Getting Started

To get started using SCT, you may wish to read our 2024 Review “Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox”.

For a more hands-on approach, you can take a look at the Batch Processing Example, or follow the longer Tutorials materials.

Batch Processing Example

The best way to learn how to use SCT is to look at the example batch_processing script. This script performs a typical analysis of multi-parametric MRI data, including cross-sectional area measurements, magnetization transfer, diffusion tensor imaging metrics computation and extraction within specific tracts, and functional MRI pre-processing. This script will download an example dataset containing T1w, T2w, T2*w, MT, DTI and fMRI data.

To launch the script run:


While the script is running, we encourage you to understand the meaning of each command line that is listed in the script. Comments are here to help justify some choices of parameters. If you have any question, please do not hesitate to ask for Help.

The script source is shown below:

# Example of commands to process multi-parametric data of the spinal cord.
# Please note that this batch script has a lot of redundancy and should not
# be used as a pipeline for regular processing. For example, there is no need
# to process both t1 and t2 to extract CSA values.
# For information about acquisition parameters, see:
# N.B. The parameters are set for these type of data. With your data, parameters
# might be slightly different.
# Usage:
#   [option] ./
#   Prevent (re-)downloading sct_example_data:
#   Specify quality control (QC) folder (Default is ~/qc_batch_processing):
#   SCT_BP_QC_FOLDER=/user/toto/my_qc_folder ./

# Abort on error
set -ve

# For full verbose, uncomment the next line
# set -x

# Fetch OS type
if uname -a | grep -i  darwin > /dev/null 2>&1; then
  # OSX
elif uname -a | grep -i  linux > /dev/null 2>&1; then
  # Linux

# Check if users wants to use his own data
if [[ -z "$SCT_BP_DOWNLOAD" ]]; then

# QC folder
if [[ -z "$SCT_BP_QC_FOLDER" ]]; then

# Remove QC folder
if [ -z "$SCT_BP_NO_REMOVE_QC" ] && [ -d "$SCT_BP_QC_FOLDER" ]; then
  echo "Removing $SCT_BP_QC_FOLDER folder."
  rm -rf "$SCT_BP_QC_FOLDER"

# get starting time:
start=$(date +%s)

# download example data
if [[ "$SCT_BP_DOWNLOAD" == "1" ]]; then
  sct_download_data -d sct_example_data
cd "$SCT_DIR/data/sct_example_data"

# t2
# ===========================================================================================
cd t2
# Segment spinal cord
sct_deepseg_sc -i t2.nii.gz -c t2 -qc "$SCT_BP_QC_FOLDER"
# Tips: If you are not satisfied with the results you can try with another algorithm:
# sct_propseg -i t2.nii.gz -c t2 -qc "$SCT_BP_QC_FOLDER"
# Fit binarized centerline from SC seg (default settings)
sct_get_centerline -i t2_seg.nii.gz -method fitseg -qc "$SCT_BP_QC_FOLDER"
# Fit soft centerline from SC seg
sct_get_centerline -i t2_seg.nii.gz -method fitseg -centerline-soft 1 -o t2_seg_centerline_soft.nii.gz -qc "$SCT_BP_QC_FOLDER"
# Vertebral labeling
# Tips: for manual initialization of labeling by clicking at disc C2-C3, use flag -initc2
sct_label_vertebrae -i t2.nii.gz -s t2_seg.nii.gz -c t2 -qc "$SCT_BP_QC_FOLDER"
# Create labels at in the cord at C2 and C5 mid-vertebral levels
sct_label_utils -i t2_seg_labeled.nii.gz -vert-body 2,5 -o labels_vert.nii.gz
# Tips: you can also create labels manually using:
# sct_label_utils -i t2.nii.gz -create-viewer 2,5 -o labels_vert.nii.gz
# Register to template
sct_register_to_template -i t2.nii.gz -s t2_seg.nii.gz -l labels_vert.nii.gz -c t2 -qc "$SCT_BP_QC_FOLDER"
# Tips: If you are not satisfied with the results, you can tweak registration parameters.
# For example here, we would like to take into account the rotation of the cord, as well as
# adding a 3rd registration step that uses the image intensity (not only cord segmentations).
# so we could do something like this:
# sct_register_multimodal -i "$SCT_DIR/data/PAM50/template/PAM50_t2s.nii.gz -iseg "$SCT_DIR/data/PAM50/template/PAM50_cord.nii.gz -d t2s.nii.gz -dseg t2s_seg.nii.gz -param step=1,type=seg,algo=slicereg,smooth=3:step=2,type=seg,algo=bsplinesyn,slicewise=1,iter=3 -initwarp ../t2/warp_template2anat.nii.gz
# Warp template without the white matter atlas (we don't need it at this point)
sct_warp_template -d t2.nii.gz -w warp_template2anat.nii.gz -a 0
# Compute cross-sectional area (and other morphometry measures) for each slice
sct_process_segmentation -i t2_seg.nii.gz
# Compute cross-sectional area and average between C2 and C3 levels
sct_process_segmentation -i t2_seg.nii.gz -vert 2:3 -o csa_c2c3.csv
# Compute cross-sectionnal area based on distance from pontomedullary junction (PMJ)
# Detect PMJ
sct_detect_pmj -i t2.nii.gz -c t2 -qc "$SCT_BP_QC_FOLDER" 
# Compute cross-section area at 60 mm from PMJ averaged on a 30 mm extent
sct_process_segmentation -i t2_seg.nii.gz -pmj t2_pmj.nii.gz -pmj-distance 60 -pmj-extent 30 -qc "$SCT_BP_QC_FOLDER" -qc-image t2.nii.gz -o csa_pmj.csv
# Compute morphometrics in PAM50 anatomical dimensions
sct_process_segmentation -i t2_seg.nii.gz -vertfile t2_seg_labeled.nii.gz -perslice 1 -normalize-PAM50 1 -o csa_pam50.csv
# Go back to root folder
cd ..

