Real‐time respiratory motion compensated roadmaps for hepatic arterial interventions

Abstract

Purpose During hepatic arterial interventions, catheter or guidewire position is determined by referencing or overlaying a previously acquired static vessel roadmap. Respiratory motion leads to significant discrepancies between the true position and configuration of the hepatic arteries and the roadmap, which makes navigation and accurate catheter placement more challenging and time consuming. The purpose of this work was to develop a dynamic respiratory motion compensated device guidance system and evaluate the accuracy and real‐time performance in an in vivo porcine liver model. Methods The proposed device navigation system estimates a respiratory motion model for the hepatic vasculature from prenavigational X‐ray image sequences acquired under free‐breathing conditions with and without contrast enhancement. During device navigation, the respiratory state is tracked based on live fluoroscopic images and then used to estimate vessel deformation based on the previously determined motion model. Additionally, guidewires and catheters are segmented from the fluoroscopic images using a deep learning approach. The vessel and device information are combined and shown in a real‐time display. Two different display modes are evaluated within this work: (1) a compensated roadmap display, where the vessel roadmap is shown moving with the respiratory motion; (2) an inverse compensated device display, where the device representation is compensated for respiratory motion and overlaid on a static roadmap. A porcine study including seven animals was performed to evaluate the accuracy and real‐time performance of the system. In each pig, a guidewire and microcatheter with a radiopaque marker were navigated to distal branches of the hepatic arteries under fluoroscopic guidance. Motion compensated displays were generated showing real‐time overlays of the vessel roadmap and intravascular devices. The accuracy of the motion model was estimated by comparing the estimated vessel motion to the motion of the X‐ray visible marker. Results The median (minimum, maximum) error across animals was 1.08 mm (0.92 mm, 1.87 mm). Across different respiratory states and vessel branch levels, the odds of the guidewire tip being shown in the correct vessel branch were significantly higher (odds ratio = 3.12, p < 0.0001) for motion compensated displays compared to a noncompensated display (median probabilities of 86 and 69%, respectively). The average processing time per frame was 17 ms. Conclusions The proposed respiratory motion compensated device guidance system increased the accuracy of the displayed device position relative to the hepatic vasculature. Additionally, the provided display modes combine both vessel and device information and do not require the mental integration of different displays by the physician. The processing times were well within the range of conventional clinical frame rates.

Real-time continuous image guidance for endoscopic retrograde cholangiopancreatography based on 3D/2D registration and respiratory compensation

“…The diaphragm-based method not only requires manual annotation, but also cannot guarantee real-time performance, thus prolonging the operation time and increasing X-ray radiation exposure. The method based on X-ray image centroid extraction was used in the current study to estimate the respiratory state[ 9 ] and monitor the motion change between frames through X-ray image gray distribution. The respiratory motion curve was acquired and used to complete continuous compensation for the fluoroscopy sequence, and real-time biliary roadmapping projected onto X-ray fluoroscopy was completed.…”

“…Preoperative three-dimensional (3D) imaging is widely used for planning in interventional radiosurgery. Three-dimensional computed tomography (3D-CT) has been utilized in transcatheter arterial chemoembolization (TACE), and allows for better visualization of small vessels[ 8 , 9 ]. Transjugular intrahepatic portosystemic shunt (TIPS) combines preoperative 3D-CT images with images taken at the time of intervention, and has demonstrated a significant reduction in operation times and radiation doses[ 10 ].…”