Feed

Breast cancer is a significant health risk for middle-aged and elderly women, with high incidence and mortality rates. Accurate diagnosis and treatment rely on both mammography and breast MRI images, which provide complementary diagnostic information. Combined observation of these images helps localize lesions and plan treatments. However, direct mapping of information between 2D mammography and 3D MRI images is challenging. We propose a 2D/3D registration framework for breast MRI-Mammography images-BreastBioMorph (BBMorph). A biomechanical finite element model based on 3D MRI reconstructions and hyperelastic Mooney-Rivlin material simulates breast tissue and mammography compression, providing coarse alignment between deformed MRI volumes and mammography. An Edge-Guided unsupervised registration network integrates breast edge constraints into a pyramid structure, refining deformation fields progressively. The effectiveness of the registration framework is validated with clinical breast data. Experimental results are compared with those of state-of-the-art cross-modal registration approaches. The proposed method achieves promising results, with a Dice coefficient of 89.05%, MI of 0.497, SSIM of 0.815, ASSD of 17.11, and %|J_φ|≤0 of 0.104. The method provides a valuable reference for the registration of cross-modal breast images.