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Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization.

机译:使用生物力学FEM模型和基于强度的优化自动进行多模态2D / 3D乳房图像配准。

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摘要

Due to their different physical origin, X-ray mammography and Magnetic Resonance Imaging (MRI) provide complementary diagnostic information. However, the correlation of their images is challenging due to differences in dimensionality, patient positioning and compression state of the breast. Our automated registration takes over part of the correlation task. The registration method is based on a biomechanical finite element model, which is used to simulate mammographic compression. The deformed MRI volume can be compared directly with the corresponding mammogram. The registration accuracy is determined by a number of patient-specific parameters. We optimize these parameters--e.g. breast rotation--using image similarity measures. The method was evaluated on 79 datasets from clinical routine. The mean target registration error was 13.2mm in a fully automated setting. On basis of our results, we conclude that a completely automated registration of volume images with 2D mammograms is feasible. The registration accuracy is within the clinically relevant range and thus beneficial for multimodal diagnosis.
机译:由于它们的物理来源不同,因此X射线乳房X线照片和磁共振成像(MRI)可提供补充的诊断信息。然而,由于尺寸,患者位置和乳房受压状态的差异,其图像的相关性具有挑战性。我们的自动注册接管了相关任务的一部分。配准方法基于生物力学有限元模型,该模型用于模拟乳房X线照片压缩。可以将变形后的MRI体积直接与相应的乳房X线照片进行比较。配准精度由许多患者特定参数确定。我们优化这些参数-例如乳房旋转-使用图像相似性度量在临床常规的79个数据集上对该方法进行了评估。在全自动设置中,平均目标对准误差为13.2mm。根据我们的结果,我们得出结论,使用2D乳房X线照片对体积图像进行全自动配准是可行的。配准精度在临床相关范围内,因此对于多模式诊断是有益的。

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