Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity

TL;DR

This paper introduces a deep learning method for non-rigid inter-modality image registration that uses intra-modality similarity supervision from paired data, enabling accurate and efficient registration of multimodal images like CT and MR.

Contribution

The novel approach trains a registration network supervised by intra-modality similarity metrics, improving accuracy without requiring paired data during testing.

Findings

Achieves promising accuracy in non-rigid inter-modality registration.

Outperforms state-of-the-art methods in experiments.

Efficiently registers multimodal images without paired data at inference.

Abstract

Non-rigid inter-modality registration can facilitate accurate information fusion from different modalities, but it is challenging due to the very different image appearances across modalities. In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images. In particular, the training of our inter-modality registration network is supervised by intra-modality similarity metric based on the available paired data, which is derived from a pre-aligned CT and MR dataset. Specifically, in the training stage, to register the input CT and MR images, their similarity is evaluated on the warped MR image and the MR image that is paired with the input CT. So that, the intra-modality similarity metric can be directly applied to measure whether the input CT and MR images are well registered. Moreover, we use the idea of dual-modality fashion, in which we measure the similarity on both CT modality and MR modality. In this way, the complementary anatomies in both modalities can be jointly considered to more accurately train the inter-modality registration network. In the testing stage, the trained inter-modality registration network can be directly applied to register the new multimodal images without any paired data. Experimental results have shown that, the proposed method can achieve promising accuracy and efficiency for the challenging non-rigid inter-modality registration task and also outperforms the state-of-the-art approaches.