A Patient-Specific Self-supervised Model for Automatic X-Ray/CT Registration

Abstract

The accurate estimation of X-ray source pose in relation to pre-operative images is crucial for minimally invasive procedures. However, existing deep learning-based automatic registration methods often have one or some limitations, including heavy reliance on subsequent conventional refinement steps, requiring manual annotation for training, or ignoring the patient’s anatomical specificity. To address these limitations, we propose a patient-specific and self-supervised end-to-end framework. Our approach utilizes patient’s preoperative CT to generate simulated X-rays that include patient-specific information. We propose a self-supervised regression neural network trained on the simulated patient-specific X-rays to predict six degrees of freedom pose of the X-ray source. In our proposed network, regularized autoencoder and multi-head self-attention mechanism are employed to encourage the model to automatically capture patient-specific salient information that supports accurate pose estimation, and Incremental Learning strategy is adopted for network training to avoid over-fitting and promote network performance. Meanwhile, an novel refinement model is proposed, which provides a way to obtain gradients with respect to the pose parameters to further refine the pose predicted by the regression network. Our method achieves a mean projection distance of 3.01 mm with a success rate of $100\%$ on simulated X-rays, and a mean projection distance of 1.55 mm on X-rays. The code is available at github.com/BaochangZhang/PSSS_registration.