一种用于 PET 肿瘤体积和活性定量的算法：无需指定相机的点扩散函数 (PSF)。

An algorithm for PET tumor volume and activity quantification: without specifying camera's point spread function (PSF).

机构信息

Department of Biomedical Engineering, Florida International University, Miami, FL, USA.

出版信息

Med Phys. 2012 Jul;39(7):4187-202. doi: 10.1118/1.4728219.

DOI:10.1118/1.4728219

PMID:22830752

Abstract

PURPOSE

The authors have developed an algorithm for segmentation and removal of the partial volume effect (PVE) of tumors in positron emission tomography (PET) images. The algorithm accurately measures functional volume (FV) and activity concentration (AC) of tumors independent of the camera's full width half maximum (FWHM).

METHODS

A novel iterative histogram thresholding (HT) algorithm is developed to segment the tumors in PET images, which have low resolution and suffer from inherent noise in the image. The algorithm is initiated by manually drawing a region of interest (ROI). The segmented tumors are subjected to the iterative deconvolution thresholding segmentation (IDTS) algorithm, where the Van-Cittert's method of deconvolution is used for correcting PVE. The IDTS algorithm is fully automated and accurately measures the FV and AC, and stops once it reaches convergence. The convergence criteria or stopping conditions are developed in such a way that the algorithm does not rely on estimating the FWHM of the point spread function (PSF) to perform the deconvolution process. The algorithm described here was tested in phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution, and an irregular shaped volume was used to represent a tumor with a heterogeneous activity distribution. The phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1 min, 3 min, and 5 min). The parameters in the algorithm were also changed (FWHM and matrix size of the Gaussian function) to check the accuracy of the algorithm. Simulated data were also used to test the algorithm with tumors having heterogeneous activity distribution.

RESULTS

The results show that changing the size and shape of the ROI during initiation of the algorithm had no significant impact on the FV. An average FV overestimation of 30% and an average AC underestimation of 35% were observed for the smallest tumor (0.5 ml) over the entire range of noise and SBR level. The difference in average FV and AC estimations from the actual volumes were less than 5% as the tumor size increased to 16 ml. For tumors with heterogeneous activity profile, the overall volume error was less than 10%. The average overestimation of FV was less than 10% and classification error was around 11%.

CONCLUSIONS

The algorithm developed herein was extensively tested and is not dependent on accurately quantifying the camera's PSF. This feature demonstrates the robustness of the algorithm and enables it to be applied on a wide range of noise and SBR within an image. The ultimate goal of the algorithm is to be able to be operated independent of the camera type used and the reconstruction algorithm deployed.

摘要

目的

作者开发了一种算法，用于分割和消除正电子发射断层扫描（PET）图像中肿瘤的部分容积效应（PVE）。该算法可以独立于相机的全宽半最大值（FWHM）准确测量肿瘤的功能体积（FV）和活性浓度（AC）。

方法

开发了一种新颖的迭代直方图阈值（HT）算法，用于分割 PET 图像中的肿瘤，这些图像分辨率较低，并且图像中存在固有噪声。该算法通过手动绘制感兴趣区域（ROI）启动。分割后的肿瘤接受迭代去卷积阈值分割（IDTS）算法的处理，其中使用范·凯特尔（Van-Cittert）去卷积方法校正 PVE。IDTS 算法是全自动的，可以准确测量 FV 和 AC，并且一旦达到收敛就停止。收敛标准或停止条件的制定方式是，该算法不依赖于估计点扩散函数（PSF）的 FWHM 来执行去卷积过程。这里描述的算法在体模研究中进行了测试，其中空心球体（0.5-16 ml）用于表示具有均匀活性分布的肿瘤，而不规则形状的体积用于表示具有不均匀活性分布的肿瘤。进行了具有不同信号与背景比（SBR）和不同采集时间（1 分钟、3 分钟和 5 分钟）的体模研究。还改变了算法中的参数（FWHM 和高斯函数的矩阵大小），以检查算法的准确性。还使用具有不均匀活性分布的肿瘤的模拟数据来测试算法。

结果

结果表明，在启动算法期间改变 ROI 的大小和形状对 FV 没有显著影响。对于整个噪声和 SBR 水平范围内的最小肿瘤（0.5 ml），观察到平均 FV 高估 30%，平均 AC 低估 35%。随着肿瘤大小增加到 16 ml，FV 和 AC 实际体积的估计值之间的差异小于 5%。对于具有不均匀活性分布的肿瘤，整体体积误差小于 10%。FV 的平均高估小于 10%，分类误差约为 11%。