Experimental comparison of lesion detectability for four fully-3D PET reconstruction schemes.
Experimental comparison of lesion detectability for four fully-3D PET reconstruction schemes.
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DOI：
10.1109/tmi.2008.2006520
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发表时间：
2009-04
影响因子：
10.6
通讯作者：
Conti M
中科院分区：
文献类型：
--
作者：
Kadrmas DJ;Casey ME;Black NF;Hamill JJ;Panin VY;Conti M
这项工作的目的是利用实验获得的数据评估四种全三维正电子发射断层扫描（PET）重建方案的病变检测性能。建立一个多室拟人化幻影来模拟全身18f -氟脱氧葡萄糖（FDG）癌症成像，并在3D模式下扫描12次，获得典型的嘈杂临床扫描计数水平。其中8个扫描有26个68Ge“无壳”病变（直径为6,8,10,12,16毫米），以不同的目标：背景比放置在整个幻像中。这提供了病变存在和病变不存在的数据集，具有已知的真理，适合通过定位接收器操作特征（LROC）方法评估病变可检测性。研究了四种重建方案：1)傅里叶重建（FORE），然后是二维衰减加权有序子集期望最大化，2)全三维AW-OSEM， 3)全三维普通泊松响应线（LOR-）OSEM；4)具有精确点扩展函数（PSF）模型的全3d loro - osem。进行了两种形式的LROC分析。首先，采用信道化非预白化（CNPW）观测器优化处理参数（迭代次数、重建后滤波器），用于人体观测器的研究。然后，人类观察者对每张图像进行评级，并选择最可能的病变位置。以LROC曲线下面积（ALROC）和正确定位的概率作为优值。人类观察者研究结果显示，FORE与AW-OSEM3D之间无统计学差异（ALROC分别为0.41和0.36），而loro - osem3d的病变检测性能有所提高（ALROC = 0.45, p = 0.076），使用PSF模型后进一步改善（ALROC = 0.55, p = 0.024）。数值CNPW观测器提供了相同的算法排名，但得到了不同的ALROC值。这些结果表明，与上一代算法相比，具有更复杂的统计和成像模型的重建算法的病变检测性能有所提高。
The objective of this work was to evaluate the lesion detection performance of four fully-3D positron emission tomography (PET) reconstruction schemes using experimentally acquired data. A multi-compartment anthropomorphic phantom was set up to mimic whole-body 18F-fluorodeoxyglucose (FDG) cancer imaging and scanned 12 times in 3D mode, obtaining count levels typical of noisy clinical scans. Eight of the scans had 26 68Ge “shell-less” lesions (6, 8-, 10-, 12-, 16-mm diameter) placed throughout the phantom with various target:background ratios. This provided lesion-present and lesion-absent datasets with known truth appropriate for evaluating lesion detectability by localization receiver operating characteristic (LROC) methods. Four reconstruction schemes were studied: 1) Fourier rebinning (FORE) followed by 2D attenuation-weighted ordered-subsets expectation-maximization, 2) fully-3D AW-OSEM, 3) fully-3D ordinary-Poisson line-of-response (LOR-)OSEM; and 4) fully-3D LOR-OSEM with an accurate point-spread function (PSF) model. Two forms of LROC analysis were performed. First, a channelized nonprewhitened (CNPW) observer was used to optimize processing parameters (number of iterations, post-reconstruction filter) for the human observer study. Human observers then rated each image and selected the most-likely lesion location. The area under the LROC curve (ALROC) and the probability of correct localization were used as figures-of-merit. The results of the human observer study found no statistically significant difference between FORE and AW-OSEM3D (ALROC = 0.41 and 0.36, respectively), an increase in lesion detection performance for LOR-OSEM3D (ALROC = 0.45, p = 0.076), and additional improvement with the use of the PSF model (ALROC = 0.55, p = 0.024). The numerical CNPW observer provided the same rankings among algorithms, but obtained different values of ALROC. These results show improved lesion detection performance for the reconstruction algorithms with more sophisticated statistical and imaging models as compared to the previous-generation algorithms.