Tck2connectome devours some streamlines

1

I am trying to estimate how many strealines are there in my .csv file, but when I calculate streamline counts it does not give all the streamlines that exist in my .tck file despite I ran tck2connectome with “-keep_unassigned” option.

I want to ask to professional MRtrix team, what is happening to missing streamlines?

tck2connectome -keep_unassigned -assignment_end_voxels -symmetric -zero_diagonal tracks_200k.tck coreg_hcpmmp.mif connectome_hcpmmp.csv -out_assignment assignments_hcpmmp.csv

But when I calculate the output with this code, I get the result of 240k. I am expecting to have 400k because there is 200k streamline in my .tck file

mrtrix_connectome = pd.read_csv(“path-to-connectome_hcpmmp.csv”, header=None)

total_sum = np.sum(np.sum(mrtrix_connectome)

print(“Total sum:”, total_sum)

2

Hi,

Check what happens if you remove the flag -zero_diagonal, maybe that’s the reason.

Best regards,

Manuel

3

that worked

thanks manuel

Sincerely

4

Dear Manuel,

Do you know how can I see a specific streamline from a .tck file?

Some streamlines are not associated to any node and some streamlines are only associated to one node, by using -keep_unassigned I can obtain their counts but I also want to see how they are projecting in the space

Sincerely

5

For example, In my assignments file 3rd streamline is not bound to any node

I want to see where is it

6

Hi,

Check the command connectome2tck, I don’t remember from the top of my head the options you need, but it should be straight forward.

Best regards,

Manuel