AnatomIQ：用于医学影像中自动背景检测的开源工具包
AnatomIQ: An Open-Source Toolkit for Automated Background Detection in Medical Imaging
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中文总结 AI 辅助
AnatomIQ是一个开源Python工具包，自动检测医学影像背景，实现无体模的NPS和TTF分析，消除操作者变异性，支持持续质量监测和重建优化。
中文摘要 AI 辅助
在CT图像质量评估中，手动选择背景以计算对比度噪声比（CNR）既耗时又依赖操作者，且损害可重复性。诸如噪声功率谱（NPS）和任务传递函数（TTF）等高级指标传统上需要专用的体模采集，增加了成本、辐射暴露和工作流程中断。我们开发了AnatomIQ，一个开源的Python工具包，可自动进行背景检测，并直接在患者解剖结构上进行无体模的NPS和TTF分析。自动背景检测使用组织特定的亨氏单位阈值化和形态学滤波，并配有一个网页界面，可从PACS截图点击选择病变。报告的指标包括CNR、信噪比、可检测性指数和剂量归一化的品质因数，并带有自动优化建议。NPS从自动检测的均匀组织区域量化噪声纹理，而TTF利用自然的圆形解剖结构（如气管、血管）作为边缘体模，共同表征分辨率-噪声权衡，无需专用体模扫描。每例分析耗时2-3分钟，CNR在10秒内计算完成，NPS/TTF可按需获取。自动检测可靠地识别了跨解剖部位的有效区域，包括头颈部扫描，这是均匀区域可用性的近乎最坏情况测试。NPS和TTF流程均与体模衍生参考值进行了验证，确认体内测量反映真实的解剖纹理和分辨率，而非流程伪影。通过消除操作者变异性和体模需求，AnatomIQ实现了实用的持续质量监测和基于证据的重建优化。该工具包开源，并提供可选的商业许可用于学术和临床使用。
英文摘要
Manual background selection for contrast-to-noise ratio (CNR) calculations in CT image quality assessment is time-consuming, operator-dependent, and compromises reproducibility. Advanced metrics such as Noise Power Spectrum (NPS) and Task Transfer Function (TTF) traditionally require dedicated phantom acquisitions, adding cost, radiation exposure, and workflow disruption. We developed AnatomIQ, an open-source Python toolkit that automates background detection and performs phantom-free NPS and TTF analysis directly on patient anatomy. Automated background detection uses tissue-specific Hounsfield unit thresholding and morphological filtering, paired with a web interface for click-to-select lesion identification from PACS screenshots. Reported metrics include CNR, signal-to-noise ratio, detectability indices, and dose-normalized figures of merit with automated optimization recommendations. NPS quantifies noise texture from automatically detected uniform tissue regions, while TTF leverages natural circular anatomical structures (e.g., trachea, vessels) as edge phantoms, together characterizing the resolution-noise trade-off without dedicated phantom scans. Analysis takes 2-3 minutes per case, with CNR computed in under 10 seconds and NPS/TTF available on demand. Automated detection reliably identified valid regions across anatomical sites, including head-and-neck scans, a near-worst-case test of uniform-region availability. Both NPS and TTF pipelines were validated against phantom-derived references, confirming that in vivo measurements reflect genuine anatomical texture and resolution rather than pipeline artifacts. By eliminating operator variability and phantom requirements, AnatomIQ enables practical continuous quality monitoring and evidence-based reconstruction optimization. The toolkit is open-source, with optional commercial licensing for academic and clinical use.
发表机构
- Thayer School of Engineering, Dartmouth College（达特茅斯学院泰勒工程学院）
- Dartmouth Hitchcock Medical Center（达特茅斯-希契科克医疗中心）
- Medical University of South Carolina（南卡罗来纳医科大学）
机构由 AI 辅助整理，请以论文原文为准。
