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Library based x-ray scatter correction for dedicated cone beam breast CT

机译:基于库的X射线散射校正专用锥形束乳房CT

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摘要

PURPOSE: The image quality of dedicated cone beam breast CT (CBBCT) is limited by substantial scatter contamination, resulting in cupping artifacts and contrast-loss in reconstructed images. Such effects obscure the visibility of soft-tissue lesions and calcifications, which hinders breast cancer detection and diagnosis. In this work, we propose a library-based software approach to suppress scatter on CBBCT images with high efficiency, accuracy, and reliability.METHODS: The authors precompute a scatter library on simplified breast models with different sizes using the geant4-based Monte Carlo (MC) toolkit. The breast is approximated as a semiellipsoid with homogeneous glandular/adipose tissue mixture. For scatter correction on real clinical data, the authors estimate the breast size from a first-pass breast CT reconstruction and then select the corresponding scatter distribution from the library. The selected scatter distribution from simplified breast models is spatially translated to match the projection data from the clinical scan and is subtracted from the measured projection for effective scatter correction. The method performance was evaluated using 15 sets of patient data, with a wide range of breast sizes representing about 95% of general population. Spatial nonuniformity (SNU) and contrast to signal deviation ratio (CDR) were used as metrics for evaluation.RESULTS: Since the time-consuming MC simulation for library generation is precomputed, the authors\u27 method efficiently corrects for scatter with minimal processing time. Furthermore, the authors find that a scatter library on a simple breast model with only one input parameter, i.e., the breast diameter, sufficiently guarantees improvements in SNU and CDR. For the 15 clinical datasets, the authors\u27 method reduces the average SNU from 7.14% to 2.47% in coronal views and from 10.14% to 3.02% in sagittal views. On average, the CDR is improved by a factor of 1.49 in coronal views and 2.12 in sagittal views.CONCLUSIONS: The library-based scatter correction does not require increase in radiation dose or hardware modifications, and it improves over the existing methods on implementation simplicity and computational efficiency. As demonstrated through patient studies, the authors\u27 approach is effective and stable, and is therefore clinically attractive for CBBCT imaging.
机译:目的:专用的锥形束乳腺CT(CBBCT)的图像质量受到大量散布污染的限制,从而在重建图像中导致拔罐伪影和对比度损失。这样的影响使软组织病变和钙化的可见度变得模糊,这阻碍了乳腺癌的检测和诊断。在这项工作中,我们提出了一种基于库的软件方法来高效,准确和可靠地抑制CBBCT图像上的散射。方法:作者使用基于geant4的Monte Carlo(不同大小的简化乳房模型)预先计算了一个散射库。 MC)工具包。乳房近似为具有均匀腺体/脂肪组织混合物的半椭圆体。为了对实际临床数据进行散点校正,作者从首次通过的胸部CT重建中估计了乳房大小,然后从库中选择了相应的散点分布。从简化的乳房模型中选择的散射分布在空间上进行转换以匹配临床扫描的投影数据,并从测量的投影中减去以进行有效的散射校正。使用15组患者数据评估了方法的性能,其中广泛的乳腺大小代表了总人口的95%。结果:由于预先计算了用于库生成的耗时的MC模拟,因此作者的方法有效地校正了散射,并且处理时间最短。此外,作者发现,在仅具有一个输入参数即乳房直径的简单乳房模型上的散布库足以保证SNU和CDR的改善。对于15个临床数据集,作者的方法在冠状视图中将平均SNU从7.14%降低到2.47%,在矢状视图中将平均SNU从10.14%降低到3.02%。平均而言,在冠状视野中CDR改善了1.49倍,在矢状视野中改善了2.12倍。结论:基于库的散射校正不需要增加辐射剂量或硬件修改,并且在实现简单性上优于现有方法和计算效率。如患者研究所示,作者的方法有效且稳定,因此对CBBCT成像具有临床吸引力。

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