首页> 外文会议>Conference on Medical Imaging 2008: Computer-Aided Diagnosis; 20080219-21; San Diego,CA(US) >Optimized Acquisition Scheme for Multi-projection Correlation Imaging of Breast Cancer
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Optimized Acquisition Scheme for Multi-projection Correlation Imaging of Breast Cancer

机译:乳腺癌多投影相关成像的优化采集方案

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We are reporting the optimized acquisition scheme of multi-projection breast Correlation Imaging (CI) technique, which was pioneered in our lab at Duke University. CI is similar to tomosynthesis in its image acquisition scheme. However, instead of analyzing the reconstructed images, the projection images are directly analyzed for pathology. Earlier, we presented an optimized data acquisition scheme for CI using mathematical observer model. In this article, we are presenting a Computer Aided Detection (CADe)-based optimization methodology. Towards that end, images from 106 subjects recruited for an ongoing clinical trial for tomosynthesis were employed. For each patient, 25 angular projections of each breast were acquired. Projection images were supplemented with a simulated 3 mm 3D lesion. Each projection was first processed by a traditional CADe algorithm at high sensitivity, followed by a reduction of false positives by combining geometrical correlation information available from the multiple images. Performance of the CI system was determined in terms of free-response receiver operating characteristics (FROC) curves and the area under ROC curves. For optimization, the components of acquisition such as the number of projections, and their angular span were systematically changed to investigate which one of the many possible combinations maximized the sensitivity and specificity. Results indicated that the performance of the CI system may be maximized with 7-11 projections spanning an angular arc of 44.8°, confirming our earlier findings using observer models. These results indicate that an optimized CI system may potentially be an important diagnostic tool for improved breast cancer detection.
机译:我们正在报告多投影乳房相关成像(CI)技术的优化采集方案,该方案是杜克大学在我们实验室中首创的。 CI在其图像采集方案中类似于断层合成。然而,代替分析重建的图像,直接分析投影图像的病理。之前,我们使用数学观察器模型提出了一种针对CI的优化数据采集方案。在本文中,我们提出了一种基于计算机辅助检测(CADe)的优化方法。为此,采用了来自正在进行的断层合成临床试验的106名受试者的图像。对于每个患者,获取每个乳房的25个角投影。投影图像辅以模拟的3毫米3D病变。每个投影首先通过传统的CADe算法以高灵敏度进行处理,然后通过组合可从多个图像获得的几何相关信息来减少误报。 CI系统的性能由自由响应接收器工作特性(FROC)曲线和ROC曲线下的面积确定。为了进行优化,系统地更改了获取的组成部分(例如,投影的数量及其角度跨度),以研究许多可能的组合中的哪一种使灵敏度和特异性最大化。结果表明,CI系统的性能可以通过7-11个跨度为44.8°的弧形投影来最大化,这证实了我们先前使用观察者模型得出的发现。这些结果表明,优化的CI系统可能是改善乳腺癌检测的重要诊断工具。

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