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Hi, We've been using fsl/5.0 for a while, but we wanted motion parameters from the eddy_movement_rms files. To that end, I'm now using eddy_openmp from fsl/5.0.11. While eddy_openmp seems to clearly be returning motion corrected NIFTIs, the actual eddy_movement_rms files looked incorrect, because they were mostly populated with zeroes and repeated values, for ~40% of our dataset. These jobs were all multi-threaded (pe threaded 4). Example of one of our 'bad' eddy_corrected_data.eddy_movement_rms file; 0 0 0.2097059397 0.2097059397 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 0.2097059397 0 When we check the "eddy_corrected_data.eddy_parameters" file, it shows us this; 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 0.1028521901 0.3712571436 -0.1851838779 -0.006534020416 -0.0004291008227 -0.004189225845 0 0 0 0 0 0 0 0 0 0 Each time I resubmit our cohort (~480 subjects), between 35-45% of the jobs end up writing files such as the ones shown above. Neither the subjectIDs, nor the compute nodes are identical in each run for the failed jobs, i.e., it appears to have no pattern in terms of the subjects it chooses to do this to. Our DTI preprocessing pipeline written in python, that calls executables for lpca_denoising (in MATLAB), eddy_openmp, and finally we fit a tensor model to our data. This is the same preprocessing script we've been using for our other datasets, we only noticed this issue because we wanted the movement rms files from eddy_openmp. fsl/5.0's eddy works fine (single thread), and doesn't write any zeroes to its output files. On our end we have tried increasing the memory for these jobs, increasing the number of threads to 6, running them with and without the option of a temporary directory etc. I do not know the inner workings of eddy_openmp, and was hoping the developers or users may be able to help me figure out why we're having so many repeated values and zeroes written to the files. The actual NIFTI image output appears to be okay, but due to these zeroes, I cannot be sure if it is okay to go ahead and use them. Hoping to hear from you soon Jacob ######################################################################## To unsubscribe from the FSL list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=FSL&A=1