Department of Biomedical Engineering, University of Houston, Houston, Texas 77204, USA.
Med Phys. 2013 Sep;40(9):092505. doi: 10.1118/1.4818824.
Mathematical model observers are intended for efficient assessment of diagnostic image quality, but model-observer studies often are not representative of clinical realities. Model observers based on a visual-search (VS) paradigm may allow for greater clinical relevance. The author has compared the performances of several VS model observers with those of human observers and an existing scanning model observer for a study involving nodule detection and localization in simulated Tc-99m single-photon emission computed tomography (SPECT) lung volumes.
A localization receiver operating characteristic (LROC) study compared two iterative SPECT reconstruction strategies: an all-corrections (AllC) strategy with compensations for attenuation, scatter, and distance-dependent camera resolution and an "RC" strategy with resolution compensation only. Nodules in the simulation phantom were of three different relative contrasts. Observers in the study had access to the coronal, sagittal, and transverse displays of the reconstructed volumes. Three human observers each read 50 training volumes and 100 test volumes per reconstruction strategy. The same images were analyzed by a channelized nonprewhitening (CNPW) scanning observer and two VS observers. The VS observers implemented holistic search processes that identified focal points of Tc-99m uptake for subsequent analysis by the CNPW scanning model. The level of prior knowledge about the background structure in the images was a study variable for the model observers. Performance was scored by area under the LROC curve.
The average human-observer performances were respectively 0.67 ± 0.04 and 0.61 ± 0.03 for the RC and AllC strategies. Given approximate knowledge about the background structure, both VS models scored 0.69 ± 0.08 (RC) and 0.66 ± 0.08 (AllC). The scanning observer reversed the strategy ranking in scoring 0.73 ± 0.08 with the AllC strategy and 0.64 ± 0.08 with the RC strategy. The VS observers exhibited less sensitivity to variations in background knowledge compared to the scanning observer.
The VS framework has the potential to increase the clinical similitude of model-observer studies and to enhance the ability of existing model observers to quantitatively predict human-observer performance.
数学模型观察者旨在高效评估诊断图像质量，但模型观察者研究通常与临床实际情况不符。基于视觉搜索（VS）范式的模型观察者可能具有更大的临床相关性。作者比较了几种 VS 模型观察者与人类观察者以及现有的扫描模型观察者在模拟 Tc-99m 单光子发射计算机断层扫描（SPECT）肺部容积中的结节检测和定位研究中的性能。
定位接收者操作特征（LROC）研究比较了两种迭代 SPECT 重建策略：一种是具有衰减、散射和距离相关相机分辨率补偿的全校正（AllC）策略，另一种是仅具有分辨率补偿的“RC”策略。模拟体模中的结节具有三种不同的相对对比度。研究中的观察者可以访问重建体积的冠状、矢状和横断显示。每位人类观察者分别阅读每种重建策略的 50 个训练卷和 100 个测试卷。相同的图像由通道非白化（CNPW）扫描观察者和两个 VS 观察者进行分析。VS 观察者实施了整体搜索过程，确定了 Tc-99m 摄取的焦点，以便后续由 CNPW 扫描模型进行分析。图像中背景结构的先验知识水平是模型观察者的一个研究变量。性能通过 LROC 曲线下的面积进行评分。
平均人类观察者的性能分别为 0.67 ± 0.04 和 0.61 ± 0.03，RC 和 AllC 策略。在具有近似背景结构知识的情况下，两个 VS 模型的得分分别为 0.69 ± 0.08（RC）和 0.66 ± 0.08（AllC）。扫描观察者在评分中反转了策略排名，AllC 策略为 0.73 ± 0.08，RC 策略为 0.64 ± 0.08。VS 观察者与扫描观察者相比，对背景知识变化的敏感性较低。
VS 框架有可能提高模型观察者研究的临床相似性，并增强现有模型观察者定量预测人类观察者性能的能力。
