开源工具库助力荧光成像系统标准化评估
Fluorescence Reference Target Quantitative Analysis Library
- 基于实体参照靶标实现荧光图像定量分析的模块化流程
- 支持线性响应、检测限等关键指标计算，符合监管指南
- 适合医疗影像研发与医疗器械评估人员使用
荧光引导手术中荧光成像系统的标准化性能评估仍是一个亟待解决的问题。尽管美国医学物理学家协会（AAPM）TG311报告和近期FDA草案指南提出了系统表征的推荐指标，但实际获取这些指标的工具仍有限、不一致且难以获取。我们提出QUEL-QAL，一个开源的Python库，旨在通过固体参考靶标简化并标准化荧光图像的定量分析。该库提供模块化、可复现的工作流，包含感兴趣区域（ROI）检测、统计分析与可视化功能。其支持响应线性、检测限、深度敏感性和空间分辨率等关键指标，符合监管与学术指导要求。基于广泛使用的Python工具包构建，具有可扩展性，便于用户适配新型靶标设计与分析协议。通过提升透明度、可复现性与监管一致性，QUEL-QAL为荧光成像系统的基准测试提供了基础工具，推动其开发与评估进程。
原文摘要 · Abstract (English)
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 While the American Association of Physicists in Medicine (AAPM) TG311 report and recent FDA draft guidance provide recommended metrics for system characterization, practical tools for extracting these metrics remain limited, inconsistent, and often inaccessible.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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。