Assisting vascular access surgery planning for hemodialysis by using MR, image segmentation techniques, and computer simulations.

ABSTRACT

The surgical creation of a vascular access, used for hemodialysis treatment of renal patients, has considerable complication rates (30-50 %). Image-based computational modeling might assist the surgeon in planning by enhanced analysis of preoperative hemodynamics, and in the future might serve as platform for outcome prediction. The objective of this study is to investigate preoperative personalization of the computer model using magnetic resonance (MR). MR-angiography and MR-flow data were obtained for eight patients and eight volunteers. Blood vessels were extracted for model input by a segmentation algorithm. Windkessel elements were added at the ends to represent the peripheral beds. Monte Carlo-based calibration was used to estimate the most influential non-measurable parameters. The predicted flow waveforms were compared with the MR-flow measurements for framework evaluation. The vasculature of all subjects were segmented in on average <5 min. The Monte Carlo-calibrated simulations showed a deviation between measured and simulated flow waveforms of 9 and 10 % for volunteers and patients, respectively. The presented method accurately mimics the preoperative hemodynamic state. Furthermore, the surgeon can interactively explore the hemodynamics at any vascular tree position. This integration of measurements in a modeling approach can provide the surgeon with additional information for preoperative planning.