A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data.
A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data.
复制标题
用于量化乳房断层合成投影数据中肿块可检测性的虚拟试验框架。
DOI：
10.1118/1.4800501
复制
发表时间：
2013
期刊：
影响因子：
3.8
通讯作者：
Park,Subok
中科院分区：
文献类型：
--
作者：
Young,Stefano;Bakic,PredragR;Myers,KyleJ;Jennings,RobertJ;Park,Subok
目的：数字乳腺断层合成（DBT）是一种很有前途的乳腺癌筛查工具，已经开始进入临床实践。然而，关于如何定量评估和优化这些系统的争论一直存在，因为图像质量的不同定义可能导致不同的最佳设计策略。我们需要强大而准确的工具来扩展我们对 DBT 系统优化的理解并验证已发布的设计原则。 方法：作者开发了一个用于特定任务 DBT 评估的虚拟试验框架，该框架使用数字模型、开源 X 射线传输代码以及用于定量系统评估的投影空间、空间域观察者模型。作者将重建算法的评估视为一个单独的问题，并重点关注原始、未过滤的投影图像中的信息内容。具体来说，作者研究了扫描角度和角度投影数量对嵌入随机变化的解剖背景中的小（直径 3 毫米）信号的可检测性的影响。可检测性通过受试者工作特征曲线下面积（AUC）来衡量。对三个测试案例重复进行了实验，其中可检测性限制因素是解剖变异性、量子噪声或电子噪声。作者还将虚拟试验框架与其他已发表的研究并列，以说明其优点和缺点。 结果：虚拟 DBT 研究中的大量变量导致很难直接比较不同作者的结果，因此每个结果都必须在特定虚拟试验框架的背景下进行解释。以下结果适用于压缩厚度为 5.15 cm 且体素为 500 μm3 的 25% 密度模型（使用较大的 500 μm2 检测器像素以避免体素边缘伪影）： 1. 对于解剖学可变性限制范围内的原始、未过滤投影图像，AUC 似乎保持恒定或随扫描角度略有增加。 2. 在相同的情况下，当作者固定扫描角度时，AUC 随着投影数量的增加而渐近增加。渐近 AUC 性能的投影阈值数量取决于扫描角度。在量子和电子噪声占主导地位的情况下，AUC 行为作为扫描角度和投影数量的函数有时与解剖学限制的情况不同。例如，在固定扫描角度下，AUC 通常会随着电子噪声主导区域中投影数量的增加而降低。这些结果旨在展示虚拟试验框架的功能，而不是用作 DBT 的优化规则。结论：作者展示了一种新颖的模拟框架和工具，用于以客观的、针对特定任务的方式评估 DBT 系统。该框架有助于进一步研究 DBT 中的图像质量权衡。
Purpose:Digital breast tomosynthesis (DBT) is a promising breast cancer screening tool that has already begun making inroads into clinical practice. However, there is ongoing debate over how to quantitatively evaluate and optimize these systems, because different definitions of image quality can lead to different optimal design strategies. Powerful and accurate tools are desired to extend our understanding of DBT system optimization and validate published design principles.Methods:The authors developed a virtual trial framework for task‐specific DBT assessment that uses digital phantoms, open‐source x‐ray transport codes, and a projection‐space, spatial‐domain observer model for quantitative system evaluation. The authors considered evaluation of reconstruction algorithms as a separate problem and focused on the information content in the raw, unfiltered projection images. Specifically, the authors investigated the effects of scan angle and number of angular projections on detectability of a small (3 mm diameter) signal embedded in randomly‐varying anatomical backgrounds. Detectability was measured by the area under the receiver‐operating characteristic curve (AUC). Experiments were repeated for three test cases where the detectability‐limiting factor was anatomical variability, quantum noise, or electronic noise. The authors also juxtaposed the virtual trial framework with other published studies to illustrate its advantages and disadvantages.Results:The large number of variables in a virtual DBT study make it difficult to directly compare different authors’ results, so each result must be interpreted within the context of the specific virtual trial framework. The following results apply to 25% density phantoms with 5.15 cm compressed thickness and 500 μm3voxels (larger 500 μm2detector pixels were used to avoid voxel‐edge artifacts): 1. For raw, unfiltered projection images in the anatomical‐variability‐limited regime, AUC appeared to remain constant or increase slightly with scan angle. 2. In the same regime, when the authors fixed the scan angle, AUC increased asymptotically with the number of projections. The threshold number of projections for asymptotic AUC performance depended on the scan angle. In the quantum‐ and electronic‐noise dominant regimes, AUC behaviors as a function of scan angle and number of projections sometimes differed from the anatomy‐limited regime. For example, with a fixed scan angle, AUC generally decreased with the number of projections in the electronic‐noise dominant regime. These results are intended to demonstrate the capabilities of the virtual trial framework, not to be used as optimization rules for DBT.Conclusions:The authors have demonstrated a novel simulation framework and tools for evaluating DBT systems in an objective, task‐specific manner. This framework facilitates further investigation of image quality tradeoffs in DBT.