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tortoise_dti [2014/06/27 12:20]
pmolfese
tortoise_dti [2014/06/27 12:22]
pmolfese
Line 6: Line 6:
 ====== Preprocessing Diffusion Data ====== ====== Preprocessing Diffusion Data ======
  
-Start by launching ​DIFF_CALC.  You can simply double-click on the file prepvm.  ​+Start by launching ​DIFF_PREP.  You can simply double-click on the file prepvm.  ​
  
 The first thing to do is get your data into TORTOISE. ​ You can import a variety of data formats (Phillips PAR/REC, DICOM, NIFTI, and Bruker). ​ I highly recommend importing from either DICOM or FSL NIFTI. ​ If you choose DICOM, you will need to supply TORTOISE with your gradient file, as it will not read the directions from your DICOM headers. ​ If you choose FSL NIFTI, TORTOISE will read your bvec and bval files from text, they just need to have the name (bvec or bval) in the filename and be in the same folder as the NIFTI file.  TORTOISE will search sub-folders for these files, so if you have multiple bvec or bval files in your folder or sub-folders,​ it will give you an error that it found multiple bvec/bval files and you’ll have to tidy up your file structure. The first thing to do is get your data into TORTOISE. ​ You can import a variety of data formats (Phillips PAR/REC, DICOM, NIFTI, and Bruker). ​ I highly recommend importing from either DICOM or FSL NIFTI. ​ If you choose DICOM, you will need to supply TORTOISE with your gradient file, as it will not read the directions from your DICOM headers. ​ If you choose FSL NIFTI, TORTOISE will read your bvec and bval files from text, they just need to have the name (bvec or bval) in the filename and be in the same folder as the NIFTI file.  TORTOISE will search sub-folders for these files, so if you have multiple bvec or bval files in your folder or sub-folders,​ it will give you an error that it found multiple bvec/bval files and you’ll have to tidy up your file structure.
  
-{{ :​tortoise.001.jpg?​direct&​400 |}}+{{ :​tortoise.001.jpg?​direct&​500 |}}
  
 In the image above (click to make bigger), you can see the common settings highlighted with yellow arrows. ​ On the import side, you’ll want to specify your format (FSL NIFTI is my recommendation),​ the file path to your NIFTI file (note – uncompressed NIFTI only -.nii not .nii.gz – as of this post), and then click import. ​ Using NIFTI files imported from dcm2nii and DIMON have worked well for me.  The one advantage to dcm2nii is that it will automatically create the bval and bvec files in the correct format for TORTOISE. In the image above (click to make bigger), you can see the common settings highlighted with yellow arrows. ​ On the import side, you’ll want to specify your format (FSL NIFTI is my recommendation),​ the file path to your NIFTI file (note – uncompressed NIFTI only -.nii not .nii.gz – as of this post), and then click import. ​ Using NIFTI files imported from dcm2nii and DIMON have worked well for me.  The one advantage to dcm2nii is that it will automatically create the bval and bvec files in the correct format for TORTOISE.
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 Next you’re going to click the “Registration param” button to setup the more advanced settings. Next you’re going to click the “Registration param” button to setup the more advanced settings.
  
-{{ :​tortoise.002.jpg?​direct&​400 |}}+{{ :​tortoise.002.jpg?​direct&​500 |}}
  
 Inside your Registration Settings, I highly recommend you keep most of the defaults. ​ The things that I’ve found reason to change (in TORTOISE version 2.0.1) are the final DWI voxelsize resolution, it defaults to 1.5mm^3, but it may not make sense to go to such a fine grid if you collect at say 3mm^3. ​ The other thing that I’ve changed is the initial upsampling setting, if you have a particularly thick slice DWI image, you might consider changing it to slice_only. ​ In the registration settings is also where you will turn off the BSpline correction if you aren’t using a T2-weighted image with fat suppression for your structural image. Inside your Registration Settings, I highly recommend you keep most of the defaults. ​ The things that I’ve found reason to change (in TORTOISE version 2.0.1) are the final DWI voxelsize resolution, it defaults to 1.5mm^3, but it may not make sense to go to such a fine grid if you collect at say 3mm^3. ​ The other thing that I’ve changed is the initial upsampling setting, if you have a particularly thick slice DWI image, you might consider changing it to slice_only. ​ In the registration settings is also where you will turn off the BSpline correction if you aren’t using a T2-weighted image with fat suppression for your structural image.
tortoise_dti.txt · Last modified: 2014/06/27 12:23 by pmolfese