# t2s (stands for t2-star)
# ===========================================================================================
cd t2s
# Spinal cord segmentation
sct_deepseg_sc -i t2s.nii.gz -c t2s -qc "$SCT_BP_QC_FOLDER"
# Segment gray matter
sct_deepseg_gm -i t2s.nii.gz -qc "$SCT_BP_QC_FOLDER"
# Register template->t2s (using warping field generated from template<->t2 registration)
sct_register_multimodal -i "$SCT_DIR/data/PAM50/template/PAM50_t2s.nii.gz" -iseg "$SCT_DIR/data/PAM50/template/PAM50_cord.nii.gz" -d t2s.nii.gz -dseg t2s_seg.nii.gz -param step=1,type=seg,algo=centermass:step=2,type=seg,algo=bsplinesyn,slicewise=1,iter=3:step=3,type=im,algo=syn,slicewise=1,iter=1,metric=CC -initwarp ../t2/warp_template2anat.nii.gz -initwarpinv ../t2/warp_anat2template.nii.gz -owarp warp_template2t2s.nii.gz -owarpinv warp_t2s2template.nii.gz
# Warp template
sct_warp_template -d t2s.nii.gz -w warp_template2t2s.nii.gz
# Subtract GM segmentation from cord segmentation to obtain WM segmentation
sct_maths -i t2s_seg.nii.gz -sub t2s_gmseg.nii.gz -o t2s_wmseg.nii.gz
# Compute cross-sectional area of the gray and white matter between C2 and C5
sct_process_segmentation -i t2s_wmseg.nii.gz -vert 2:5 -perlevel 1 -o csa_wm.csv -angle-corr-centerline t2s_seg.nii.gz
sct_process_segmentation -i t2s_gmseg.nii.gz -vert 2:5 -perlevel 1 -o csa_gm.csv -angle-corr-centerline t2s_seg.nii.gz
# OPTIONAL: Update template registration using information from gray matter segmentation
# # <<<
# # Register WM/GM template to WM/GM seg
# sct_register_multimodal -i "$SCT_DIR/data/PAM50/template/PAM50_wm.nii.gz" -d t2s_wmseg.nii.gz -dseg t2s_seg.nii.gz -param step=1,type=im,algo=syn,slicewise=1,iter=5 -initwarp warp_template2t2s.nii.gz -initwarpinv warp_t2s2template.nii.gz -qc "$SCT_BP_QC_FOLDER" -owarp warp_template2t2s.nii.gz -owarpinv warp_t2s2template.nii.gz
# # Warp template (this time corrected for internal structure)
# sct_warp_template -d t2s.nii.gz -w warp_template2t2s.nii.gz
# # >>>
cd ..

