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PaulBautin edited this page Feb 22, 2024 · 10 revisions

Dataset Protocol

MRI Protocol

All participants were imaged using a 3.0 T MRI scanner (Philips Ingenia, Siemens, Canada) across T1-weighted and diffusion weighted contrasts. T1-weighted images (5 min) were obtained using a MAG prepared (MP) Gradient Recalled (GR) sequence with repetition time (RT) = 7.9 ms, echo time (TE) = 3.5 ms, flip angle = 8°, voxel size 1.0 × 1.0 × 1.0 mm3. Diffusion-weighted images (10 min) were obtained using segmented k-space (SK) Spin Echo (SE) sequence with repetition time (RT) = 4000 ms, echo time (TE) = 92 ms, flip angle = 90°, voxel size 2.0 × 2.0 × 2.0 mm3. For each subject 108 diffusion volumes (7 b = 0 mm2/s, 8 b = 300 mm2/s, 32 b = 1,000 mm2/s, 60 b = 2,000 mm2/s) were obtained including a b0 with reverse phase encoding for correcting susceptibility induced distortions.

functionnal: BOLD images were obtained using a 2D segmented k-space (SK) Fat Saturation (FS) Gradient recalled (GR) with repetition time (RT) = 1.075 s, echo time (TE) = 30 ms, flip angle = 55°, voxel size 3.0 × 3.0 × 3.0 mm3. For each subject 576 volumes were obtained including 1 reversed phase encoding volume for correcting susceptibility induced distortions. SENSE = 1.2 and multi band (MB) = 4

Inclusion/Exclusion criteria for CLBP participants

  • Age ≥ 18 ans
  • Has had CLBP for more than 16 weeks (4 months), with pain (with poor irradiation) towards the buttock or lower
  • Visual Analogue Scale score (VAS) ≥ 30 mm (maximum of 100 mm) within the last 24 hours
  • Must not use medication. Example: cortisone infiltration in the last 2 years, chonic opiodes, antidepressants

Dataset info

For this study 25 participants with chronic lower back pain (X males, X females) and 27 control participants (X males, X females) were recruited and scanned over a period of 6 months (X to X 2021) in Sherbrooke, Quebec, Canada.:

  • remove subject 18
  • subjects 37, 39, 8 have only visit v1
  • subject 16 has only visit v1 and v2
  • remove subject 4 v1 because no inverse b0

Dataset manipulation

Brain Imaging Data Structure

A tutorial on converting dicom files to BIDS format can be found at this website. First create a conda environment for dcm2bids and install dcm2bids dependencies:

# Activate environment
conda activate env_dcm2bids
# Install conda dependencies
conda install -c conda-forge dcm2bids
conda install -c conda-forge dcm2niix

Create scaffold for files running command: dcm2bids_scaffold -o bids_project

Enter the new folder and transfer the Dicom files to the sourcedata folder. You are now ready to build a configuration file. An example for our dataset can be found on this repository.

To run dcm2bids conversion we have created a script (present in this repository) that renames files before running dcm2bids. Run command: bash code/run_dicom_bids.sh

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