Spinal Cord Injury Imaging Pipeline for MRI and CT Analysis in R • scimagR

An end-to-end R pipeline for longitudinal MRI and CT analysis in spinal cord injury (SCI) research. Provides R wrappers for the Spinal Cord Toolbox (SCT), DICOM tools, SCI-specific clinical metrics, and pipeline orchestration with built-in quality control.

Installation

Install from GitHub:

# install.packages("pak")
pak::pak("cttir/scimagR")

Prerequisites

scimagR requires the following external tools:

Tool	Version	Purpose
Spinal Cord Toolbox	>= 6.4	Segmentation, labeling, parameter extraction
dcm2niix	any	DICOM to NIfTI conversion
Python 3 + pydicom	any	DICOM metadata extraction & anonymization

Verify installation:

library(scimagR)
check_sct()
check_dcm2niix()
check_pydicom()

Quick Start

library(scimagR)

# 1. Create a new project
sci_create_project(
  "~/projects/my-sci-study",
  title = "Cervical SCI Longitudinal MRI",
  author = "Your Name"
)

# 2. Place DICOMs in data/raw/dicom/, fill registry CSVs

# 3. Run the pipeline
sci_run_pipeline("~/projects/my-sci-study")

Modules

Module	Key Functions	Description
SCT Interface	`sct_segment_sc()`, `sct_segment_lesion()`, `sct_label_vertebrae()`	R wrappers for all SCT CLI commands
DICOM Tools	`extract_dicom_metadata()`, `anonymize_dicom()`, `convert_dcm2niix()`	Metadata extraction, anonymization, conversion
SCI Metrics	`compute_mscc()`, `compute_compression_ratio()`, `classify_phase()`	Domain-specific clinical measures
Registry	`create_registry()`, `validate_registry()`, `coverage_matrix()`	Structured time-point tracking
QC System	`filter_evaluable()`, `log_exclusion()`, `integrity_summary()`	Artifact grading and exclusion tracking
Visualization	`theme_sci()`, `scale_colour_phase()`, `plot_violin_box()`	Publication-ready figures
Pipeline	`sci_run_pipeline()`, `sci_pipeline_status()`	Orchestration with resume support
Scaffold	`sci_create_project()`	Workflowr project setup

Citation

If you use scimagR in your research, please cite:

Heller R (2026). scimagR: Spinal Cord Injury Imaging Pipeline for MRI and
CT Analysis in R. R package version 0.1.0.
https://github.com/cttir/scimagR

Use of LLM tools

Portions of this package were prepared with assistance from large language model tooling for narrowly defined, non-authorial tasks: copyediting, prose smoothing, Markdown/LaTeX formatting, scaffolding of boilerplate files (CI configs, build scripts), code refactoring. The tools used were Chat AI, the LLM service of KISSKI (GWDG), and a self-hosted Mistral Small (24B, Apache-2.0) run locally via Ollama and the ollamar R package — local inference only, with no data sent to third parties for the self-hosted model.

All scientific claims, methodological choices, analyses, interpretations, and conclusions are the author’s own. No LLM-generated text was incorporated without review and revision, and every reference was verified against its DOI, arXiv ID, or ISBN.

License

MIT