Getting Started with scimagR • scimagR

Overview

scimagR provides an end-to-end pipeline for longitudinal MRI and CT analysis in spinal cord injury (SCI) research. This vignette walks you through:

Installing prerequisites
Creating a new project
Organizing your data
Running the pipeline
Analyzing results

Prerequisites

scimagR requires the following external tools:

Spinal Cord Toolbox (SCT) >= 6.4: https://spinalcordtoolbox.com
dcm2niix: https://github.com/rordenlab/dcm2niix
Python 3 with pydicom: pip install pydicom

Check that all tools are available:

library(scimagR)

check_sct()
check_dcm2niix()
check_pydicom()

Creating a Project

Use sci_create_project() to scaffold a new workflowr-based analysis project:

sci_create_project(
  path = "~/projects/my-sci-study",
  title = "Cervical SCI Longitudinal MRI Analysis",
  author = "Your Name"
)

This creates a complete directory structure with template files, configuration, and analysis Rmd notebooks.

Data Organization

Place your DICOM files in data/raw/dicom/ following this structure:

data/raw/dicom/
├── SCI001/
│   ├── ses-01/
│   │   └── *.dcm
│   └── ses-02/
│       └── *.dcm
└── SCI002/
    └── ses-01/
        └── *.dcm

Fill in the CSV templates in data/metadata/:

imaging_registry.csv: One row per imaging session
clinical_data.csv: One row per patient

Running the Pipeline

The pipeline orchestrates all processing steps:

sci_run_pipeline("~/projects/my-sci-study", steps = 1:8)

Check pipeline status at any time:

sci_pipeline_status("~/projects/my-sci-study")

Computing SCI Metrics

scimagR includes validated implementations of common SCI metrics:

# Maximum Spinal Cord Compression
compute_mscc(di = 5.2, da = 8.1, db = 8.5)

# Compression ratio
compute_compression_ratio(ap = 6.5, transverse = 12.0)

# Phase classification
classify_phase(c(0, 5, 30, 200, 400))

Visualization

Use the built-in theme and palettes for publication-ready figures:

library(ggplot2)

ggplot(data, aes(x = phase, y = mscc, fill = phase)) +
  geom_violin() +
  scale_fill_phase() +
  theme_sci()

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.