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Proceedings Paper

Feasibility of 3D motion-compensated needle guidance for TIPS procedures
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Paper Abstract

X-ray fluoroscopy is commonly used to guide needles during transjugular intrahepatic portosystemic shunt (TIPS) procedures. Respiratory motion and the 2D nature of the x-ray projections, however, make it difficult to accurately guide the needle from a hepatic vein, through the liver parenchyma, and into the portal vein, which is not visible on x-ray images in the absence of continuous contrast-enhancement. Due to these challenges, multiple needle passes are often required, which increase the risk for perforation of the liver capsule and hemorrhage. To overcome these challenges, we propose a motion-compensated 3D needle guidance system, which generates a respiratory motion model of the portal venous system using a 3D DSA and a contrast enhanced 2D x-ray sequence acquired under free breathing conditions. The respiratory motion is tracked during the needle pass based on brightness variations above and below the diaphragm in the 2D images, which allows for creation of a motion-compensated surface model of the target vasculature. Additionally, a 3D needle reconstruction algorithm from two 2D x-ray images is presented, which allows for motion-compensated 3D device imaging. A preliminary pig study was performed to evaluate the feasibility of the proposed techniques. The biplane needle reconstruction was compared to conventional cone beam CT acquisitions, where a root mean squared distance of 0.98 mm and a tip localization error of 1.22 mm were measured. The maximum error of the estimated vascular motion per frame in the two pig studies was 0.63 mm and 1.63 mm respectively. If successfully translated to clinical TIPS procedures, the proposed needle guidance could result in fewer unnecessary needle passes and therefore shorter procedure times and lower risk to the patient.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131505 (16 March 2020); doi: 10.1117/12.2548874
Show Author Affiliations
Martin G. Wagner, Univ. of Wisconsin-Madison (United States)
Sarvesh Periyasamy, Univ. of Wisconsin-Madison (United States)
Michael A. Speidel, Univ. of Wisconsin-Madison (United States)
Paul F. Laeseke, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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