file size after using @auto_tlrc

AFNI Message Board
1

Hi experts,

I am using @auto_tlrc to convert my resting-state fMRI data to MNI152. After that, about 1/3 of the files get really huge. The input data is 610MB. The shape of nii file is [100 100 80 200]. The output data is 3.71GB, with shape [161 191 151 200]. The others (2/3 participants’ files) are 138.3MB, with shape [54 64 50 200]. I wonder which one is correct. Dose files usually get larger after the convert or smaller?

Here’s the code:
[quote=“@auto_tlrc \
#-dxyz 3 \
-pad_base 50 \
-apar $i.T1.tc+tlrc \
-pad_input 50 \
-prefix $i.rs.tc \
-input $i.rs.aptopup_al+orig.BRIK”]
[/quote]

Thank you in advance!

Xiyue

2

Hi Xiyue,

That is simply because of the the larger box of the result.
161x191x151 / (10010080) ~= 5.8, and 610 * 5.8 = 3538.

I don’t think that @auto_tlrc has any ability to specify a master output grid. But it would be possible to extract the warp and apply it with 3dAllineate instead, which does have a -master option for the output grid.

Are the voxels really 1mm isotropic? What was the original voxel size?

  • rick
3

Hi Rick,
Thank you for your reply. The orignal rs file size is [80 80 45 200], voxel: 3 mm x 3 mm x 3 mm, 200 volumes.
Maybe I need to set -dxyz 3 in this case?

Xiyue

4

That is what we tend to do, stay close to the original voxel size. When processed with afni_proc.py, the output is (by default, at least) isotropic based on the minimum voxel dimension, truncated to 3 significant bits. Here, 3mm is already truncated, and would be used for the result.

Going from 3mm^3 to 1mm^3 is a factor of 27 in data storage, that would make a big difference. Using 3mm would not be as pretty, but it would be more honest to the data, in some sense.

  • rick
5

Oh I see. Thank you so much Rick!