.
Kelly Brown
University of Colorado HSC
Altitude Research Center
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