Freesurfer

freesurfer@nmr.mgh.harvard.edu

19 participants
30088 discussions

Re: [Freesurfer] coregistration - FA to Freesurfer
by Ed Gronenschild 18 Oct '11

18 Oct '11

Dear Joana, Two things: 1. You should be very careful to use fslswapdim since it may result in an unintended left-right flipping. It's better to use fslreorient2std. 2. The tool dt-recon computes the coregistration of the lowb volume to the anatomical volume, see register.dat. You can then use the following to coregister FA: mri_vol2vol --reg register.dat --mov fa.nii --o fa-ana.nii -targ subject/mri/T1.mgz where "subject" is the output folder generated by the recon-all command. You should not forget to set the global variable SUBJECTS_DIR Cheers, Ed On 14 Oct, 2011, at 18:00, freesurfer-request(a)nmr.mgh.harvard.edu wrote: > > Dear Bruce and Anastasia, > > Thanks for the quick reply! > > I tried the bbregister as you suggested but when i visualize the > target and > dti image they don't look registered at all. > > This is the comand i used: > > bbregister --s CN01 --mov /home/joana/FA/CN01/dti_FA.nii.gz --reg > /home/joana/FA/CN01/register.dat --init-fsl --dti --template-out > /home/joana/FA/CN01/template.nii.gz --fslmat /home/joana/FA/CN01/ > flirt.tmx > > I tried to use the bbregister tools to align them but I didn't get > very > far... > > Did i miss something? > > I read in another mail something about using the register.dat > created in > bbregister and do mri_label2vol but i'm not sure how to do it. > > Thanks, > > Joana

