Multimodal image registration is a challenging but essential step for
numerous image-guided procedures. Most registration algorithms rely on the
computation of complex, frequently non-differentiable similarity metrics to
deal with the appearance discrepancy of anatomical structures between imaging
modalities. Recent Machine Learning based approaches are limited to specific
anatomy-modality combinations and do not generalize to new settings. We propose
a generic framework for creating expressive cross-modal descriptors that enable
fast deformable global registration. We achieve this by approximating existing
metrics with a dot-product in the feature space of a small convolutional neural
network (CNN) which is inherently differentiable can be trained without
registered data. Our method is several orders of magnitude faster than local
patch-based metrics and can be directly applied in clinical settings by
replacing the similarity measure with the proposed one. Experiments on three
different datasets demonstrate that our approach generalizes well beyond the
training data, yielding a broad capture range even on unseen anatomies and
modality pairs, without the need for specialized retraining. We make our
training code and data publicly available.

通过近似现有指标与小型卷积神经网络的特征空间中的点积，我们提出了一个通用框架，用于创建具有表达性的跨模态描述符，从而实现了快速可变形全局注册。我们的方法比基于局部图像块的指标快几个数量级，并且可以直接应用于临床环境中，只需用所提出的指标替换相似度测量。实验表明，我们的方法在训练数据之外也具有很好的泛化性，即使在未知的解剖学和模态组合上也有着广泛的适应能力，无需进行专门的重新训练。我们公开了我们的训练代码和数据。