CT and MRI are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information of both modalities can be very beneficial. Registration is the first step for this fusion. While the soft tissues around the vertebra are deformable, each vertebral body is constrained to move rigidly. We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. To achieve this goal, we introduce anatomy-aware losses for training the network. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. We evaluate our method on an in-house dataset of 167 patients. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.

翻译：CT 和 MRI 是脊椎诊断和治疗规划中两种信息最丰富的模式。 CT 在分析结骨结构时非常有用, MRI 提供有关软组织的信息。 因此, 冻结两种模式的信息非常有益 。 注册是这种聚合的第一步。 虽然脊椎周围的软组织可以变形, 但每个脊椎体都只能僵硬移动。 我们建议一个薄弱的深层次学习框架, 保存每个脊椎的僵硬性和体积, 并最大限度地提高注册的准确性。 为了实现这一目标, 我们引入了对网络的解剖觉损失。 我们专门设计这些损失只依赖于CT标签图, 因为CT的自动脊椎分割会产生与MRI相反的准确结果。 我们评估了我们内部167个病人数据集的方法。 我们的结果表明, 添加解剖觉损失会提高推断的转化的准确性, 同时又保持准确性。