#-------------------------------------------- #@# Hippocampal Subfields processing (T1) left Thu Dec 17 12:05:21 CET 2020 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/opt/freesurfer/7.1.1/MCRv84//runtime/glnxa64:/opt/freesurfer/7.1.1/MCRv84//bin/glnxa64:/opt/freesurfer/7.1.1/MCRv84//sys/os/glnxa64:/opt/freesurfer/7.1.1/MCRv84//sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/opt/freesurfer/7.1.1/MCRv84//sys/java/jre/glnxa64/jre/lib/amd64/server:/opt/freesurfer/7.1.1/MCRv84//sys/java/jre/glnxa64/jre/lib/amd64/client:/opt/freesurfer/7.1.1/MCRv84//sys/java/jre/glnxa64/jre/lib/amd64:/opt/R/3.5.1/lib64/R/lib:/opt/cluster/lib:/opt/cluster/external/p7zip-16.02/lib/p7zip Registering imageDump.mgz to hippocampal mask from ASEG 7.1.1 --mov: Using imageDump.mgz as movable/source volume. --dst: Using /home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz as target volume. --lta: Output transform as trash.lta . --mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz ! --sat: Using saturation 50 in M-estimator! reading source 'imageDump.mgz'... reading target '/home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz'... Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 0 Type Target : 3 ensure both FLOAT (3) Reordering axes in mov to better fit dst... ( -1 3 -2 ) Determinant after swap : 0.015625 Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) Dst: (1, 1, 1)mm and dim (38, 39, 63) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 0 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 39, 63) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - no Dst reslice necessary Registration::computeMultiresRegistration - computing centroids - computing initial transform -- using translation info - Get Gaussian Pyramid Limits ( min size: 16 max size: -1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 -0.3773093080253 0 1.0000000000000 0 2.1148246252257 0 0 1.0000000000000 -2.0733903827146 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 19 31 ) T( 19 19 31 ) Iteration(f): 1 -- diff. to prev. transform: 13.1875 Iteration(f): 2 -- diff. to prev. transform: 6.80473 Iteration(f): 3 -- diff. to prev. transform: 6.6167 Iteration(f): 4 -- diff. to prev. transform: 6.78298 Iteration(f): 5 -- diff. to prev. transform: 0.625873 max it: 5 reached! Resolution: 0 S( 38 39 63 ) T( 38 39 63 ) Iteration(f): 1 -- diff. to prev. transform: 6.47442 Iteration(f): 2 -- diff. to prev. transform: 1.63452 Iteration(f): 3 -- diff. to prev. transform: 0.300624 Iteration(f): 4 -- diff. to prev. transform: 0.107388 Iteration(f): 5 -- diff. to prev. transform: 0.0250386 max it: 5 reached! - final transform: Tf = [ ... 0.9905498265002 -0.0558283673261 -0.1252766323867 5.6450814796323 0.1003642556845 0.9175875714603 0.3846557745341 -12.9958814513727 0.0934775769976 -0.3935940066912 0.9145193822415 7.3531774744762 0 0 0 1.0000000000000 ] - final iscale: If = 1 ********************************************************** * * WARNING: Registration did not converge in 5 steps! * Problem might be ill posed. * Please inspect output manually! * ********************************************************** Final Transform: Adjusting final transform due to initial resampling (voxel or size changes) ... M = [ ... -0.2476374566250 -0.0313191580967 0.0139570918315 38.5003166492308 -0.0250910639211 0.0961639436335 -0.2293968928651 20.0770216669627 -0.0233693942494 0.2286298455604 0.0983985016728 -0.5533689445097 0 0 0 1.0000000000000 ] Determinant : -0.015625 writing output transformation to trash.lta ... converting VOX to RAS and saving RAS2RAS... mapmovhdr: Changing vox2ras MOV header (to map to DST) ... To check aligned result, run: freeview -v /home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz imageDump_coregistered.mgz Registration took 0 minutes and 1 seconds. Thank you for using RobustRegister! If you find it useful and use it for a publication, please cite: Highly Accurate Inverse Consistent Registration: A Robust Approach M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. http://dx.doi.org/10.1016/j.neuroimage.2010.07.020 http://reuter.mit.edu/papers/reuter-robreg10.pdf 7.1.1 --mov: Using imageDump.mgz as movable/source volume. --dst: Using /home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz as target volume. --lta: Output transform as trash.lta . --mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz ! --affine: Enabling affine transform! --sat: Using saturation 50 in M-estimator! reading source 'imageDump.mgz'... reading target '/home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz'... Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 0 Type Target : 3 ensure both FLOAT (3) Reordering axes in mov to better fit dst... ( -1 3 -2 ) Determinant after swap : 0.015625 Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) Dst: (1, 1, 1)mm and dim (38, 39, 63) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 0 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 39, 63) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - no Dst reslice necessary Registration::computeMultiresRegistration - computing centroids - computing initial transform -- using translation info - Get Gaussian Pyramid Limits ( min size: 16 max size: -1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 -0.3773081857769 0 1.0000000000000 0 2.1147372976911 0 0 1.0000000000000 -2.0733392207393 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 19 31 ) T( 19 19 31 ) Iteration(f): 1 -- diff. to prev. transform: 13.4057 Iteration(f): 2 -- diff. to prev. transform: 6.82866 Iteration(f): 3 -- diff. to prev. transform: 9.88716 Iteration(f): 4 -- diff. to prev. transform: 10.7689 Iteration(f): 5 -- diff. to prev. transform: 3.25195 max it: 5 reached! Resolution: 0 S( 38 39 63 ) T( 38 39 63 ) Iteration(f): 1 -- diff. to prev. transform: 10.2673 Iteration(f): 2 -- diff. to prev. transform: 2.17054 Iteration(f): 3 -- diff. to prev. transform: 0.806598 Iteration(f): 4 -- diff. to prev. transform: 0.338381 Iteration(f): 5 -- diff. to prev. transform: 0.104875 max it: 5 reached! - final transform: Tf = [ ... 1.2030223237189 0.1884187660644 -0.0167932422667 -7.4790282553433 0.1560810568266 1.0236440479534 0.4645717799507 -19.5773333553553 0.2438450826014 -0.5807477383867 0.9710372375638 5.5635008355781 0 0 0 1.0000000000000 ] - final iscale: If = 1 ********************************************************** * * WARNING: Registration did not converge in 5 steps! * Problem might be ill posed. * Please inspect output manually! * ********************************************************** Final Transform: Adjusting final transform due to initial resampling (voxel or size changes) ... M = [ ... -0.3007555803217 -0.0041983073249 -0.0471046758039 40.7127838486195 -0.0390202577973 0.1161429175878 -0.2559109503930 18.9165458088794 -0.0609612673877 0.2427592519668 0.1451869008898 -2.9024121066671 0 0 0 1.0000000000000 ] Determinant : -0.0237324 Decompose into Rot * Shear * Scale : Rot = [ ... -0.9890542235170 -0.1314605763023 -0.0670064162713 -0.0031820392005 0.4730167673437 -0.8810476788677 -0.1475181940297 0.8711907108607 0.4682575442428 ] Shear = [ ... 1.0000000000000 -0.1199668004135 0.0876086668809 -0.1044704729898 1.0000000000000 0.0392019164457 0.0847597162801 0.0435529158645 1.0000000000000 ] Scale = diag([ 0.3065806370242 0.2669790646166 0.2966109260227 ]) writing output transformation to trash.lta ... converting VOX to RAS and saving RAS2RAS... mapmovhdr: Changing vox2ras MOV header (to map to DST) ... To check aligned result, run: freeview -v /home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz imageDump_coregistered.mgz Registration took 0 minutes and 1 seconds. Thank you for using RobustRegister! If you find it useful and use it for a publication, please cite: Highly Accurate Inverse Consistent Registration: A Robust Approach M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. http://dx.doi.org/10.1016/j.neuroimage.2010.07.020 http://reuter.mit.edu/papers/reuter-robreg10.pdf Out of memory. Type HELP MEMORY for your options. Error in segmentSubjectT1_autoEstimateAlveusML (line 210) MATLAB:nomem @#@FSTIME 2020:12:17:12:05:21 run_segmentSubjectT1_autoEstimateAlveusML.sh N 14 e 13.45 S 1.20 U 8.73 P 73% M 849776 F 0 R 340169 W 0 c 2552 w 6922 I 262320 O 216696 L 12.17 9.21 10.22 @#@FSLOADPOST 2020:12:17:12:05:34 run_segmentSubjectT1_autoEstimateAlveusML.sh N 14 11.08 9.11 10.17 Linux mentat004.dccn.nl 4.19.94-300.el7.x86_64 #1 SMP Thu Jan 9 16:15:13 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux T1 hippocampal subfields exited with ERRORS at Thu Dec 17 12:05:35 CET 2020