First I should note that using the same scripts I completed 52 participants successfully and am currently trying to batch process the newest 42 participants that are only failing at the path level. Upon Visual Inspection they all appear normal (with the exception of the one attached). After carefully comparing a random sample of 5 from each group images from the files that failed versus the files that were successful, I found:

1. The range of values in the mean_ph1samples in the successful ( i.e. -3.00 to 87.30 ) differed from the failures (i.e. -2 to 17) (the successful samples varied on their high end from 50-114 but the failures were on the 5-24 range)
2. The range of values in the mean_th1samples in the successful ( -0.02 to 19.12) differed from the failures (-0.00267 to 7.176) (the successful samples varied on their high end from 19-17 but the failures were on the 3-10 range)
3. Some, but not all (2/5), mean_S0samples, had a abnormal range in successful (0 to 19998.4) compared to the unsuccessful (0 to 709.96)

However, I understand that the diffusion units are likely arbitrary and not sure these patterns will hold in a larger sample.

I know this is a lot of confusing information and so in response to your question. After an initial inspection there are two types of output:
1. data that appears visually normal with a slightly altered range.
  Should I be concerned about the range if all other data looks fine?

2. data that appears visually normal with a normal range and bizarre merged files.
  What do you think could be a cause of these odd merged files?




On Tue, Dec 22, 2015 at 1:54 PM, Anastasia Yendiki <ayendiki@nmr.mgh.harvard.edu> wrote:

Hi Katherine - Have you looked at any of the intermediate images? Do they look normal?

a.y


On Mon, 21 Dec 2015, Katherine Damme wrote:

Hello Everyone!

It looks as though my prep and bedp stages completed properly, but the prep stage ends in a segmentation
errror.

See attached and let me know your thoughts. I would prefer not to have to redo all steps if possible!
Thank you!




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