Karlsruhe Institute of Technology, Hermann-v.Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.
Med Image Anal. 2013 Feb;17(2):209-18. doi: 10.1016/j.media.2012.10.003. Epub 2012 Nov 29.
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.
由于其不同的物理起源，X 射线乳房摄影和磁共振成像（MRI）提供了互补的诊断信息。然而，由于维度、患者体位和乳房压缩状态的差异，它们的图像相关性具有挑战性。我们的自动配准接管了部分相关任务。该配准方法基于生物力学有限元模型，用于模拟乳房 X 光摄影压缩。变形的 MRI 体积可以与相应的乳房 X 光照片直接进行比较。配准精度由许多患者特定的参数决定。我们使用图像相似性度量来优化这些参数，例如乳房旋转。该方法在 79 个来自临床常规的数据集上进行了评估。在全自动设置下，平均目标配准误差为 13.2mm。基于我们的结果，我们得出结论，使用二维乳房 X 光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，因此对多模态诊断有益。