# t1
# ===========================================================================================
cd t1
# Contrast-agnostic registration
# 1. t1w preprocessing (cropping around spinal cord)
sct_deepseg_sc -i t1.nii.gz -c t1 -centerline cnn
sct_create_mask -i t1.nii.gz -p centerline,t1_seg.nii.gz -size 35mm -f cylinder -o mask_t1.nii.gz
sct_crop_image -i t1.nii.gz -m mask_t1.nii.gz
# 2. t2w preprocessing (cropping around spinal cord)
cp ../t2/t2.nii.gz ./t2.nii.gz
sct_deepseg_sc -i t2.nii.gz -c t2
sct_create_mask -i t2.nii.gz -p centerline,t2_seg.nii.gz -size 35mm -f cylinder -o mask_t2.nii.gz
sct_crop_image -i t2.nii.gz -m mask_t2.nii.gz
# 3. Perform registration
sct_register_multimodal -i t1_crop.nii.gz -d t2_crop.nii.gz -param step=1,type=im,algo=dl

# Other useful utilities
# 1. Smooth spinal cord along superior-inferior axis
sct_smooth_spinalcord -i t1.nii.gz -s t1_seg.nii.gz
# 2. Flatten cord in the right-left direction (to make nice figure)
sct_flatten_sagittal -i t1.nii.gz -s t1_seg.nii.gz
# Go back to root folder
cd ..

# mt
# ===========================================================================================
cd mt
# Get centerline from mt1 data
sct_get_centerline -i mt1.nii.gz -c t2
# sct_get_centerline -i mt1.nii.gz -c t2 -qc "$SCT_BP_QC_FOLDER"
# Create mask
sct_create_mask -i mt1.nii.gz -p centerline,mt1_centerline.nii.gz -size 45mm
# Crop data for faster processing
sct_crop_image -i mt1.nii.gz -m mask_mt1.nii.gz -o mt1_crop.nii.gz
# Segment spinal cord
sct_deepseg_sc -i mt1_crop.nii.gz -c t2 -qc "$SCT_BP_QC_FOLDER"
# Register mt0->mt1
# Tips: here we only use rigid transformation because both images have very similar sequence parameters. We don't want to use SyN/BSplineSyN to avoid introducing spurious deformations.
# Tips: here we input -dseg because it is needed by the QC report
sct_register_multimodal -i mt0.nii.gz -d mt1_crop.nii.gz -dseg mt1_crop_seg.nii.gz -param step=1,type=im,algo=slicereg,metric=CC -x spline -qc "$SCT_BP_QC_FOLDER"
# Register template->mt1
# Tips: here we only use the segmentations due to poor SC/CSF contrast at the bottom slice.
# Tips: First step: slicereg based on images, with large smoothing to capture potential motion between anat and mt, then at second step: bpslinesyn in order to adapt the shape of the cord to the mt modality (in case there are distortions between anat and mt).
sct_register_multimodal -i "$SCT_DIR/data/PAM50/template/PAM50_t2.nii.gz" -iseg "$SCT_DIR/data/PAM50/template/PAM50_cord.nii.gz" -d mt1_crop.nii.gz -dseg mt1_crop_seg.nii.gz -param step=1,type=seg,algo=slicereg,smooth=3:step=2,type=seg,algo=bsplinesyn,slicewise=1,iter=3 -initwarp ../t2/warp_template2anat.nii.gz -initwarpinv ../t2/warp_anat2template.nii.gz -owarp warp_template2mt.nii.gz -owarpinv warp_mt2template.nii.gz
# Warp template
sct_warp_template -d mt1_crop.nii.gz -w warp_template2mt.nii.gz -qc "$SCT_BP_QC_FOLDER"
# Compute mtr
sct_compute_mtr -mt0 mt0_reg.nii.gz -mt1 mt1_crop.nii.gz
# Register t1w->mt1
# Tips: We do not need to crop the t1w image before registration because step=0 of the registration is to put the source image in the space of the destination image (equivalent to cropping the t1w)
sct_register_multimodal -i t1w.nii.gz -d mt1_crop.nii.gz -dseg mt1_crop_seg.nii.gz -param step=1,type=im,algo=slicereg,metric=CC -x spline -qc "$SCT_BP_QC_FOLDER"
# Compute MTsat
# Tips: Check your TR and Flip Angle from the Dicom data
sct_compute_mtsat -mt mt1_crop.nii.gz -pd mt0_reg.nii.gz -t1 t1w_reg.nii.gz -trmt 0.030 -trpd 0.030 -trt1 0.015 -famt 9 -fapd 9 -fat1 15
# Extract MTR, T1 and MTsat within the white matter between C2 and C5.
# Tips: Here we use "-discard-neg-val 1" to discard inconsistent negative values in MTR calculation which are caused by noise.
sct_extract_metric -i mtr.nii.gz -method map -o mtr_in_wm.csv -l 51 -vert 2:5
sct_extract_metric -i mtsat.nii.gz -method map -o mtsat_in_wm.csv -l 51 -vert 2:5
sct_extract_metric -i t1map.nii.gz -method map -o t1_in_wm.csv -l 51 -vert 2:5
# Bring MTR to template space (e.g. for group mapping)
sct_apply_transfo -i mtr.nii.gz -d "$SCT_DIR/data/PAM50/template/PAM50_t2.nii.gz" -w warp_mt2template.nii.gz
# Go back to root folder
cd ..