4 8

MCSRCH ERROR
by Kenichiro Tanaka 18 Oct '11

18 Oct '11

Hello All, While processing a brain image data(.mgz) through ver5.0.0, I got the following errors. I have no idea how to resolve them. I need outputs in stats directory, there are only lh.aparc.a2009s.stats, lh.aparc.stats, and lh.curv.stats. What caused them? How should I resolve them? IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 3 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 005: -log(p) = 10318.1 tol 0.000010 . . . inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=175.699, avgs=0 005/300: dt: 0.9000, rms radial error=175.446, avgs=0 010/300: dt: 0.9000, rms radial error=174.904, avgs=0 015/300: dt: 0.9000, rms radial error=174.193, avgs=0 020/300: dt: 0.9000, rms radial error=173.383, avgs=0 025/300: dt: 0.9000, rms radial error=172.518, avgs=0 030/300: dt: 0.9000, rms radial error=171.620, avgs=0 035/300: dt: 0.9000, rms radial error=170.706, avgs=0 040/300: dt: 0.9000, rms radial error=169.784, avgs=0 045/300: dt: 0.9000, rms radial error=168.860, avgs=0 050/300: dt: 0.9000, rms radial error=167.935, avgs=0 055/300: dt: 0.9000, rms radial error=167.012, avgs=0 060/300: dt: 0.9000, rms radial error=166.092, avgs=0 065/300: dt: 0.9000, rms radial error=165.175, avgs=0 070/300: dt: 0.9000, rms radial error=164.262, avgs=0 075/300: dt: 0.9000, rms radial error=163.352, avgs=0 080/300: dt: 0.9000, rms radial error=162.447, avgs=0 085/300: dt: 0.9000, rms radial error=161.547, avgs=0 090/300: dt: 0.9000, rms radial error=160.651, avgs=0 095/300: dt: 0.9000, rms radial error=159.759, avgs=0 100/300: dt: 0.9000, rms radial error=158.871, avgs=0 105/300: dt: 0.9000, rms radial error=157.989, avgs=0 110/300: dt: 0.9000, rms radial error=157.110, avgs=0 115/300: dt: 0.9000, rms radial error=156.236, avgs=0 120/300: dt: 0.9000, rms radial error=155.367, avgs=0 125/300: dt: 0.9000, rms radial error=154.502, avgs=0 130/300: dt: 0.9000, rms radial error=153.642, avgs=0 135/300: dt: 0.9000, rms radial error=152.786, avgs=0 140/300: dt: 0.9000, rms radial error=151.934, avgs=0 145/300: dt: 0.9000, rms radial error=151.087, avgs=0 150/300: dt: 0.9000, rms radial error=150.245, avgs=0 155/300: dt: 0.9000, rms radial error=149.407, avgs=0 160/300: dt: 0.9000, rms radial error=148.573, avgs=0 165/300: dt: 0.9000, rms radial error=147.744, avgs=0 170/300: dt: 0.9000, rms radial error=146.919, avgs=0 175/300: dt: 0.9000, rms radial error=146.098, avgs=0 180/300: dt: 0.9000, rms radial error=145.282, avgs=0 185/300: dt: 0.9000, rms radial error=144.470, avgs=0 190/300: dt: 0.9000, rms radial error=143.663, avgs=0 195/300: dt: 0.9000, rms radial error=142.860, avgs=0 200/300: dt: 0.9000, rms radial error=142.061, avgs=0 205/300: dt: 0.9000, rms radial error=141.266, avgs=0 210/300: dt: 0.9000, rms radial error=140.476, avgs=0 215/300: dt: 0.9000, rms radial error=139.693, avgs=0 220/300: dt: 0.9000, rms radial error=138.914, avgs=0 225/300: dt: 0.9000, rms radial error=138.140, avgs=0 230/300: dt: 0.9000, rms radial error=137.370, avgs=0 235/300: dt: 0.9000, rms radial error=136.604, avgs=0 240/300: dt: 0.9000, rms radial error=135.842, avgs=0 245/300: dt: 0.9000, rms radial error=135.085, avgs=0 250/300: dt: 0.9000, rms radial error=134.332, avgs=0 255/300: dt: 0.9000, rms radial error=133.583, avgs=0 260/300: dt: 0.9000, rms radial error=132.838, avgs=0 265/300: dt: 0.9000, rms radial error=132.098, avgs=0 270/300: dt: 0.9000, rms radial error=131.362, avgs=0 275/300: dt: 0.9000, rms radial error=130.629, avgs=0 280/300: dt: 0.9000, rms radial error=129.901, avgs=0 285/300: dt: 0.9000, rms radial error=129.177, avgs=0 290/300: dt: 0.9000, rms radial error=128.456, avgs=0 295/300: dt: 0.9000, rms radial error=127.740, avgs=0 300/300: dt: 0.9000, rms radial error=127.028, avgs=0 spherical inflation complete. . . . epoch 4 (K=1280.0), pass 1, starting sse = 541.21 taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.32/19 = 0.01710 finalwriting spherical brain to ../surf/lh.qsphere.nofix spherical transformation took 0.10 hours distance error %100000.00 . . . pass 1: epoch 1 of 3 starting distance error %21.36 pass 1: epoch 2 of 3 starting distance error %21.04 unfolding complete - removing small folds... starting distance error %20.80 removing remaining folds... final distance error %20.81 364: 0 negative triangles . . . Thanks, Kenichiro Tanaka.

2 1

aparc+aspec to T1
by Hsiao-Ying (Monica) Wey 17 Oct '11

17 Oct '11

Dear experts, I would like to move my aparc+aseg.mgz image back to the native T1 space (rawavg.mgz). I tried: mri_convert -rl rawavg.mgz -rt nearest aparc+aseg.mgz ./subj001_aparc+aseg2raw.nii.gz However, it there a way that I don't change the value within the original aparc+aseg.mgz, so that I can use the LUT to define ROIs for additional processes? Thanks. Monica

3 2

Incorrect pial surface
by Jordan Pierce 17 Oct '11

17 Oct '11

We're trying to reconstruct pial and white matter surfaces for cortical thickness analysis and sections of the gray matter are being excluded, particularly in the temporal lobes. As far as we can tell, there is no way to add this back to the pial surface...is there a solution to this problem? Jordan Pierce

