Breast tissue deformation has recently gained interest in various medical applications. The recovery of large deformations caused by gravity or compression loads and image registration is a non-trivial task. The most effective tool for breast cancer visualisation is Magnetic Resonance Imaging (MRI). However, for MRI scans the patient is in a prone position with the breast placed in signal enhancement coils, while other procedures, i.e. surgery, PET-CT (Positron Emission Tomography fused with Computer Tomography) are performed with the patient in a supine position. The need therefore arises to estimate the large breast deformations caused by natural body movement during examinations or surgery. There is no doubt that a patient's breast in both positions has a different shape and that this influences relationships between intra-breast structures. In this work, we present the fundamentals of a method for transformation of breast images based on Finite Element Methods (FEMs). This 2D model uses the simplest constitutive tissue description, which makes it easily applicable and fast. According to the Jaccard Index, the average accuracy obtained is 95%, the lowest is 87%, and the highest is 99%. The model parameter set is proposed for six different breast size classes, covering the whole population. The algorithm provides reliable breast images in a supine position in a few simple steps.

乳房组织变形最近已在各种医学应用中引起关注。恢复由重力或压缩载荷和图像配准引起的大变形是一项不小的任务。乳腺癌可视化最有效的工具是磁共振成像（MRI）。但是，对于MRI扫描，患者处于俯卧位置，乳房放置在信号增强线圈中，而其他操作（即手术），PET-CT（正电子发射断层扫描与计算机断层摄影相融合）是在患者仰卧位置进行的。因此，需要估计由检查或手术期间自然的身体运动引起的较大的乳房变形。毫无疑问，两个部位的患者乳房都有不同的形状，这会影响乳房内部结构之间的关系。在这项工作中，我们介绍了基于有限元方法（FEM）的乳房图像转换方法的基础。此2D模型使用最简单的本构组织描述，因此易于应用且快速。根据Jaccard索引，获得的平均准确度为95％，最低为87％，最高为99％。提出了针对六个不同乳房尺寸类别的模型参数集，覆盖了整个人群。该算法只需几个简单的步骤即可在仰卧位置提供可靠的乳房图像。最低的是87％，最高的是99％。提出了针对六个不同乳房尺寸类别的模型参数集，覆盖了整个人群。该算法只需几个简单的步骤即可在仰卧位置提供可靠的乳房图像。最低的是87％，最高的是99％。提出了针对六个不同乳房尺寸类别的模型参数集，覆盖了整个人群。该算法只需几个简单的步骤即可在仰卧位置提供可靠的乳房图像。