临床前正电子发射断层扫描与小鼠体模匹配,用于基于云的自动图像分析。

IF 1.2 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES
Edward Cabral, Mary Katherine Montgomery, Meaghan Berg, Lisa Kathryn Manzuk, Anand Giddabasappa, Ziyue Karen Jiang
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引用次数: 0

摘要

正电子发射断层扫描(PET)是一种分子成像模式,可用于研究多种药理学问题,如生物标志物调节、受体占用和相关化合物的生物分布。在生物分布研究中,实验对象在接受试验物品后通常要进行纵向成像。然后对图像进行分析,得出化合物在不同时间点在不同器官中的分布情况。这是药物开发中了解研究化合物分布和潜在结合情况的关键步骤。然而,基于 PET 成像的生物分布分析的标准/手动方法耗费大量人力和时间,而且往往与操作员之间的高变异性有关。此外,在不同的时间点保持动物位置一致也是一项挑战。为了解决这些问题,我们使用了一系列小鼠体成型动物模型(BCAM),以便在 PET/CT 成像采集过程中对动物进行严格而一致的定位。此外,还采用了一个软件即服务(SaaS)平台,该平台由基于云的器官概率图(OPM)和人工智能驱动的分割工具组成,可对体内 PET 成像数据进行可靠的自动量化。本文介绍的工作流程包括:(1)为使用BCAMs成像准备小鼠,包括适当植入皮下肿瘤以与模具兼容;(2)使用G8扫描仪获取BCAMs的PET/CT图像;(3)使用基于云的SaaS进行自动器官分割和生物分布分析。这里使用的是[18F]FDG示踪剂,但其他生物标记物和/或放射性标记化合物也可以很容易地应用到工作流程中。该程序只需少量培训即可准确有效地执行,自动 PET 数据分析得出的结果令人满意,与手动方法一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preclinical Positron Emission Tomography with Body Conforming Animal Molds for Cloud-Based Automated Image Analysis in Mice.

Positron Emission Tomography (PET) is a molecular imaging modality that can be used to investigate a multitude of pharmacological questions, such as biomarker modulation, receptor occupancy, and biodistribution of compounds of interest. In biodistribution studies, experimental subjects are often longitudinally imaged after receiving the test article. The images are then analyzed to derive the compound's distribution profile in various organs at different timepoints. This constitutes a crucial step in drug development to understand the distribution and potentially binding profile of an investigative compound. Standard/manual methods of PET imaging-based biodistribution analyses, however, are labor-intensive and time-consuming and are often associated with high inter-operator variability. Further, it is challenging to keep the animals' positions consistent across different timepoints. To address these shortcomings, a series of mouse Body Conforming Animal Molds (BCAMs) were used to enable rigid and consistent positioning of animals during PET/CT imaging acquisition. Further, a Software-as-a-Service (SaaS) platform consisting of a cloud-based Organ Probability Map (OPM) and an artificial intelligence-powered segmentation tool were employed to enable reliable and automated quantitation of in vivo PET imaging data. The workflow presented here includes (1) prepping mice for imaging with the BCAMs, including the proper implantation of subcutaneous tumors to be compatible with the molds, (2) acquiring PET/CT images with BCAMs using the G8 scanner, and 3) performing automated organ segmentation and biodistribution analysis using the cloud-based SaaS. [18F]FDG was used as an exemplar tracer here, but other biomarkers and/or radio-labeled compounds can be readily adapted into the workflow. This procedure can be executed accurately and effectively with minimal training, and the automated PET data analysis yielded satisfactory results consistent with the manual method.

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来源期刊
Jove-Journal of Visualized Experiments
Jove-Journal of Visualized Experiments MULTIDISCIPLINARY SCIENCES-
CiteScore
2.10
自引率
0.00%
发文量
992
期刊介绍: JoVE, the Journal of Visualized Experiments, is the world''s first peer reviewed scientific video journal. Established in 2006, JoVE is devoted to publishing scientific research in a visual format to help researchers overcome two of the biggest challenges facing the scientific research community today; poor reproducibility and the time and labor intensive nature of learning new experimental techniques.
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