Analysis pipeline for: Deep Learning Super-Resolution for MRI: Technical Advances and Translational Potential for Low-Resource Settings
Generates all figures (5 main + supplementary) and tables (6 main) for the MRI super-resolution narrative review targeting Nature Machine Intelligence.
# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install dependencies and sync virtual environment
uv sync
# Generate all figures and tables (Unified Pipeline)
python scripts/figures/generate_all_figures.py
# Verified statistics can also be printed via:
python scripts/tables/abstract_numbers.py- Python 3.11 or higher
- uv package manager
The pipeline includes advanced analytics for manuscript revision:
- Random Forest Feature Importance: Predicts LMIC relevance.
- Mann-Whitney U Tests: Pairwise comparison of study characteristics.
- Fleiss' Kappa: Inter-rater reliability (2 raters, N=10 subset).
- Geographic Equity: World Bank income classification mapping.
# Main figures
python scripts/figures/fig1_year_distribution.py # Figure 1: Publication Trends
python scripts/figures/fig2_architecture_distribution.py # Figure 2: AI Architecture Landscape
python scripts/figures/fig3_lmic_relevance.py # Figure 3: LMIC Relevance Analysis
python scripts/figures/fig4_performance_comparison.py # Figure 4: Performance Metrics
python scripts/figures/fig5_field_strength_application.py # Figure 5: Field Strength & Application
# Figure 6: Translational Roadmap (Manual PNG, converted to PDF by master script)
# Main tables
python scripts/tables/table1_study_characteristics.py # Table 1: Study Characteristics
python scripts/tables/table2_ai_architectures.py # Table 2: AI Architectures
python scripts/tables/table3_performance_metrics.py # Table 3: Performance Metrics
python scripts/tables/table4_lmic_applicability.py # Table 4: LMIC Applicability
python scripts/tables/table5_statistical_insights.py # Table 5: Statistical Insights
python scripts/tables/table6_geographic_equity.py # Table 6: Geographic Equityuv run pytest tests/ -vAll tests (consistency, validation, generation) should pass. Verified for N=48 and Kappa=0.728.
Source data: data/data-clean.csv (48 primary studies, 11 reviewers).
Corrected dataset refined from an initial pool of 183 papers (2020-2025).
| Metric | Value |
|---|---|
| Papers included (Primary Studies) | 48 |
| Brain MRI (dominant area) | 24 (50.0%) |
| CNN (most common architecture) | 23 (47.9%) |
| Low-field MRI mentioned | 14 (29.2%) |
| High LMIC relevance (Score 4-5) | 19 (39.6%) |
| Clinical validation reported | 19 (39.6%) |
| Code publicly available | 6 (12.5%) |
| Median PSNR | 32.6 dB |
| Median SSIM | 0.917 |
| Inter-rater Agreement (Fleiss' Kappa) | 0.728 (Substantial) |
- Development & testing: See CONTRIBUTING.md
- Dependencies: See pyproject.toml
- Statistical methods: See docs/STATISTICAL_METHODS.md