Dear Andrew, sorry for the late reply. Aren't there any error outputs for the two cases? If not, would you mind uploading the subjects so I can take a look? Cheers, Eugenio

Juan Eugenio Iglesias Postdoctoral researcher BCBL www.jeiglesias.com www.bcbl.eu

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From: "Andrew Schoen" schoen.andrewj@gmail.com To: "Freesurfer support list" freesurfer@nmr.mgh.harvard.edu Sent: Wednesday, December 23, 2015 4:05:00 PM Subject: [Freesurfer] Hippocampal Subfields (6.0) Error

Hi all, We are running some hippocampal subfield segmentations for two independent studies. They have already been processed through the standard pipeline (5.3), so I was just running the following command:

recon-all -s ${subjectid} -hippocampal-subfields-T1

For both test cases, the processing seems to have halted (no more updates to the log file), but the command keeps running. For one study, it trailed off here:

************************************************************************************************************************ Resolution level 1 iteration 2 deformation iterations 13 Number of Meshes: 1 Using CG optimizer Did one deformation step of max. 0.00037944 voxels in 14.6033 seconds

minLogLikelihoodTimesPrior =

2.3442e+06

Resolution level 1 iteration 2 deformation iterations 14 Number of Meshes: 1 Using CG optimizer Did one deformation step of max. 1.3541e-06 voxels in 3.5131 seconds

minLogLikelihoodTimesPrior =

2.3442e+06

Resolution level 1 iteration 2 deformation iterations 15 Number of Meshes: 1 Using CG optimizer Did one deformation step of max. 0 voxels in 30.0807 seconds

minLogLikelihoodTimesPrior =

2.3442e+06

maximalDeformation is too small; stopping Iteration 3 of 7 (eof) ************************************************************************************************************************

and the other here:

************************************************************************************************************************ Making Left-Accumbens-area map to reduced label 13 -------------- Making Unknown map to reduced label 14 Computing hyperparameters for estimation of Gaussians parameters Estimating typical intensities of molecular layer and alveus For ML in T1, we assume gray with high N In T2, we actually assume the same hypermean Smoothing mesh collection with kernel size 1.500000 ...Smoothing class: 0 Smoothing class: 1 Smoothing class: 2 Smoothing class: 3 Smoothing class: 4 Smoothing class: 5 Smoothing class: 6 Smoothing class: 7 Smoothing class: 8 Smoothing class: 9 Smoothing class: 10 Smoothing class: 11 Smoothing class: 12 Smoothing class: 13 done numberOfImages = 2 Image: 0 Image: 2 Iteration 1 of 7 (eof) ************************************************************************************************************************

Interestingly, for one study, we have T2-weighted images, and when I ran with that (same subject as second log, above), it ran to completion fine.

Any idea what might be happening? Let me know if any other info would be useful!

Thanks!

Andrew Schoen Center for Healthy Minds Waisman Center University of Wisconsin - Madison

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