Gifford Howard C, Liang Zhihua, Das Mini
Department of Biomedical Engineering, University of Houston, Houston, Texas 77204.
Department of Biomedical Engineering, University of Houston, Houston, Texas 77204 and Department of Physics, University of Houston, Houston, Texas 77204.
Med Phys. 2016 Mar;43(3):1563-75. doi: 10.1118/1.4942485.
Mathematical model observers commonly used for diagnostic image-quality assessments in x-ray imaging research are generally constrained to relatively simple detection tasks due to their need for statistical prior information. Visual-search (VS) model observers that employ morphological features in sequential search and analysis stages have less need for such information and fewer task constraints. The authors compared four VS observers against human observers and an existing scanning model observer in a pilot study that quantified how mass detection and localization in simulated digital breast tomosynthesis (DBT) can be affected by the number P of acquired projections.
Digital breast phantoms with embedded spherical masses provided single-target cases for a localization receiver operating characteristic (LROC) study. DBT projection sets based on an acquisition arc of 60° were generated for values of P between 3 and 51. DBT volumes were reconstructed using filtered backprojection with a constant 3D Butterworth postfilter; extracted 2D slices were used as test images. Three imaging physicists participated as observers. A scanning channelized nonprewhitening (CNPW) observer had knowledge of the mean lesion-absent images. The VS observers computed an initial single-feature search statistic that identified candidate locations as local maxima of either a template matched-filter (MF) image or a gradient-template MF (GMF) image. Search inefficiencies that modified the statistic were also considered. Subsequent VS candidate analyses were carried out with (i) the CNPW statistical discriminant and (ii) the discriminant computed from GMF training images. These location-invariant discriminants did not utilize covariance information. All observers read 36 training images and 108 study images per P value. Performance was scored in terms of area under the LROC curve.
Average human-observer performance was stable for P between 7 and 35. In the absence of search inefficiencies, the VS models based on the GMF analysis provided the best correlation (Pearson ρ ≥ 0.62) with the human results. The CNPW-based VS observers deviated from the humans primarily at lower values of P. In this limited study, search inefficiencies allowed for good quantitative agreement with the humans for most of the VS observers.
The computationally efficient training requirements for the VS observer are suitable for high-resolution imaging, indicating that the observer framework has the potential to overcome important task limitations of current model observers for x-ray applications.
在X射线成像研究中，常用于诊断图像质量评估的数学模型观察者通常由于需要统计先验信息而局限于相对简单的检测任务。在顺序搜索和分析阶段采用形态学特征的视觉搜索（VS）模型观察者对这类信息的需求较少，任务限制也较少。在一项初步研究中，作者将四种VS观察者与人类观察者以及现有的扫描模型观察者进行了比较，该研究量化了采集投影数量P如何影响模拟数字乳腺断层合成（DBT）中的肿块检测和定位。
带有嵌入式球形肿块的数字乳腺体模为定位接收器操作特性（LROC）研究提供了单目标病例。针对P在3到51之间的值，生成了基于60°采集弧的DBT投影集。使用带有恒定三维巴特沃斯后置滤波器的滤波反投影重建DBT体积；提取的二维切片用作测试图像。三名成像物理学家作为观察者参与。一个扫描通道化非白化（CNPW）观察者了解平均无病变图像。VS观察者计算一个初始单特征搜索统计量，将候选位置识别为模板匹配滤波器（MF）图像或梯度模板MF（GMF）图像的局部最大值。还考虑了修改统计量的搜索低效性。随后的VS候选分析使用（i）CNPW统计判别式和（ii）从GMF训练图像计算的判别式进行。这些位置不变判别式未利用协方差信息。每个P值下，所有观察者阅读36幅训练图像和108幅研究图像。根据LROC曲线下的面积对性能进行评分。
对于P在7到35之间，人类观察者的平均性能稳定。在没有搜索低效性的情况下，基于GMF分析的VS模型与人类结果的相关性最佳（皮尔逊ρ≥0.62）。基于CNPW的VS观察者主要在P值较低时偏离人类。在这项有限的研究中，搜索低效性使得大多数VS观察者与人类在定量上具有良好的一致性。
VS观察者高效的计算训练要求适用于高分辨率成像，表明该观察者框架有潜力克服当前X射线应用模型观察者的重要任务限制。
