一种使用基于蒙特卡罗的数学模型对 PET 病变进行分割的迭代技术。

An iterative technique to segment PET lesions using a Monte Carlo based mathematical model.

机构信息

Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA.

出版信息

Med Phys. 2009 Oct;36(10):4803-9. doi: 10.1118/1.3222732.

DOI:10.1118/1.3222732

PMID:19928110

Abstract

PURPOSE

The need for an accurate lesion segmentation tool in 18FDG PET is a prerequisite for the estimation of lesion response to therapy, for radionuclide dosimetry, and for the application of 18FDG PET to radiotherapy planning. In this work, the authors have developed an iterative method based on a mathematical fit deduced from Monte Carlo simulations to estimate tumor segmentation thresholds.

METHODS

The GATE software, a GEANT4 based Monte Carlo tool, was used to model the GE Advance PET scanner geometry. Spheres ranging between 1 and 6 cm in diameters were simulated in a 10 cm high and 11 cm in diameter cylinder. The spheres were filled with water-equivalent density and simulated in both water and lung equivalent background. The simulations were performed with an infinite, 8/1, and 4/1 target-to-background ratio (T/B). A mathematical fit describing the correlation between the lesion volume and the corresponding optimum threshold value was then deduced through analysis of the reconstructed images. An iterative method, based on this mathematical fit, was developed to determine the optimum threshold value. The effects of the lesion volume and T/B on the threshold value were investigated. This method was evaluated experimentally using the NEMA NU2-2001 IEC phantom, the ACNP cardiac phantom, a randomly deformed aluminum can, and a spheroidal shape phantom implemented artificially in the lung, liver, and brain of patient PET images. Clinically, the algorithm was evaluated in six lesions from five patients. Clinical results were compared to CT volumes.

RESULTS

This mathematical fit predicts an existing relationship between the PET lesion size and the percent of maximum activity concentration within the target volume (or threshold). It also showed a dependence of the threshold value on the T/B, which could be eliminated by background subtraction. In the phantom studies, the volumes of the segmented PET targets in the PET images were within 10% of the nominal ones. Clinically, the PET target volumes were also within 10% of those measured from CT images.

CONCLUSIONS

This iterative algorithm enabled accurately segment PET lesions, independently of their contrast value.

摘要

目的

在 18FDG PET 中需要准确的病灶分割工具，这是估计病灶对治疗的反应、放射性核素剂量学以及将 18FDG PET 应用于放射治疗计划的前提。在这项工作中，作者开发了一种基于蒙特卡罗模拟推导的数学拟合的迭代方法来估计肿瘤分割阈值。

方法

使用 GATE 软件，一种基于 GEANT4 的蒙特卡罗工具，对 GE Advance PET 扫描仪的几何形状进行建模。在一个 10cm 高、11cm 直径的圆柱体中模拟了直径在 1 到 6cm 之间的球体。球体用与水等效的密度填充，并在水和肺等效背景中进行模拟。在无限、8/1 和 4/1 的目标与背景比（T/B）下进行模拟。然后通过分析重建图像，推导出描述病灶体积与相应最佳阈值值之间相关性的数学拟合。基于该数学拟合，开发了一种迭代方法来确定最佳阈值值。研究了病灶体积和 T/B 对阈值值的影响。该方法使用 NEMA NU2-2001 IEC 体模、ACNP 心脏体模、随机变形的铝罐以及在患者 PET 图像的肺、肝和脑内人工实现的球状形状体模进行了实验评估。在临床上，该算法在来自五名患者的六个病灶中进行了评估。临床结果与 CT 体积进行了比较。

结果

该数学拟合预测了 PET 病灶大小与靶体积（或阈值）内最大活性浓度百分比之间的现有关系。它还显示了阈值值对 T/B 的依赖性，通过背景减除可以消除这种依赖性。在体模研究中，PET 图像中分割的 PET 目标的体积在标称值的 10%以内。临床上，PET 目标的体积也在 CT 图像测量值的 10%以内。