Martin G Wagner1, Sarvesh Periyasamy2, Colin Longhurst3, Matthew J McLachlan1, Joseph F Whitehead1, Michael A Speidel1,4, Paul F Laeseke2. 1. Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA. 2. Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA. 3. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA. 4. Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA.
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.
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.
Authors: F G Court; S A Wemyss-Holden; C P Morrison; B D Teague; P E Laws; J Kew; A R Dennison; G J Maddern Journal: Br J Surg Date: 2003-04 Impact factor: 6.939
Authors: Paul J Dorio; Fred T Lee; Kevin P Henseler; Mark Pilot; Myron A Pozniak; Thomas C Winter; Sarah A Shock Journal: AJR Am J Roentgenol Date: 2003-04 Impact factor: 3.959