Hepatic Vessel Roadmap Prediction Using Adaptive Tracking and Bending Energy Modeling in X-Ray Fluoroscopy

Dynamic visualization of the hepatic vessel is crucial in X-ray image-guided transjugular intrahepatic portosystemic shunt (TIPS) procedures. However, intraoperative breathing and the presence of guidewires complicate the prediction of the vessel position and posture without contrast agents. The respiration compensation technique aims to utilize the intraoperative respiration modeling to deform the initial vessel roadmap, thereby achieving the dynamic vessel prediction in the X-ray image sequence for the interventional guidance. Therefore, we propose a novel respiration compensation framework utilizing the adaptive tracking and bending energy modeling to achieve the stable vessel roadmap prediction under free breathing. First, we introduce the inter-frame rigid displacement compensation module based on the domain adaptation and adaptive centroid tracking. This module fits the respiratory curve from the X-ray images, providing the temporal motion priors for aligning roadmaps across frames. Second, we propose the novel deformation compensation module based on the bending energy modeling to correct the respiratory motion, wherein we utilize the energy features of the guidewires to drive the non-rigid registration. The control points sampled by the bending energy guide the local image to form the deformation field, facilitating the dynamic overlap of the vessel roadmaps in X-ray images. Experimental results on simulated and clinical datasets show an average tracking error of 0.95 $\pm$ 0.26 mm and 1.49 $\pm$ 0.40 mm, respectively. The effective and fast (mean 57 ms per frame) compensation achieved by our framework has the potential for improving the outcome of liver intervention and reducing the reliance on contrast agents.