# dmri
# ===========================================================================================
cd dmri
# bring t2 segmentation in dmri space to create mask (no optimization)
sct_dmri_separate_b0_and_dwi -i dmri.nii.gz -bvec bvecs.txt 
sct_register_multimodal -i ../t2/t2_seg.nii.gz -d dmri_dwi_mean.nii.gz -identity 1 -x nn
# create mask to help moco and for faster processing
sct_create_mask -i dmri_dwi_mean.nii.gz -p centerline,t2_seg_reg.nii.gz -size 35mm
# crop data
sct_crop_image -i dmri.nii.gz -m mask_dmri_dwi_mean.nii.gz -o dmri_crop.nii.gz
# motion correction
# Tips: if data have very low SNR you can increase the number of successive images that are averaged into group with "-g". Also see: sct_dmri_moco -h
sct_dmri_moco -i dmri_crop.nii.gz -bvec bvecs.txt
# segment spinal cord
sct_deepseg_sc -i dmri_crop_moco_dwi_mean.nii.gz -c dwi -qc "$SCT_BP_QC_FOLDER"
# Generate QC for sct_dmri_moco ('dmri_crop_moco_dwi_mean_seg.nii.gz' is needed to align each slice in the QC mosaic)
sct_qc -i dmri_crop.nii.gz -d dmri_crop_moco.nii.gz -s dmri_crop_moco_dwi_mean_seg.nii.gz -p sct_dmri_moco -qc "$SCT_BP_QC_FOLDER"
# Register template to dwi
# Tips: Again, here, we prefer to stick to segmentation-based registration. If there are susceptibility distortions in your EPI, then you might consider adding a third step with bsplinesyn or syn transformation for local adjustment.
sct_register_multimodal -i "$SCT_DIR/data/PAM50/template/PAM50_t1.nii.gz" -iseg "$SCT_DIR/data/PAM50/template/PAM50_cord.nii.gz" -d dmri_crop_moco_dwi_mean.nii.gz -dseg dmri_crop_moco_dwi_mean_seg.nii.gz -param step=1,type=seg,algo=centermass:step=2,type=seg,algo=bsplinesyn,metric=MeanSquares,smooth=1,iter=3 -initwarp ../t2/warp_template2anat.nii.gz -initwarpinv ../t2/warp_anat2template.nii.gz -qc "$SCT_BP_QC_FOLDER" -owarp warp_template2dmri.nii.gz -owarpinv warp_dmri2template.nii.gz
# Warp template and white matter atlas
sct_warp_template -d dmri_crop_moco_dwi_mean.nii.gz -w warp_template2dmri.nii.gz -qc "$SCT_BP_QC_FOLDER"
# Compute DTI metrics
# Tips: The flag -method "restore" allows you to estimate the tensor with robust fit (see: sct_dmri_compute_dti -h)
sct_dmri_compute_dti -i dmri_crop_moco.nii.gz -bval bvals.txt -bvec bvecs.txt
# Compute FA within right and left lateral corticospinal tracts from slices 2 to 14 using weighted average method
sct_extract_metric -i dti_FA.nii.gz -z 2:14 -method wa -l 4,5 -o fa_in_cst.csv
# Bring metric to template space (e.g. for group mapping)
sct_apply_transfo -i dti_FA.nii.gz -d "$SCT_DIR/data/PAM50/template/PAM50_t2.nii.gz" -w warp_dmri2template.nii.gz
# Go back to root folder
cd ..

