| Summary: | The evolution of lung lesions can be assessed by examining multiple CT screenings, which needs to align two CT images accurately. In this study, we propose a multi-stride and multi-resolution affine registration network, called MMAR-net, for 3D affine registration of medical images, which works in an unsupervised way by optimizing the similarity loss. In order to extract more extensive image features, we use a multi-stride module to replace the conventional convolution module. Furthermore, we make use of the image features at multiple scales by dot product between two feature vectors, which could enhance the robustness of image representation. We conduct comprehensive comparison experiments between our model and the existing affine registration methods on two publicly available datasets, DIR-Lab and Learn2Reg, which are both relevant to lung CT image registration. Quantitative and qualitative comparison results demonstrate that our model outperforms existing single-step affine registration networks. Our method improves the key metric of dice similarity coefficient on DIR-Lab and Learn2Reg to 90.57% and 95.51%, respectively.
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