arXiv 2504.171142025-04-23eess.IVcs.AIcs.CVphysics.med-ph

动态PET中基于多器官分割的图像导出输入函数的解剖约束建模

Anatomy-constrained modelling of image-derived input functions in dynamic PET using multi-organ segmentation

Department of diagnostic and interventional Radiology and Neuroradiology, University Hospital Augsburg（诊断与介入放射学及神经放射学系，奥格斯堡大学医院）；Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich（计算机辅助医疗程序与增强现实，慕尼黑技术大学）；Digital Medicine, University Hospital Augsburg（数字医学，奥格斯堡大学医院）；Center of Advanced Analytics and Predictive Sciences, University of Augsburg（高级分析与预测科学中心，奥格斯堡大学）

Valentin Langer, Kartikay Tehlan, Thomas Wendler

备注：: The code is available under https://github.com/tinolan/curve_fit_multi_idif

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AI总结：

提出基于多器官分割的IDIFs整合方法，利用CT分割纳入器官特异性血供，在九例动态PET数据上使肝脏和肺部MSE分别降低13.39%和10.42%，有望推动动力学建模临床常规应用。

摘要：

动态正电子发射断层扫描（PET）中[$^{18}$F]FDG分布的准确动力学分析需要对图像导出输入函数（IDIFs）进行解剖约束建模。传统上，IDIFs从主动脉获取，忽略了解剖变异和复杂的血管贡献。本研究提出了一种基于多器官分割的方法，整合了来自主动脉、门静脉、肺动脉和输尿管的IDIFs。利用肝脏、肺、肾脏和膀胱的高分辨率CT分割，我们纳入器官特异性的血液供应源以改进动力学建模。该方法在九名患者的动态[$^{18}$F]FDG PET数据上进行了评估，肝脏的平均平方误差（MSE）降低了$13.39\%$，肺部的降低了$10.42\%$。这些初步结果凸显了多个IDIFs在改进解剖建模和充分利用动态PET成像方面的潜力。该方法可促进示踪剂动力学建模融入临床常规。

英文摘要：

Accurate kinetic analysis of [$^{18}$F]FDG distribution in dynamic positron emission tomography (PET) requires anatomically constrained modelling of image-derived input functions (IDIFs). Traditionally, IDIFs are obtained from the aorta, neglecting anatomical variations and complex vascular contributions. This study proposes a multi-organ segmentation-based approach that integrates IDIFs from the aorta, portal vein, pulmonary artery, and ureters. Using high-resolution CT segmentations of the liver, lungs, kidneys, and bladder, we incorporate organ-specific blood supply sources to improve kinetic modelling. Our method was evaluated on dynamic [$^{18}$F]FDG PET data from nine patients, resulting in a mean squared error (MSE) reduction of $13.39\%$ for the liver and $10.42\%$ for the lungs. These initial results highlight the potential of multiple IDIFs in improving anatomical modelling and fully leveraging dynamic PET imaging. This approach could facilitate the integration of tracer kinetic modelling into clinical routine.

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