Radiotherapists require accurate registration of MR/CT images to effectively
use information from both modalities. In a typical registration pipeline, rigid
or affine transformations are applied to roughly align the fixed and moving
images before proceeding with the deformation step. While recent learning-based
methods have shown promising results in the rigid/affine step, these methods
often require images with similar field-of-view (FOV) for successful alignment.
As a result, aligning images with different FOVs remains a challenging task.
Self-supervised landmark detection methods like self-supervised Anatomical
eMbedding (SAM) have emerged as a useful tool for mapping and cropping images
to similar FOVs. However, these methods are currently limited to intra-modality
use only. To address this limitation and enable cross-modality matching, we
propose a new approach called Cross-SAM. Our approach utilizes a novel
iterative process that alternates between embedding learning and CT-MRI
registration. We start by applying aggressive contrast augmentation on both CT
and MRI images to train a SAM model. We then use this SAM to identify
corresponding regions on paired images using robust grid-points matching,
followed by a point-set based affine/rigid registration, and a deformable
fine-tuning step to produce registered paired images. We use these registered
pairs to enhance the matching ability of SAM, which is then processed
iteratively. We use the final model for cross-modality matching tasks. We
evaluated our approach on two CT-MRI affine registration datasets and found
that Cross-SAM achieved robust affine registration on both datasets,
significantly outperforming other methods and achieving state-of-the-art
performance.

为了解决不同 FOV 的图像对齐问题，本文提出了一种名为 Cross-SAM 的新方法，该方法利用嵌入学习和 CT-MRI 注册的迭代过程，以实现跨模态匹配，并在 CT-MRI 仿射注册数据集上表现出鲁棒性，明显优于其他方法，达到了最先进的性能。