Experts,

I have been working on Freesurfer last 5 months and initially the recon processes were within normal time limits.

However, I am working on a different set of subjects now and its consuming days together. For my current data,autorecon1 is fine, it stops at a particular point in autorecon2 for infinity. Any suggestions would be helpful.

I am pasting the autorecon2 process here:

recon-all -autorecon2 -subjid ja Subject Stamp: freesurfer-Linux-centos4-stable-pub-v4.1.0 Current Stamp: freesurfer-Linux-centos4-stable-pub-v4.1.0 INFO: SUBJECTS_DIR is /usr/local/freesurfer/subjects Actual FREESURFER_HOME /usr/local/freesurfer -rw-rw-r-- 1 barnali barnali 257784 Jul 9 10:32 /usr/local/freesurfer/subjects/ja/scripts/recon-all.log Linux barnali-work 2.6.24-16-server #1 SMP Thu Apr 10 13:58:00 UTC 2008 i686 GNU/Linux #------------------------------------- #@# EM Registration Thu Jul 9 10:35:21 PDT 2009 /usr/local/freesurfer/subjects/ja/mri

mri_em_register -mask brainmask.mgz nu.mgz /usr/local/freesurfer/average/RB_all_2008-03-26.gca transforms/talairach.lta

using MR volume brainmask.mgz to mask input volume... reading 1 input volumes... logging results to talairach.log reading '/usr/local/freesurfer/average/RB_all_2008-03-26.gca'... average std = 6.9 using min determinant for regularization = 4.7 0 singular and 1812 ill-conditioned covariance matrices regularized reading 'nu.mgz'... freeing gibbs priors...done. bounding unknown intensity as < 14.9 or > 790.2 total sample mean = 84.0 (478 zeros) ************************************************ spacing=8, using 2185 sample points, tol=1.00e-05... ************************************************ register_mri: find_optimal_transform find_optimal_transform: nsamples 2185, passno 0, spacing 8 resetting wm mean[0]: 102 --> 107 resetting gm mean[0]: 64 --> 64 input volume #1 is the most T1-like using real data threshold=8.0 using (107, 91, 112) as brain centroid... mean wm in atlas = 107, using box (90,77,91) --> (123, 104,132) to find MRI wm before smoothing, mri peak at 137 after smoothing, mri peak at 151, scaling input intensities by 0.709 scaling channel 0 by 0.708609 initial log_p = -92132.8 ************************************************ First Search limited to translation only. ************************************************

Found translation: (-2.8, -1.7, 1.7): log p = -39503.6 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-37619.9, old_max_log_p =-39503.6 (thresh=-39464.1) 1.062 0.000 0.000 -10.944; 0.000 1.053 0.139 -22.515; 0.000 -0.131 0.991 18.328; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-35219.5, old_max_log_p =-37619.9 (thresh=-37582.3) 1.062 0.000 0.000 -10.944; 0.000 1.266 0.027 -29.117; 0.000 0.025 1.003 -1.688; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 2 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-35219.5, old_max_log_p =-35219.5 (thresh=-35184.3) 1.062 0.000 0.000 -10.944; 0.000 1.266 0.027 -29.117; 0.000 0.025 1.003 -1.688; 0.000 0.000 0.000 1.000; reducing scale to 0.2500 **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-32338.2, old_max_log_p =-35219.5 (thresh=-35184.3) 1.096 0.000 0.000 -15.249; 0.000 1.208 0.026 -25.944; 0.000 0.026 1.035 -8.782; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 4 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-31970.6, old_max_log_p =-32338.2 (thresh=-32305.8) 1.096 0.000 0.000 -17.124; 0.000 1.189 0.025 -21.806; 0.000 0.026 1.035 -6.907; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 5 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-31970.6, old_max_log_p =-31970.6 (thresh=-31938.6) 1.096 0.000 0.000 -17.124; 0.000 1.189 0.025 -21.806; 0.000 0.026 1.035 -6.907; 0.000 0.000 0.000 1.000; reducing scale to 0.0625 **************************************** Nine parameter search. iteration 6 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-31028.4, old_max_log_p =-31970.6 (thresh=-31938.6) 1.083 0.000 0.000 -14.039; 0.000 1.194 0.025 -22.828; 0.000 0.026 1.039 -7.345; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 7 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-30899.3, old_max_log_p =-31028.4 (thresh=-30997.4) 1.079 0.000 0.000 -13.957; 0.000 1.194 0.025 -22.828; 0.000 0.026 1.043 -7.785; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 8 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-30683.0, old_max_log_p =-30899.3 (thresh=-30868.4) 1.070 0.000 0.000 -12.858; 0.000 1.194 0.025 -22.828; 0.000 0.026 1.039 -7.344; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 9 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-30108.0, old_max_log_p =-30683.0 (thresh=-30652.3) 1.058 0.000 0.000 -11.225; 0.000 1.194 0.025 -22.828; 0.000 0.026 1.051 -9.140; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 10 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-29914.1, old_max_log_p =-30108.0 (thresh=-30077.9) 1.058 0.000 0.000 -11.225; 0.000 1.180 0.025 -21.172; 0.000 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 11 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-29914.1, old_max_log_p =-29914.1 (thresh=-29884.1) 1.058 0.000 0.000 -11.225; 0.000 1.180 0.025 -21.172; 0.000 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000; min search scale 0.025000 reached *********************************************** Computing MAP estimate using 2185 samples... *********************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-05 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.05773 0.00000 0.00000 -11.22528; 0.00000 1.17956 0.02501 -21.17203; 0.00000 0.02668 1.05912 -10.03400; 0.00000 0.00000 0.00000 1.00000; nsamples 2185 Quasinewton: input matrix 1.05773 0.00000 0.00000 -11.22528; 0.00000 1.17956 0.02501 -21.17203; 0.00000 0.02668 1.05912 -10.03400; 0.00000 0.00000 0.00000 1.00000; v3p/netlib/opt/lbfgs.c: lb3_1.lp > 0 outof QuasiNewtonEMA: 012: -log(p) = 29914.1 tol 0.000010 Resulting transform: 1.058 0.000 0.000 -11.225; 0.000 1.180 0.025 -21.172; 0.000 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000;

