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Spinal Cord Toolbox documentation
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Overview

  • Introduction
  • SCT Concepts
    • PAM50 Template
    • Point labeling conventions
    • Inspecting the results of your analysis (Quality Control, FSLeyes)
    • Voxels Space Orientation and Coordinate Conventions
    • Temporary Directories
    • Warping fields
  • Testimonials
  • Studies using SCT

User section

  • Installation
    • Installation for MacOS
    • Installation for Linux
    • Installation for Windows
  • Getting Started
  • SCT Courses
  • Tutorials
    • Segmentation
      • Before starting this tutorial
      • Contrasts
      • Algorithm #1: sct_propseg
      • Algorithm #2: sct_deepseg_sc
      • Choosing between segmentation algorithms
      • Hands-on: Using sct_deepseg_sc on T2 data
      • Fixing a failed sct_deepseg_sc segmentation
      • Specialized segmentation models: sct_deepseg
    • Vertebral labeling
      • Before starting this tutorial
      • Types of vertebral labels
      • Point labeling conventions
      • Labeling algorithm: sct_label_vertebrae
      • Applying the labeling algorithm
      • Alternative #1: Manually labeling the C2-C3 disc
      • Alternative #2: Manual labeling all labels
      • How many vertebral labels should I use for registration?
      • Extracting specific labels for registration
    • Shape analysis
      • Compute CSA (and other shape metrics)
        • Before starting this tutorial
        • Cross-sectional area (CSA)
        • Other shape metrics
        • Verify the correctness of the metrics
      • Quantify spinal cord compression
        • Before starting this tutorial
        • Normalization pipeline
        • Generate the necessary input files
        • Compute normalized morphometrics
    • Lesion analysis
      • Before starting this tutorial
      • Lesion segmentation in spinal cord injury (SCI)
      • Lesion segmentation in multiple sclerosis (MS)
      • Compute lesion morphometric measures
      • Template/Atlas-based lesion analysis
    • Spinal nerve rootlets segmentation
      • Before starting this tutorial
      • Spinal nerve rootlets segmentation
    • Registration to template
      • Registering labeled anatomical images to the PAM50 template
        • Before starting this tutorial
        • Registration algorithm: sct_register_to_template
        • Applying the registration algorithm
        • Customizing the registration command
        • Transforming the template using warping fields
      • Coregistering additional data (MT, DT) to the PAM50 template
        • Before starting this tutorial
        • Spinal cord segmentation for MT1 data
        • Creating a mask around the segmentation
        • Registration Option 1: Reusing previous warping fields
        • Registration Option 2: Direct registration to the template
        • Transforming the template using warping fields
      • Registering lumbar images to the PAM50 template
        • Before starting this tutorial
        • Using sct_deepseg to segment the lumber region of the spinal cord
        • Adding landmark labels for template matching
        • Applying the registration algorithm
    • Multimodal registration
      • Computing MTR using co-registration between MT0 and MT1 data
        • Before starting this tutorial
        • Spinal cord segmentation for MT1 data
        • Creating a mask around the segmentation
        • Coregistering MT0 with MT1
        • Computing MTR using coregistered MT data
      • Contrast-agnostic registration with deep learning
        • Before starting this tutorial
        • Preprocessing steps to highlight the spinal cord (T2w)
        • Preprocessing steps to highlight the spinal cord (T1w)
        • Coregistering T1w with T2w
    • Gray matter segmentation
      • Segmenting the gray and white matter for T2* data
        • Before starting this tutorial
        • Gray matter segmentation algorithm: sct_deepseg_gm
        • Applying the gray matter segmentation algorithm
        • Computing the white matter segmentation
      • Computing metrics using GM and WM segmentations
        • Before starting this tutorial
        • Using binary masks to compute CSA for gray and white matter
        • Using binary masks to extract intensity values for gray and white matter
      • Improving registration results using white and gray matter segmentations
        • Before starting this tutorial
        • GM-informed registration between the PAM50 template and T2* data
