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Statistical Deformation Models of Breast Compressions from Biomechanical Simulations

机译:生物力学模拟乳房压缩统计变形模型

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This paper describes the construction of 3D statistical deformation models (SDMs) from biomechanical simulations. The method was used to capture the average breast motion and its variability due to compressing it between two plates as performed for X-ray mammography. One SDM described the motion from the compressed to the undeformed state. Another SDM captured the deformation difference due to variations in patient positioning and compression magnitude. Such models could prove useful for guiding the development of algorithms to register serial X-ray mammograms. The SDMs are based on simulating plausible breast compressions for a population of 20 patients via finite element models created from segmented 3D MR breast images. Tissue properties and boundary conditions were varied according to reported values. Three compression configurations (called current, priori, prior2) were simulated per breast. The associated displacement fields were mapped into a common space and SDMs were generated using principle component analysis. Leave-one-patient-out tests showed that these models can reduce the mean error of unseen deformations on average by 87% (19.35mm to 2.49mm for current-to-undeformed, 13.49mm to 1.70mm for current-to-prior) when using the first 16 modes of variation.
机译:本文介绍的3D统计变形模型(SDMS)从生物力学模拟建设。用于捕获的平均乳房运动及其变化的方法,由于作为X射线乳房X射线摄影执行的两个板之间压缩它。一个SDM从压缩到未变形状态描述的运动。另一个SDM捕获的变形差,由于在患者定位和压缩幅值变化。这样的模型可以证明,用于引导算法的开发,以注册序列X射线乳房X线照片非常有用。的SDMS是基于模拟似是而非乳房压缩用于通过从分割的3D MR乳房图像创建有限元模型的群体的20例患者。根据报告的值组织性质和边界条件是变化的。三个压缩配置(称为电流,先验,prior2)每乳房进行了模拟。相关联的位移字段被映射到一个共同的空间,并利用主成分分析产生SDMS。留一患者出试验表明,这些模型可以通过87%平均降低看不见变形的平均误差(19.35毫米到2.49毫米用于电流到未变形的13.49毫米到1.70毫米为电流 - )之前使用变化的第一16种模式时。

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