pass 1, spacing 8: log(p) = -29914.1 (old=-92132.8) transform before final EM align: 1.058 0.000 0.000 -11.225; 0.000 1.180 0.025 -21.172; 0.000 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000;

************************************************** EM alignment process ... Computing final MAP estimate using 244171 samples. ************************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-07 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.05773 0.00000 0.00000 -11.22528; 0.00000 1.17956 0.02501 -21.17203; 0.00000 0.02668 1.05912 -10.03400; 0.00000 0.00000 0.00000 1.00000; nsamples 244171 Quasinewton: input matrix 1.05773 0.00000 0.00000 -11.22528; 0.00000 1.17956 0.02501 -21.17203; 0.00000 0.02668 1.05912 -10.03400; 0.00000 0.00000 0.00000 1.00000; dfp_em_step_func: 011: -log(p) = 3772889.8 after pass:transform: ( 1.07, 0.01, 0.01, -11.23) ( 0.00, 1.18, 0.03, -21.17) ( 0.00, 0.03, 1.06, -10.03) v3p/netlib/opt/lbfgs.c: lb3_1.lp > 0 pass 2 through quasi-newton minimization... v3p/netlib/opt/lbfgs.c: lb3_1.lp > 0 outof QuasiNewtonEMA: 013: -log(p) = 3772889.8 tol 0.000000 final transform: 1.067 0.008 0.009 -11.225; 0.001 1.180 0.027 -21.172; 0.001 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000;

writing output transformation to transforms/talairach.lta... registration took 33 minutes and 19 seconds. #-------------------------------------- #@# CA Normalize Thu Jul 9 11:08:40 PDT 2009 /usr/local/freesurfer/subjects/ja/mri

mri_ca_normalize -mask brainmask.mgz nu.mgz /usr/local/freesurfer/average/RB_all_2008-03-26.gca transforms/talairach.lta norm.mgz

using MR volume brainmask.mgz to mask input volume... reading 1 input volumes reading atlas from '/usr/local/freesurfer/average/RB_all_2008-03-26.gca'... reading transform from 'transforms/talairach.lta'... reading input volume from nu.mgz... resetting wm mean[0]: 102 --> 107 resetting gm mean[0]: 64 --> 64 input volume #1 is the most T1-like using real data threshold=8.0 using (107, 91, 112) as brain centroid... mean wm in atlas = 107, using box (90,77,91) --> (123, 104,132) to find MRI wm before smoothing, mri peak at 137 after smoothing, mri peak at 151, scaling input intensities by 0.709 scaling channel 0 by 0.708609 using 244171 sample points... INFO: compute sample coordinates transform 1.067 0.008 0.009 -11.225; 0.001 1.180 0.027 -21.172; 0.001 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000; INFO: transform used finding control points in Left_Cerebral_White_Matter.... found 41584 control points for structure... bounding box (125, 59, 30) --> (189, 159, 190)

It stops here and doesnt process any further. I tried twice, with same result.

I work on a dual core Ubuntu 5GB machine

Thanks

Barnali