4 10

open-access article on TRACULA now available
by Anastasia Yendiki 17 Oct '11

17 Oct '11

Hi all - Since a few of you have inquired about a TRACULA reference in the past, here's one: http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2011.00023/abstra… There's a link on the right where you can download the PDF. Happy reading! a.y

1 0

Hanging or running at 'SubCort Seg'?
by Irwin, William 17 Oct '11

17 Oct '11

Hi- I'm a bit stumped by a cased. The input volume looks very good. The first error was the Talairach transform would not complete, so I used the '-notal-check' flag. As I have been focused on other things, I just let the job run, but after 2 weeks, it is stuck (I assume) as indicated. It is running on a fast processor with plenty of memory, so system constraints are not the issue #@# SubCort Seg Sun Oct 9 16:41:44 PDT 2011 mri_ca_label -align -nobigventricles norm.mgz transforms/talairach.m3z /usr/local/freesurfer/average/RB_all_2008-03-26.gca as eg.auto_noCCseg.mgz mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz -lta /media/ClusterData2/data/localdata2/FreeSurfer/wirwin/wirwin_subject s/FT047-5/mri/transforms/cc_up.lta FT047-5 Any suggestions as to what might be the problem? Thanks much, Wil

2 5

implementation on Neurodebian
by Cartik Sharma 17 Oct '11

17 Oct '11

Dear Freesurfer developer(s), Is there a way to get Freesurfer run faster on Neurodebian. While the package produces some truly out of the world segmentations and labels for the cortical surface, it seems to take time. For eg: Defect correction and retesellation takes an hour, other steps take longer..I'm not sure if it's the original dataset, the OS platform (i.e Virtual Machine, Neurodebian) or original computational engine itself. Would be happy in efforts and hints to make this run faster. Best regards, Cartik Sharma

1 0

First-level analysis with continuous covariate
by Emily Ward 17 Oct '11

17 Oct '11

How does one execute a first-level (subject-level) analysis with a continuous covariate? For example, in an event-related experiment presenting pictures of scenes, I have two conditions: environment (indoors/outdoors) and openness (a continuous rating obtained independently). I am interested in the environment x openness interaction, so I need to model them together. The parametric modulation paradigm file seems close to what I need, but in the example, there is no discrete variable to consider. Is there a way to model both discrete and continuous variables together? If so, can this method also be used to model out nuisance regressors, like trial performance? Thank you, Emily

2 1

saving adjustments under different names
by Maria Felber 17 Oct '11

17 Oct '11

Dear all, I have run the recon-all process on my subject im01. Now I am checking for errors and found some. I also found the solution steps. However, as far as I understand it, every time I make a change, this will be permanently done. Is there a way to keep the steps I have done so far and to same further changes to a new subject file, e.g. im01_skullcorrected. I would just like to backup the steps I have done in case I worsen the solution so far and in case I want to compare them. Also, is there a way that the results from recon-all are saved under a different name? Thanks, Maria -- Maria J. Felber, PhD Student Max Planck Institute for Human Cognitive and Brain Sciences Stephanstraße 1A 04103 Leipzig, Germany Phone: +49 341 9940-2465/2522

3 2

mris_convert exporting parcellation per vertex
by Pedro Paulo de Magalhães Oliveira Junior 15 Oct '11

15 Oct '11

I need to create a file where I have the parcellation value (structure name) per vertex. I've tried mris_convert --annot /Applications/freesurfer/subjects/bert/label/lh.aparc.annot /Applications/freesurfer/subjects/bert/surf/lh.pial parc.asc But I get the error: ERROR: unknown file annot file type specified for output: saida.asc Has someone done this before? Thanks --------------------------------------------------------------------- Pedro Paulo de Magalhães Oliveira Junior Netfilter & SpeedComm Telecom -- www.netfilter.com.br -- For mobile: http://itunes.apple.com/br/artist/netfilter/id365306441

5 12

Jump to page:

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Freesurfer