Department of Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, Minnesota 55905, USA.
Med Phys. 2013 Aug;40(8):081908. doi: 10.1118/1.4812430.
The purpose of this study was to investigate the correlation between model observer and human observer performance in CT imaging for the task of lesion detection and localization when the lesion location is uncertain.
Two cylindrical rods (3-mm and 5-mm diameters) were placed in a 35×26 cm torso-shaped water phantom to simulate lesions with -15 HU contrast at 120 kV. The phantom was scanned 100 times on a 128-slice CT scanner at each of four dose levels (CTDIvol=5.7, 11.4, 17.1, and 22.8 mGy). Regions of interest (ROIs) around each lesion were extracted to generate images with signal-present, with each ROI containing 128×128 pixels. Corresponding ROIs of signal-absent images were generated from images without lesion mimicking rods. The location of the lesion (rod) in each ROI was randomly distributed by moving the ROIs around each lesion. Human observer studies were performed by having three trained observers identify the presence or absence of lesions, indicating the lesion location in each image and scoring confidence for the detection task on a 6-point scale. The same image data were analyzed using a channelized Hotelling model observer (CHO) with Gabor channels. Internal noise was added to the decision variables for the model observer study. Area under the curve (AUC) of ROC and localization ROC (LROC) curves were calculated using a nonparametric approach. The Spearman's rank order correlation between the average performance of the human observers and the model observer performance was calculated for the AUC of both ROC and LROC curves for both the 3- and 5-mm diameter lesions.
In both ROC and LROC analyses, AUC values for the model observer agreed well with the average values across the three human observers. The Spearman's rank order correlation values for both ROC and LROC analyses for both the 3- and 5-mm diameter lesions were all 1.0, indicating perfect rank ordering agreement of the figures of merit (AUC) between the average performance of the human observers and the model observer performance.
In CT imaging of different sizes of low-contrast lesions (-15 HU), the performance of CHO with Gabor channels was highly correlated with human observer performance for the detection and localization tasks with uncertain lesion location in CT imaging at four clinically relevant dose levels. This suggests the ability of Gabor CHO model observers to meaningfully assess CT image quality for the purpose of optimizing scan protocols and radiation dose levels in detection and localization tasks for low-contrast lesions.
本研究旨在探讨在病变位置不确定的情况下，进行 CT 成像中病变检测和定位任务时，模型观察者和人类观察者性能之间的相关性。
将两根直径为 3 毫米和 5 毫米的圆柱形棒放置在一个 35×26 厘米的体模中，以模拟-15 HU 对比的病变，其位置在 120 kV 时。在四个剂量水平（CTDIvol=5.7、11.4、17.1 和 22.8 mGy）下，使用 128 层 CT 扫描仪对每个体模进行 100 次扫描。在每个病变周围提取感兴趣区域（ROI），以生成包含 128×128 像素的信号存在的图像。从没有模拟病变杆的图像中生成信号不存在的 ROI。通过移动 ROI 来随机分布 ROI 中每个病变的位置。由三名受过训练的观察者进行人类观察者研究，以识别病变的存在或不存在，指示每个图像中的病变位置，并对检测任务进行 6 分制的置信度评分。使用具有 Gabor 通道的通道化 Hotelling 模型观察者（CHO）对相同的图像数据进行分析。在模型观察者研究中，为决策变量添加内部噪声。使用非参数方法计算 ROC 和定位 ROC（LROC）曲线的曲线下面积（AUC）。计算了直径为 3 毫米和 5 毫米的病变的 ROC 和 LROC 曲线的 AUC 以及模型观察者性能的平均性能之间的 Spearman 等级相关系数。
在 ROC 和 LROC 分析中，模型观察者的 AUC 值与三名人类观察者的平均值吻合良好。对于直径为 3 毫米和 5 毫米的病变的 ROC 和 LROC 分析，Spearman 等级相关系数均为 1.0，这表明在不同大小的低对比度病变（-15 HU）的 CT 成像中，具有 Gabor 通道的 CHO 模型观察者的性能与人类观察者的检测和定位任务性能高度相关，并且病变位置不确定，在四个临床相关剂量水平下。这表明 Gabor CHO 模型观察者有能力对 CT 图像质量进行有意义的评估，以便在检测和定位低对比度病变的任务中优化扫描协议和辐射剂量水平。
