Quantitative Preclinical Imaging as a Metrological Framework: Reproducibility, Validation, and Translational Maturity

Area of Science:

• Preclinical Imaging
• Biomarker Discovery
• Metrology

Background:

• Quantitative preclinical imaging provides non-invasive physiological and molecular insights.
• It generates measurable biomarkers crucial for longitudinal and translational research.
• Major modalities include PET, SPECT, MRI, CT, optical, and hybrid systems in murine and zebrafish models.

Purpose of the Study:

• To systematically analyze quantitative preclinical imaging studies (2015-2025).
• To evaluate methodological frameworks for parameter extraction, reproducibility, and validation.
• To assess the metrological maturity of harmonization infrastructure across platforms.

Main Methods:

• Systematic review of 60 studies.
• Analysis of major imaging modalities (PET, SPECT, MRI, CT, optical, hybrid).
• Evaluation using a framework distinguishing technical, biological, and computational variance sources.

Main Results:

• Variability sources are technical (instrumentation, reconstruction, calibration) and biological (heterogeneity, model factors).
• The interaction of these factors governs measurement uncertainty.
• Emerging computational approaches (AI, parametric modeling) show potential but require validation.

Conclusions:

• Quantitative preclinical imaging is framed as a metrological discipline.
• Reproducibility, bias control, and cross-modality harmonization are critical.
• Robust and translationally relevant imaging biomarkers depend on these factors.