        • Reusing the GM-informed warping field to improve MTI registration
    • Atlas-based analysis
      • Before starting this tutorial
      • Atlas-based analysis
      • The partial volume effect
      • Overcoming the partial volume effect
      • Transforming the GM/WM atlas to the MT space using warping fields
      • Using the atlas to extract MTR in white matter
      • Using the atlas to extract MTR from specific white matter tracts
      • Modifying info_label.txt to add custom tracts to your analysis
    • Diffusion-weighted MRI
      • Before starting this tutorial
      • Preprocessing steps to highlight the spinal cord
      • Motion correction for dMRI images
      • Registering dMRI data to the PAM50 template
      • Computing DTI for motion corrected dMRI data
      • Extracting DTI from specific spinal cord regions
    • Functional MRI
      • Before starting this tutorial
      • Preprocessing steps to highlight the spinal cord
      • Motion correction for fMRI images
      • Registering fMRI data to the PAM50 template
      • Spinal labeling
      • Warping the spinal levels to the fMRI space
    • Other features
      • Spinal cord smoothing as a preprocessing operation
      • Visualizing misaligned cords with 2D sagittal flattening
    • Analysis pipelines with SCT
      • Before starting this tutorial
      • Introduction to building processing pipelines using scripts
      • Running the sample script (process_data.sh) using sct_run_batch
      • Inspecting the results of processing
      • What if things go wrong?
  • Command-Line Tools
    • Segmentation
      • sct_create_mask
      • sct_deepseg
        • seg_sc_contrast_agnostic
        • seg_ms_sc_mp2rage
        • seg_sc_epi
        • seg_mice_sc
        • seg_lumbar_sc_t2w
        • seg_exvivo_gm_wm_t2
        • seg_gm_sc_7t_t2star
        • seg_mice_gm
        • seg_mouse_gm_wm_t1w
        • seg_sc_lesion_t2w_sci
        • seg_sc_ms_lesion_stir_psir
        • seg_ms_lesion_mp2rage
        • seg_ms_lesion
        • seg_tumor_edema_cavity_t1_t2
        • seg_tumor_t2
        • seg_spinal_rootlets_t2w
        • canal_t2w
        • totalspineseg
      • sct_deepseg_gm
      • sct_deepseg_lesion
      • sct_deepseg_sc
      • sct_get_centerline
      • sct_propseg
    • Segmentation analysis
      • sct_analyze_lesion
      • sct_compute_hausdorff_distance
      • sct_compute_compression
      • sct_dice_coefficient
      • sct_process_segmentation
    • Labeling
      • sct_detect_pmj
      • sct_label_vertebrae
      • sct_label_utils
    • Registration
      • sct_apply_transfo
      • sct_get_centerline
      • sct_register_multimodal
      • sct_register_to_template
      • sct_straighten_spinalcord
      • sct_warp_template
    • Diffusion MRI
      • sct_dmri_compute_bvalue
      • sct_dmri_concat_bvals
      • sct_dmri_concat_bvecs
      • sct_dmri_compute_dti
      • sct_dmri_denoise_patch2self
      • sct_dmri_display_bvecs
      • sct_dmri_moco
      • sct_dmri_separate_b0_and_dwi
      • sct_dmri_transpose_bvecs
    • Magnetization transfer
      • sct_compute_mtr
      • sct_compute_mtsat
    • Functional MRI
      • sct_fmri_compute_tsnr
      • sct_fmri_moco
    • Metric processing
      • sct_analyze_texture
      • sct_extract_metric
    • Image manipulation
      • sct_convert
      • sct_crop_image
      • sct_denoising_onlm
      • sct_flatten_sagittal
      • sct_image
      • sct_maths
      • sct_merge_images
      • sct_resample
      • sct_smooth_spinalcord
    • Miscellaneous
      • sct_compute_ernst_angle
      • sct_compute_snr
      • sct_download_data
      • sct_qc
      • sct_run_batch
    • System
      • sct_check_dependencies
      • sct_version
    • Help text (-h) for all tools
  • Analysis pipelines
  • FSLeyes Integration
  • Help
  • Citing SCT

Developer section

  • Contributing to SCT
  • Changelog
  • License
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Computing MTR using co-registration between MT0 and MT1 dataΒΆ

This tutorial will demonstrate how to generate a magnetization transfer ratio (MTR) image for magnetization transfer data.

  • Before starting this tutorial
  • Spinal cord segmentation for MT1 data
  • Creating a mask around the segmentation
  • Coregistering MT0 with MT1
  • Computing MTR using coregistered MT data
Next
Before starting this tutorial
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Multimodal registration
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