Fluorescence Reference Target Quantitative Analysis Library.

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Optical Imaging

Background:

• Standardized performance evaluation of fluorescence imaging systems is crucial for fluorescence-guided surgery (FGS).
• Current practical tools for extracting key performance metrics are limited, inconsistent, and inaccessible.
• Existing guidance from AAPM TG311 and FDA draft recommendations highlight the need for robust characterization methods.

Purpose of the Study:

• To introduce QUEL-QAL, an open-source Python library for quantitative analysis of fluorescence images.
• To streamline and standardize the performance evaluation of fluorescence imaging systems.
• To provide a reproducible workflow aligned with regulatory and academic standards.

Main Methods:

• Development of an open-source Python library, QUEL-QAL.
• Implementation of a modular workflow including ROI detection, statistical analysis, and visualization.
• Utilization of solid reference targets for quantitative analysis.
• Support for key metrics: response linearity, limit of detection, depth sensitivity, and spatial resolution.

Main Results:

• QUEL-QAL enables streamlined and standardized quantitative analysis of fluorescence images.
• The library facilitates reproducible performance evaluation aligned with AAPM and FDA guidance.
• QUEL-QAL supports essential metrics for system characterization.
• The open-source nature allows for extensibility and adaptation to new targets and protocols.

Conclusions:

• QUEL-QAL provides a foundational tool for standardized benchmarking of fluorescence imaging systems.
• The library promotes transparency and reproducibility in system evaluation.
• QUEL-QAL accelerates the development and clinical adoption of advanced fluorescence imaging technologies for surgery.