计算机断层摄影术
协议(科学)
肺癌
计算机科学
医学影像学
医学物理学
图像处理
软件
断层摄影术
图像配准
放射科
医学
计算机视觉
人工智能
病理
图像(数学)
程序设计语言
替代医学
作者
May Zaw Thin,Christopher Moore,Thomas J. Snoeks,Tammy L. Kalber,Julian Downward,Axel Behrens
出处
期刊:Nature Protocols
[Nature Portfolio]
日期:2022-12-09
卷期号:18 (3): 990-1015
被引量:14
标识
DOI:10.1038/s41596-022-00769-5
摘要
X-ray computed tomography is a reliable technique for the detection and longitudinal monitoring of pulmonary nodules. In preclinical stages of diagnostic or therapeutic development, the miniaturized versions of the clinical computed tomography scanners are ideally suited for carrying out translationally-relevant research in conditions that closely mimic those found in the clinic. In this Protocol, we provide image acquisition parameters optimized for low radiation dose, high-resolution and high-throughput computed tomography imaging using three commercially available micro-computed tomography scanners, together with a detailed description of the image analysis tools required to identify a variety of lung tumor types, characterized by specific radiological features. For each animal, image acquisition takes 4–8 min, and data analysis typically requires 10–30 min. Researchers with basic training in animal handling, medical imaging and software analysis should be able to implement this protocol across a wide range of lung cancer models in mice for investigating the molecular mechanisms driving lung cancer development and the assessment of diagnostic and therapeutic agents. A micro-computed X-ray tomography-based approach for quantifying the number and volume of lung cancer nodules over time, enabling the tracking of individual nodule formation, tumor growth and response to therapy.
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