 |
 |
Hi Jesper, Thanks for the fast reply and offering to look at the data. I uploaded our original MRI in upload session *329707. * These are clinical data, so we will not be able to change the MRI parameters to use a longer echo. If you have any suggestions for any fixes we might try post-hoc, that would be excellent. Veronica On Mon, Oct 26, 2009 at 7:18 AM, Jesper Andersson <[log in to unmask]>wrote: > Dear Veronica, > > it is a little hard to known exactly what is going wrong. In general fnirt > can sometimes be quite sensitive to data that has been collected with a much > shorter echo-time than what the data constituting the template was. Which is > jargon for images with much higher intensity in the scrappy bits outside the > brain (meninges and stuff). This is something we are aware of and are > working to fix. > > My suspicion is that that is what you are seeing, caused by the high > intensities in the meninges just outside the visual cortex. If you want to > you can download the data to our server and I can take a look at it ( > http://www.fmrib.ox.ac.uk/cgi-bin/upload.cgi). > > Jesper > > > > On 24 Oct 2009, at 09:20, Veronica W. wrote: > > Hello, >> >> I have been trying to use FLIRT followed by FNIRT to map a patient MRI >> to the standard MNI 152 brain. This has produced distorted results, and I >> was hoping someone could help me troubleshoot. >> >> In specific, the distortion is that cortical gyri and sulci seem to be >> unusually shaped and do not match with the MNI_152 template. There >> are extra gyri in some locations and missing gyri in others. Also, there >> is an outward distortion at the top of the skull. >> >> Please see below for links to images, my code, and configurations. >> >> I thought the trouble might be the regularization settings, so I have >> tried >> a "small lambda" and "large lambda" configuration as well as using >> the standard configuration file. These configurations are listed below >> (and >> results for each are linked). The standard configuration file seems to >> produce the best results. >> >> Thanks in advance for any advice you might have! >> >> -Veronica W. >> Neuroscience Statistics Research Lab, MIT >> >> >> ##### LINKS TO IMAGES ########### >> >> 1. My original MRI >> http://web.mit.edu/vsw/www/FSL/Original_MRI.jpg >> >> 2. Template MNI 152 2mm brain >> http://web.mit.edu/vsw/www/FSL/MNI_2mm_template.jpg >> >> 3. Results using T1_2_MNI152_2mm.cnf >> http://web.mit.edu/vsw/www/FSL/Conversion_with_standard_2mm_cnf.jpg >> >> 4. Results with large lambda config. >> http://web.mit.edu/vsw/www/FSL/Conversion_with_high_lambda.jpg >> >> 5. Results with small lambda config. >> http://web.mit.edu/vsw/www/FSL/Conversion_with_low_lambda.jpg >> >> ##### CONVERSION SCRIPT ######### >> >> This is exactly as suggested on the FNIRT Example Uses page for >> "Registering T1-structural to MNI152" >> >> #1. Perform BET on MRI image >> bet $MRI $MRI_BETTED -f 0.5 -R >> >> #2. Use FLIRT to map betted MRI to MNI152_2mm_brain template >> flirt -in $MRI_BETTED -ref MNI152_T1_2mm_brain.nii.gz -out >> $MRI_FLIRTED_BETTED -omat $MRI_FLIRTED_BETTED_OMAT -searchrx -180 180 >> -searchry -180 180 -searchrz -180 180 -dof 12 -interp trilinear -bins 256 >> -cost corratio >> >> #3. Use FNIRT on raw MRI, using as an input the affine transformation >> matrix >> from Step 2 >> fnirt --in=$MRI --aff=$MRI_FLIRTED_BETTED_OMAT --cout=$FNIRT_OUT >> --config=$FNIRT_CNF_FILE >> >> #4 Apply warp >> applywarp --ref=MNI152_T1_2mm.nii.gz --in=$MRI --warp=$FNIRT_OUT >> --out=$MRI_FNIRTED >> >> ####### THREE CONFIGURATIONS I TRIED ############ >> >> 1. Standard 2mm cnf configuration file >> completely unchanged from T1_2_MNI152_2mm.cnf >> >> 2. Large lambda >> --ref=MNI152_T1_2mm.nii.gz >> --refmask=MNI152_T1_2mm_brain_mask_dil.nii.gz >> --imprefm=1 >> --impinm=1 >> --imprefval=0 >> --impinval=0 >> --subsamp=4,2,1,1 >> --miter=5,5,5,5 >> --infwhm=6,4,2,2 >> --reffwhm=4,2,0,0 >> --lambda=300,200,100,100 >> --estint=1,1,1,1 >> --applyrefmask=1,1,1,1 >> --applyinmask=1 >> --warpres=10,10,10 >> --ssqlambda=1 >> --regmod=bending_energy >> --intmod=global_non_linear_with_bias >> --intorder=5 >> --biasres=50,50,50 >> --biaslambda=10000 >> --refderiv=0 >> >> 3. Small lambda >> Same as above, with --lambda=300,75,30,10 >> >> >> Any advice about how to produce less distortion in the cortical gyri >> and sulci would be appreciated! Thank you. >> >>