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A mathematical model approach towards combining information from multiple image projections of the same patient

机译:一种数学模型方法,用于合并来自同一患者的多个图像投影的信息

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The purpose of this study was to, ⅰ) use a mathematical observer model to combine information obtained from multiple angular projections of the same breast to determine the overall detectability of a simulated lesion in a multi-projection breast imaging system and, ⅱ) determine the optimum acquisition parameters of such a system. Multi-projection imaging is similar to tomosynthesis, except that the raw projection images are directly analyzed instead of reconstructing those images, thereby avoiding reconstruction artifacts. 25 angular projections of each breast from 82 human subjects in our tomosynthesis clinical trials were supplemented with projections from a simulated 3 mm 3D lesion. The lesion was assumed to be embedded in the compressed breast at a distance of 3 cm from the detector. The contrast of the lesion was determined taking into account the energy spectrum of the x-ray beam, properties of the digital detector, scatter fraction, and compressed breast thickness. A linear Hotelling observer with Laguerre-Gauss channels (LG CHO) was applied to each image. Detectability was analyzed in terms of ROC curves and the area under ROC curves (AUC). Three different methods were used to integrate ROCs from multiple (correlated) views to obtain one combined ROC as an overall metric of detectability. Specifically, 1) ROCs from different projections were simply averaged; 2) the test statistics from different projections were averaged; and 3) a Bayesian decision fusion rule was used. Finally, the number of angular projections, angular span and the acquisition dose level were optimized for highest AUC of the combined ROC as a parameter to maximize the performance of the system. It was found that the Bayesian decision fusion technique performs better than the other two techniques and likely offers the best approximation of the diagnostic process. Furthermore, if the total dose level is held constant at 1/25th of the standard dual-view mammographic screening dose, the highest detectability performance is observed when considering only two projections spread along an angular span of 11.4°.
机译:这项研究的目的是:(ⅰ)使用数学观察器模型来组合从同一乳房的多个角度投影获得的信息,以确定在多投影乳房成像系统中模拟病变的整体可检测性,并且(ⅱ)确定这种系统的最佳采集参数。多投影成像与断层合成相似,不同之处在于直接分析原始投影图像而不是重建那些图像,从而避免了重建伪像。在我们的层析合成临床试验中,来自82位人类受试者的每个乳房的25个角投影与模拟的3 mm 3D病变的投影相辅相成。假定病变与检测器相距3 cm嵌入到受压乳房中。确定病变的对比度时要考虑到X射线束的能谱,数字检测器的特性,散射分数和压缩的乳房厚度。将具有Laguerre-Gauss通道(LG CHO)的线性Hotelling观察器应用于每个图像。根据ROC曲线和ROC曲线下面积(AUC)分析了可检测性。三种不同的方法用于从多个(相关的)视图中集成ROC,以获得一个组合的ROC作为可检测性的总体指标。具体来说,1)简单估算来自不同预测的ROC; 2)对不同预测的测试统计数据取平均值; 3)使用贝叶斯决策融合规则。最后,针对组合式ROC的最高AUC,优化了角投影的数量,角跨度和采集剂量水平,以此作为最大化系统性能的参数。发现贝叶斯决策融合技术比其他两种技术表现更好,并且可能提供诊断过程的最佳近似。此外,如果总剂量水平保持恒定,为标准双乳腺X线检查筛查剂量的1/25,则当仅考虑沿11.4°角展布的两个投影时,观察到的检测性能最高。

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