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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2504.15496 |
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| _version_ | 1866917994450386944 |
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| author | Littler, Eammon A. Mannoh, Emmanuel A. LaRochelle, Ethan P. M. |
| author_facet | Littler, Eammon A. Mannoh, Emmanuel A. LaRochelle, Ethan P. M. |
| contents | Standardized performance evaluation of fluorescence imaging systems remains a critical unmet need in the field of fluorescence-guided surgery (FGS). While the American Association of Physicists in Medicine (AAPM) TG311 report and recent FDA draft guidance provide recommended metrics for system characterization, practical tools for extracting these metrics remain limited, inconsistent, and often inaccessible. We present QUEL-QAL, an open-source Python library designed to streamline and standardize the quantitative analysis of fluorescence images using solid reference targets. The library provides a modular, reproducible workflow that includes region of interest (ROI) detection, statistical analysis, and visualization capabilities. QUEL-QAL supports key metrics such as response linearity, limit of detection, depth sensitivity, and spatial resolution, in alignment with regulatory and academic guidance. Built on widely adopted Python packages, the library is designed to be extensible, enabling users to adapt it to novel target designs and analysis protocols. By promoting transparency, reproducibility, and regulatory alignment, QUEL-QAL offers a foundational tool to support standardized benchmarking and accelerate the development and evaluation of fluorescence imaging systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fluorescence Reference Target Quantitative Analysis Library Littler, Eammon A. Mannoh, Emmanuel A. LaRochelle, Ethan P. M. Medical Physics Computer Vision and Pattern Recognition Image and Video Processing Quantitative Methods Standardized performance evaluation of fluorescence imaging systems remains a critical unmet need in the field of fluorescence-guided surgery (FGS). While the American Association of Physicists in Medicine (AAPM) TG311 report and recent FDA draft guidance provide recommended metrics for system characterization, practical tools for extracting these metrics remain limited, inconsistent, and often inaccessible. We present QUEL-QAL, an open-source Python library designed to streamline and standardize the quantitative analysis of fluorescence images using solid reference targets. The library provides a modular, reproducible workflow that includes region of interest (ROI) detection, statistical analysis, and visualization capabilities. QUEL-QAL supports key metrics such as response linearity, limit of detection, depth sensitivity, and spatial resolution, in alignment with regulatory and academic guidance. Built on widely adopted Python packages, the library is designed to be extensible, enabling users to adapt it to novel target designs and analysis protocols. By promoting transparency, reproducibility, and regulatory alignment, QUEL-QAL offers a foundational tool to support standardized benchmarking and accelerate the development and evaluation of fluorescence imaging systems. |
| title | Fluorescence Reference Target Quantitative Analysis Library |
| topic | Medical Physics Computer Vision and Pattern Recognition Image and Video Processing Quantitative Methods |
| url | https://arxiv.org/abs/2504.15496 |