Dept. of Medical Physics and Quality Assessment, KU Leuven, Leuven, Belgium.
Dept. of Medical Physics and Quality Assessment, KU Leuven, Leuven, Belgium; Dept. of Radiology, UZ Leuven, Belgium.
Phys Med. 2019 Feb;58:8-20. doi: 10.1016/j.ejmp.2018.12.033. Epub 2019 Jan 17.
to develop a channelized model observer (CHO) that matches human reader (HR) scoring of a physical phantom containing breast simulating structure and mass lesion-like targets for use in quality control of digital breast tomosynthesis (DBT) imaging systems.
A total of 108 DBT scans of the phantom was acquired using a Siemens Inspiration DBT system. The detectability of mass-like targets was evaluated by human readers using a 4-alternative forced choice (4-AFC) method. The percentage correct (PC) values were then used as the benchmark for CHO tuning, again using a 4-AFC method. Three different channel functions were considered: Gabor, Laguerre-Gauss and Difference of Gaussian. With regard to the observer template, various methods for generating the expected signal were studied along with the influence of the number of training images used to form the covariance matrix for the observer template. Impact of bias in the training process on the observer template was evaluated next, as well as HR and CHO reproducibility.
HR performance was most closely matched by 8 Gabor channels with tuned phase, orientation and frequency, using an observer template generated from training image data. Just 24 DBT image stacks were required to give robust CHO performance with 0% bias, although a bias of up to 33% in the training images also gave acceptable performance. CHO and HR reproducibility were similar (on average 3.2 PC versus 3.4 PC).
The CHO algorithm developed matches human reader performance and is therefore a promising candidate for automated readout of phantom studies.
开发一种与人类读者（HR）评分相匹配的信道化模型观察者（CHO），用于评估包含乳房模拟结构和质量模拟目标的物理体模，以用于数字乳腺断层合成（DBT）成像系统的质量控制。
使用西门子 Inspiration DBT 系统获取总共 108 次体模的 DBT 扫描。通过 4 种备选的强迫选择（4-AFC）方法，由 HR 评估类肿块目标的检测性能。然后使用 PC 值作为 CHO 调谐的基准，同样采用 4-AFC 方法。考虑了三种不同的通道函数：Gabor、拉盖尔-高斯和高斯差分。关于观察者模板，研究了各种生成预期信号的方法，以及用于形成观察者模板协方差矩阵的训练图像数量的影响。接下来评估了训练过程中的偏差对观察者模板的影响，以及 HR 和 CHO 的可重复性。
HR 性能与经过调谐相位、方向和频率的 8 个 Gabor 通道最为匹配，使用的是基于训练图像数据生成的观察者模板。仅需 24 个 DBT 图像堆栈即可获得无偏差的稳健 CHO 性能，尽管训练图像中存在高达 33%的偏差也能获得可接受的性能。CHO 和 HR 的可重复性相似（平均为 3.2 PC 与 3.4 PC）。
开发的 CHO 算法与 HR 性能相匹配，因此是自动读取体模研究的有前途的候选方法。
