Multi-modal image registration is a crucial pre-processing step in many
medical applications. However, it is a challenging task due to the complex
intensity relationships between different imaging modalities, which can result
in large discrepancy in image appearance. The success of multi-modal image
registration, whether it is conventional or learning based, is predicated upon
the choice of an appropriate distance (or similarity) measure. Particularly,
deep learning registration algorithms lack in accuracy or even fail completely
when attempting to register data from an "unseen" modality. In this work, we
present Modality Agnostic Distance (MAD), a deep image distance}] measure that
utilises random convolutions to learn the inherent geometry of the images while
being robust to large appearance changes. Random convolutions are
geometry-preserving modules which we use to simulate an infinite number of
synthetic modalities alleviating the need for aligned paired data during
training. We can therefore train MAD on a mono-modal dataset and successfully
apply it to a multi-modal dataset. We demonstrate that not only can MAD
affinely register multi-modal images successfully, but it has also a larger
capture range than traditional measures such as Mutual Information and
Normalised Gradient Fields.

通过使用随机卷积来学习图像的固有几何结构，并依靠 Modality Agnostic Distance (MAD) 作为深度图像距离测度，我们展示了 MAD 不仅能够成功地进行多模态图像仿射配准，而且还具有比传统测度（如互信息和归一化梯度场）更大的拍摄范围。