Inspecting the results of processing#

After running the entire pipeline, you should have all results under the output/results/ folder.

CSA for T2 data#

Here for example, we show the mean CSA averaged between C2-C3 levels computed from the T2 data. Each line represents a subject.

CSA.csv: CSA values in T2 data across 3 subjects#

Filename

Slice (I->S)

VertLevel

MEAN(area)

STD(area)

sub-01_T2w_seg.nii.gz

161:203

2:3

83.5690681039387

2.82182217691781

sub-03_T2w_seg.nii.gz

159:199

2:3

60.6215201725797

2.87402688226702

sub-05_T2w_seg.nii.gz

159:190

2:3

68.763579167899

3.19840203029727

The variability is mainly due to the inherent variability of CSA across subjects.

MTR in white matter#

Here are the results of MTR quantification in the dorsal column of each subject between C2 and C5. Notice the remarkable inter-subject consistency.

MTR_in_DC.csv: MTR values values in white matter (dorsal columns) across 3 subjects#

Filename

Slice (I->S)

VertLevel

Label

Size [vox]

MAP()

STD()

sub-01/anat/mtr.nii.gz

3:16

2:5

dorsal columns

370.15204844543

49.7198531140872

4.99870360243909

sub-03/anat/mtr.nii.gz

3:15

2:5

dorsal columns

282.686966627213

49.3521578793729

5.19981366995629

sub-05/anat/mtr.nii.gz

4:13

2:5

dorsal columns

229.620127825366

49.2113270517532

4.76773091479419

Quality Control report#

A QC report is generated under qc/. As shown before, the QC report is useful to quickly assess the quality of the analysis pipeline.

https://raw.githubusercontent.com/spinalcordtoolbox/doc-figures/master/batch-processing-of-subjects/qc.png