# fmri
# ===========================================================================================
cd fmri
# Average all fMRI time series (to be able to do the next step)
sct_maths -i fmri.nii.gz -mean t -o fmri_mean.nii.gz
# Get cord centerline
sct_get_centerline -i fmri_mean.nii.gz -c t2s
# Create mask around the cord to help motion correction and for faster processing
sct_create_mask -i fmri_mean.nii.gz -p centerline,fmri_mean_centerline.nii.gz -size 35mm
# Crop data
sct_crop_image -i fmri.nii.gz -m mask_fmri_mean.nii.gz -o fmri_crop.nii.gz
# Motion correction
# Tips: Here data have sufficient SNR and there is visible motion between two consecutive scans, so motion correction is more efficient with -g 1 (i.e. not average consecutive scans)
sct_fmri_moco -i fmri_crop.nii.gz -g 1
# Segment spinal cord manually
#   Since these data have very poor cord/CSF contrast, it is difficult to segment the cord properly using sct_deepseg_sc
#   and hence in this case we do it manually. The file is called: fmri_crop_moco_mean_seg_manual.nii.gz
#   There is no command for this step, because the file is included in the 'sct_example_data' dataset.
# Generate QC for sct_fmri_moco ('fmri_crop_moco_mean_seg_manual.nii.gz' is needed to align each slice in the QC mosaic)
sct_qc -i fmri_crop.nii.gz -d fmri_crop_moco.nii.gz -s fmri_crop_moco_mean_seg_manual.nii.gz -p sct_fmri_moco -qc "$SCT_BP_QC_FOLDER"
# Register template->fmri
sct_register_multimodal -i "$SCT_DIR/data/PAM50/template/PAM50_t2.nii.gz" -iseg "$SCT_DIR/data/PAM50/template/PAM50_cord.nii.gz" -d fmri_crop_moco_mean.nii.gz -dseg fmri_crop_moco_mean_seg_manual.nii.gz -param step=1,type=seg,algo=slicereg,metric=MeanSquares,smooth=2:step=2,type=im,algo=bsplinesyn,metric=MeanSquares,iter=5,gradStep=0.5 -initwarp ../t2/warp_template2anat.nii.gz -initwarpinv ../t2/warp_anat2template.nii.gz -qc "$SCT_BP_QC_FOLDER" -owarp warp_template2fmri.nii.gz -owarpinv warp_fmri2template.nii.gz
# Warp template, including spinal levels (here we don't need the WM atlas)
sct_warp_template -d fmri_crop_moco_mean.nii.gz -w warp_template2fmri.nii.gz -a 0
# Note, once you have computed fMRI statistics in the subject's space, you can use
# warp_fmri2template.nii.gz to bring the statistical maps on the template space, for group analysis.

# Display results (to easily compare integrity across SCT versions)
# ===========================================================================================
set +v
end=$(date +%s)
echo "~~~"  # these are used to format as code when copy/pasting in github's markdown
echo "Version:         $(sct_version)"
echo "Ran on:          $(uname -nsr)"
echo "Duration:        $((runtime / 3600))hrs $(((runtime / 60) % 60))min $((runtime % 60))sec"
echo "---"
# The file `` will output tested values when run as a script
"$SCT_DIR"/python/envs/venv_sct/bin/python "$SCT_DIR"/testing/batch_processing/ ||
"$SCT_DIR"/python/envs/venv_sct/python.exe "$SCT_DIR"/testing/batch_processing/
echo "~~~"

# Display syntax to open QC report on web browser
echo "To open Quality Control (QC) report on a web-browser, run the following:"
echo "$open_command $SCT_BP_QC_FOLDER/index.html"

If you would like to get more examples about what SCT can do, please visit the sct-pipeline repository. Each repository is a pipeline dedicated to a specific research project.