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.

翻译：多模态图像配准是许多医学应用中的关键预处理步骤。然而，由于不同成像模态间复杂的灰度强度关系可能导致图像外观的巨大差异，该任务极具挑战性。无论是传统方法还是基于学习的多模态图像配准，其成功与否均取决于是否选择了合适的距离（或相似性）度量。特别是，深度学习配准算法在尝试配准“未见”模态的数据时，往往精度不足甚至完全失效。本文提出模态无关距离（MAD），一种采用随机卷积来学习图像内在几何结构、同时对大幅外观变化具有鲁棒性的深度图像距离度量。随机卷积作为保几何结构模块，用于模拟无限数量的合成模态，从而在训练过程中无需成对对齐数据。因此，我们能够在单模态数据集上训练MAD，并成功将其应用于多模态数据集。实验表明，MAD不仅能够成功进行多模态图像的仿射配准，其捕获范围也显著大于互信息、归一化梯度场等传统度量。