3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images

In this note, we present an approach for developing patient-specific 3D models of portal veins to provide geometric boundary conditions for computational fluid dynamics (CFD) simulations of the blood flow inside portal veins. The study is based on MRI liver images of individual patients to which we apply image registration and segmentation techniques and inlet and outlet velocity profiles acquired using PC-MRI in the same imaging session. The portal vein and its connected veins are then extracted and visualized in 3D as surfaces. Image registration is performed to align shifted images between each breath-hold when the MRI images are acquired. The image segmentation method first labels each voxel in the 3D volume of interest by using a Bayesian probability approach, and then isolates the portal veins via active surfaces initialized inside the vessel. The method was tested with two healthy volunteers. In both cases, the main portal vein and its connected veins were successfully modeled and visualized

[1]  David J. Hawkes,et al.  Voxel similarity measures for 3-D serial MR brain image registration , 1999, IEEE Transactions on Medical Imaging.

[2]  D. Efe,et al.  Portal and splenic hemodynamics in cirrhotic patients: relationship between esophageal variceal bleeding and the severity of hepatic failure , 2004, Journal of Gastroenterology.

[3]  B. Gala,et al.  Assessment of portal hemodynamics by ultrasound color Doppler and laser Doppler velocimetry in liver cirrhosis. , 2002, Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology.

[4]  A. Tannenbaum,et al.  Knowledge-based 3D segmentation and reconstruction of coronary arteries using CT images , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  David A. Steinman,et al.  Image-Based Computational Fluid Dynamics Modeling in Realistic Arterial Geometries , 2002, Annals of Biomedical Engineering.

[6]  Guillermo Sapiro,et al.  Knowledge-based segmentation of SAR data with learned priors , 2000, IEEE Trans. Image Process..

[7]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[8]  A. Tannenbaum,et al.  Flow Patterns and Wall Shear Stress Distributions at Atherosclerotic-Prone Sites in a Human Left Coronary Artery - An Exploration Using Combined Methods of CT and Computational Fluid Dynamics , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  T. Baysal,et al.  Quantitative Doppler evaluation of the splenoportal venous system in various stages of cirrhosis: Differences between right and left portal veins , 2002, Journal of clinical ultrasound : JCU.

[10]  R. Kimmel,et al.  Geodesic Active Contours , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[12]  P. Olver,et al.  Conformal curvature flows: From phase transitions to active vision , 1996, ICCV 1995.

[13]  Guillermo Sapiro,et al.  Invariant Geometric Evolutions of Surfaces and Volumetric Smoothing , 1997, SIAM J. Appl. Math..