I am using dwi2response on the HCP Diffusion dataset in order to get the response function of DWI images to generate the track of fibers. However, when running this command, it gets stuck for about 4h+ at this step (shown below). And I think it is abnormal and weird.
dwi2response msmt_5tt degibbs.mif 5ttseg.mif ms_5tt_wm.txt ms_5tt_gm.txt \ ms_5tt_csf.txt -voxels ms_5tt_voxels.mif dwi2response: dwi2response: Note that this script makes use of commands / algorithms that have relevant articles for citation. Please consult the help page (-help option) for more information. dwi2response: dwi2response: Generated scratch directory: /Users/star_volcano/Desktop/100206/T1w/Diffusion/dwi2response-tmp-LP1BO3/ dwi2response: Importing DWI data (/Users/star_volcano/Desktop/100206/T1w/Diffusion/degibbs.mif)...
I am using a MacBook pro with M1pro and 16G memory.
The information of the DWI image which I would like to process is as below(4.1G):
Dimensions: 145 x 174 x 145 x 288 Voxel size: 1.25 x 1.25 x 1.25 x 1 Data strides: [ -1 2 3 4 ] Format: MRtrix Data type: 32 bit float (little endian) Intensity scaling: offset = 0, multiplier = 1 Transform: 1 0 0 -90 -0 1 0 -126 -0 0 1 -72 command_history: mrconvert -fslgrad bvecs bvals data.nii.gz DTI.mif (version=3.0.3) dwidenoise DTI.mif denoise.mif -noise noiselevel.mif -mask preproc_mask.mif (version=3.0.3) mrdegibbs denoise.mif degibbs.mif (version=3.0.3) comments: FSL5.0 dw_scheme: 0.5421861165,0.6720491444,-0.5043651084,5 [288 entries] -0.918106027,-0.306174009,-0.2516720074,1000 ... -0.9878630087,0.008873000078,-0.1550740014,1995 -0.46236328,0.6267413795,0.6272283798,3000 mrtrix_version: 3.0.3
I have seen a similar problem on this topic: Why does dwi2response take so long with multi shell data.
However, the installation method of MRtrix3 has changed a lot compared with the year 2019, I can’t solve the problem using the way mentioned in that topic.
Is there any solution?
By the way, I am going to process about 1,000 images in the HCP dataset. Can I accelerate
calculation with GPU or using parallelization?
Thanks a lot!