Gaussian Process Models of Dynamic PET for Functional Volume Definition in Radiation Oncology
Gaussian Process Models of Dynamic PET for Functional Volume Definition in Radiation Oncology
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DOI：
10.1109/tmi.2012.2193896
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发表时间：
2012-08-01
影响因子：
10.6
通讯作者：
Owenius, Rikard
中科院分区：
文献类型：
--
作者：
Shepherd, Tony;Owenius, Rikard
在常规肿瘤正电子发射断层扫描（PET）中，通过对信号进行时间平均来丢弃动态信息，以产生“标准化摄取值”（SUV）的静态图像。根据SUV定义功能感兴趣体积（VOI）是有缺陷的，因为值受混杂因素和所选时间窗的影响，并且SUV图像对病理组织的功能异质性不敏感。此外，SUV等值线高度受阈值选择的影响，并且对于给定的VOI类型，普遍接受无阈值或其他基于SUV的分割方法。高斯过程（GP）时间序列模型描述了由无数相互作用的微观过程产生的宏观尺度动态行为，如来自异质组织的PET信号的情况。我们使用GPS从动态PET中建模时间-活性曲线（TAC），并定义PET肿瘤学的功能体积。沿着提出了组织鉴别的概率方法，以及用于功能VOI分割的新轮廓方法。我们证明了GP模型在前列腺PET中具有功能异质性的患病和转移组织的体素分类和VOI轮廓绘制中的价值。分类实验表明，上级的灵敏度和特异性SUV计算和TAC为基础的方法在最近的文献中提出的。轮廓实验揭示了金标准和GP VOI之间的形状差异，与动力学模型的相关性表明，与单独的SUV相比，新型VOI包含额外的临床相关信息。我们的结论是，所提出的模型提供了一个原则性的数据分析技术，提高了SUV的肿瘤VOI定义。持续的研究将推广GP模型用于不同的肿瘤示踪剂和成像协议，最终目标是临床使用，包括治疗计划。
In routine oncologic positron emission tomography (PET), dynamic information is discarded by time-averaging the signal to produce static images of the "standardised uptake value" (SUV). Defining functional volumes of interest (VOIs) in terms of SUV is flawed, as values are affected by confounding factors and the chosen time window, and SUV images are not sensitive to functional heterogeneity of pathological tissues. Also, SUV iso-contours are highly affected by the choice of threshold and no threshold, or other SUV-based segmentation method, is universally accepted for a given VOI type. Gaussian Process (GP) time series models describe macro-scale dynamic behavior arising from countless interacting micro-scale processes, as is the case for PET signals from heterogeneous tissue. We use GPs to model time-activity curves (TACs) from dynamic PET and to define functional volumes for PET oncology. Probabilistic methods of tissue discrimination are presented along with novel contouring methods for functional VOI segmentation. We demonstrate the value of GP models for voxel classification and VOI contouring of diseased and metastatic tissues with functional heterogeneity in prostate PET. Classification experiments reveal superior sensitivity and specificity over SUV calculation and a TAC-based method proposed in recent literature. Contouring experiments reveal differences in shape between gold-standard and GP VOIs and correlation with kinetic models shows that the novel VOIs contain extra clinically relevant information compared to SUVs alone. We conclude that the proposed models offer a principled data analysis technique that improves on SUVs for oncologic VOI definition. Continuing research will generalize GP models for different oncology tracers and imaging protocols with the ultimate goal of clinical use including treatment planning.