While mathematical model observers are intended for efficient assessment of medical imaging systems, their findings should be relevant for human observers as the primary clinical end users. We have investigated whether pursuing equivalence between the model and human-observer tasks can help ensure this goal. A localization receiver operating characteristic (LROC) study tested prostate lesion detection in simulated In-111 SPECT imaging with anthropomorphic phantoms. The test images were 2D slices extracted from reconstructed volumes. The iterative ordered sets expectation-maximization (OSEM) reconstruction algorithm was used with Gaussian postsmoothing. Variations in the number of iterations and the level of postfiltering defined the test strategies in the study. Human-observer performance was compared with that of a visual-search (VS) observer, a scanning channelized Hotelling observer, and a scanning channelized nonprewhitening (CNPW) observer. These model observers were applied with precise information about the target regions of interest (ROIs). ROI knowledge was a study variable for the human observers. In one study format, the humans read the SPECT image alone. With a dual-modality format, the SPECT image was presented alongside an anatomical image slice extracted from the density map of the phantom. Performance was scored by area under the LROC curve. The human observers performed significantly better with the dual-modality format, and correlation with the model observers was also improved. Given the human-observer data from the SPECT study format, the Pearson correlation coefficients for the model observers were 0.58 (VS), $-0.12$ (CH), and $-0.23$ (CNPW). The respective coefficients based on the human-observer data from the dual-modality study were 0.72, 0.27, and $-0.11$. These results point towards the continued development of the VS observer for enhancing task equivalence in model-observer studies.

中文翻译：

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SPECT 本地化研究中模型和人类观察者比较的任务等效性

虽然数学模型观察者旨在有效评估医学成像系统，但他们的发现应该与作为主要临床最终用户的人类观察者相关。我们已经调查了在模型和人类观察任务之间寻求等效性是否有助于确保实现这一目标。一项定位接收器操作特性 (LROC) 研究测试了模拟 In-111 SPECT 成像与拟人模型中的前列腺病变检测。测试图像是从重建体积中提取的 2D 切片。迭代有序集期望最大化 (OSEM) 重建算法与高斯后平滑一起使用。迭代次数和后过滤级别的变化定义了研究中的测试策略。人类观察者的表现与视觉搜索 (VS) 观察者的表现进行了比较，一个扫描通道化Hotelling观察者和一个扫描通道化非预白化（CNPW）观察者。这些模型观察器被应用于有关目标感兴趣区域 (ROI) 的精确信息。ROI 知识是人类观察者的研究变量。在一种研究形式中，人类单独阅读 SPECT 图像。采用双模态格式，SPECT 图像与从体模密度图中提取的解剖图像切片一起呈现。性能按 LROC 曲线下的面积进行评分。使用双模态格式时，人类观察者的表现明显更好，并且与模型观察者的相关性也得到了改善。鉴于来自 SPECT 研究格式的人类观察者数据，模型观察者的 Pearson 相关系数为 0.58 (VS)、$-0.12$ (CH) 和 $-0.23$ (CNPW)。基于来自双模态研究的人类观察者数据的相应系数为 0.72、0.27 和 $-0.11$。这些结果表明 VS 观察者的持续发展，以增强模型观察者研究中的任务等效性。