PMJ-based CSA

CSA measure based on distance from pontomedullary junction (PMJ)

👉 Please cite this work if you use it or if you are inspired by it:

Bédard S, Cohen-Adad J. Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. Frontiers in Neuroimaging [Internet]. 2022;1. Available from: https://www.frontiersin.org/articles/10.3389/fnimg.2022.1031253

Bédard, S., Bouthillier, M. & Cohen-Adad, J. (2023) Pontomedullary junction as a reference for spinal cord cross-sectional area: validation across neck positions. Sci Rep 13, 13527. https://doi.org/10.1038/s41598-023-40731-3

Table of contents

• Data collection and organization
• Manual labeling of spinal cord rootlets with FSLeyes
  • Installation
  • Run manual labeling script
• Analysis pipeline

Data collection and organization

The data is on OpenNeuro: https://openneuro.org/datasets/ds004507/versions/1.0.1

If used, please cite as:

S. Bédard and J. Cohen-Adad (2023). Spinal Cord Head Positions. OpenNeuro. [Dataset] doi: doi:10.18112/openneuro.ds004507.v1.0.1

Manual labeling of spinal cord segments using the nerve rootlets with FSLeyes

Installation

Download this repository:

git clone https://github.com/sct-pipeline/pmj-based-csa.git

Run manual labeling script

1. In the terminal, go into this repository

cd pmj-based-csa

2. Run the correction script specifying path-data and path-output

sct_run_batch -jobs 3 -path-data <PATH-DATA> -path-output ~/pmj_csa_nerve_roots_results -script identify_nerve.sh

Wait until the 3 images of the same subject (headDown, headUp, headNormal) open in FSLeyes, follow the steps:

4. Click on: Tools → Edit mode
5. Click on: Edit (ortho view) → Create mask
6. Change parameter Fill value according to the spinal level :
7. Locate the C2 spinal root using the sagittal and axial views (usually around C1 vertebral level)
8. Identify the slices that cover the C2 spinal root using the coronal and axial views (see schematic below)
9. Place the label 2 at the center of the spinal cord on the median axial slice that cover the spinal root
10. Erase the 3 voxels we don’t want (verify that the label is only one voxel)
11. Repeat steps 3 to 5 for each spinal root by modifying the parameter Fill value:
    • C3 → 3, C4 → 4, C5 → 5, … , C8 → 8, T1 → 9, …, T4 → 12, …
    • Stop when the contrast is not strong enough to accurately label the spinal roots
12. Save the mask under the same folder than the image with the suffix _spinalroots.nii.gz

13. Repeat step 4 to 12 for every image (the FSLeyes window will automatically open)

Analysis pipeline

Dependencies

• SCT v5.4
• Python 3.7

Installation

Download this repository:

git clone https://github.com/sct-pipeline/pmj-based-csa
cd pmj-based-csa
conda env create -n venv-pmj -f requirements.txt

Neck positions analysis

The data processing includes:

• Spinal cord segmentation
• Vertebral labeling
• Pontomedullary junction (PMJ) labeling
• CSA computation (dics, nerves and PMJ)
• Distance between nerve rootlets and PMJ
• Distance between nerve rootlets and discs

To launch processing, run:

cd pmj-based-csa
sct_run_batch -jobs -1 -path-data <PATH_DATA> -path-out ~/pmj-based-csa_results -script process_data.sh

To compute the statistics about the distances and CSA, run the following command:

To run analysis

python analyse_csa_results.py -path-results ~/pmj-based-csa_results/results/