通过多器官分割提升动态PET图像输入函数建模精度
Anatomy-constrained modelling of image-derived input functions in dynamic PET using multi-organ segmentation
- 基于肝脏、肺等器官的高分辨率CT分割，融合多血管来源的输入函数
- 在9名患者数据上，肝与肺的均方误差分别降低13.39%和10.42%
- 适用于需精准代谢分析的临床研究，如肿瘤代谢评估
动态正电子发射断层扫描（PET）中[$^{18}$F]FDG分布的动力学分析需要解剖约束的图像衍生输入函数（IDIF）。传统方法仅使用主动脉获取IDIF，忽略解剖变异与复杂血管贡献。本研究提出一种基于多器官分割的方法，整合主动脉、门静脉、肺动脉及输尿管的IDIF。利用肝脏、肺、肾、膀胱的高分辨率CT分割，引入器官特异性血供源以优化动力学建模。在9例[$^{18}$F]FDG动态PET数据上验证，肝脏和肺部的均方误差（MSE）分别降低13.39%和10.42%。初步结果表明，多源IDIF可显著提升解剖建模精度，充分释放动态PET的临床潜力，有望推动示踪剂动力学建模进入常规诊疗流程。
原文摘要 · Abstract (English)
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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