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  • Title: Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization.
    Author: Hopp T, Dietzel M, Baltzer PA, Kreisel P, Kaiser WA, Gemmeke H, Ruiter NV.
    Journal: Med Image Anal; 2013 Feb; 17(2):209-18. PubMed ID: 23265802.
    Abstract:
    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.
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