Forum Replies Created
-
Posts
-
To get WM GM CSF, you can use subject.pvc.frac.nii.gz. This file contains tissue fractions, i.e. amount of GM WM CSF at each voxel in the brain. Since at MR resolution, many voxels contain mixture of tissue, so this is a more realistic representation than the crisp segmentation.
If you want information about WM GM CSF Skull and Scalp, you can use this file and subject.skull.label.nii.gz.To add USCBrain as a dropdown entry in the svreg menu, please follow first section in the link below.
http://brainsuite.org/using-atlases/It seems that you are using old version of the software/MCR. Can you please upgrade to the latest version and try again? Please make sure that the computer has sufficient memory free (>=4GB). You can do that by closing as many background applications as possible.
Hi Minoru,
In order to compute cortical thickness accurately, please refer to #10 here
http://brainsuite.org/processing/svreg/svreg_modules/
The name of the module is thicknessPVC.
AnandPlease edit the brainsuite_labeldescription.xml file according to the edits that you have made.
This file is located in the BrainSuiteAtlas1 directory that you are editing. It contains ROI ID, ROI name and colors for the ROIs.
BrainSuite associates ROI IDs and label names using this file.Hi Luca,
Did you try the command as mentioned above?
You should run the subject with SVReg and with BrainSuiteAtlas1 as the atlas. Then run
/home/ajoshi/BrainSuite16a1/svreg/bin/svreg_apply_map.sh subname.svreg.inv.map.nii.gz fa_map.nii.gz out_fa.nii.gz /home/ajoshi/BrainSuite16a1/svreg/BrainSuiteAtlas1/mri.bfc.nii.gzHi You need the MCR for 2015b. Good to know that the MCR is now installed. Did you reboot the computer after that? Please let me know if you still have the issue.
I think the best way is to demarcate the spherical ROIs on the BrainSuiteAtlas1. If you already have the ROIs in MNI space, then you have to somehow merge them with BrainSuiteAtlas1. BrainSuiteAtlas1 is in MNI space, so it should be easy.
Make a custom atlas with all the ROIs that you would be using.http://brainsuite.org/processing/svreg/creating-custom-atlases/
And then proceed with the BrainSuite, SVReg and BDP sequences as usual. The new ROIs will be incorporated into your analysis.
Hi Luca,
Can you please describe what you would like to do? The image analysis of diffusion tensors is done by BDP and TrackConnect and you would not need coregistration of the results to the MNI space.
http://brainsuite.org/tutorials/dtiexercise/
http://neuroimage.usc.edu/neuro/Resources/TractConnectIf you do need to transform your data to a common MNI space,
you can apply inverse map generated by svreg as follows:
Use svreg_apply_map.sh binary provided with svreg. Here is how I would do this on Linux, though the corresponding binaries are available in Linux, Windows and Mac, so similar command will work./home/ajoshi/BrainSuite16a1/svreg/bin/svreg_apply_map.sh subname.svreg.inv.map.nii.gz fa_map.nii.gz out_fa.nii.gz /home/ajoshi/BrainSuite16a1/svreg/BrainSuiteAtlas1/mri.bfc.nii.gz
thanks,
AnandThere is no limitation on voxel size but 1mm is generally optimal for T1 brain scans. In our experience, BrainSuite generally works with tumor brains in the sense that the labeling of regions away from the tumor region is relatively unaffected. The execution time can vary for the tumor cases though it should not be too high. Did you get a chance to try normal brains with BrainSuite to make sure that the long execution time on your computer is not related to tumors?
If possible, it would also be helpful if you can post a screenshot of your results with the tumor.All the ROIs are stored in
*.svreg.label.nii.gz file.
The labels are actually 16-bit volumes, meaning that each structure label is a number between 0 and 65536. To analyze the scans automatically, it’s necessary to know which number corresponds to the region you’re interested in. Open the folder where the scan (and all of the files generated by the CSE sequence and SVReg) are located and open brainsuite_labeldescription.xmlThe statistics of cortical thickness, tissue volume etc are stored in
*roiwise.stats.txtok. it is probably related to the surface generated by BrainSuite is too complicated due to tumor. It would depend on shape of the tumor. I suggest first trying a normal brain. One example can be found here:
http://brainsuite.org/tutorials/cseexcercise/
If this brain also takes a lot of time then there is something wrong with the setup otherwise it is related to the tumor related cortical extraction issues.My guess is that due to tumor the surfaces generated by BrainSuite have too many faces or vertices. This is causing slowing down of the processes. You can try clicking surface registration only checkbox at the beginning of svreg sequence if you need only surface labels.
What is the voxel resolution of the MRIs? As long as it is around 1mm it should not matter too much.
