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Related Experiment Video

Updated: Jun 26, 2026

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

Nicolò Lauciello1,2,3, Giorgio Russo2,3, Alessandro Stefano2,3

  • 1Department of Earth and Marine Sciences, University of Palermo, Via Archirafi 22, 90123 Palermo, Italy.

Journal of Imaging
|June 25, 2026
PubMed
Summary

Quantitative preclinical imaging offers vital biomarkers for research. This review highlights technical and biological factors affecting measurement accuracy, emphasizing harmonization for reliable results.

Keywords:
computed tomographymagnetic resonance imagingnuclear imagingpreclinical imagingquantitative imaging biomarkersstandardization and harmonization

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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.