Finite-Element-Based Matching of Pre- and Intraoperative Data for Image-Guided Endovascular Aneurysm Repair

Abstract

Endovascular repair of abdominal aortic aneurysms is a well-established technique throughout the medical and surgical communities. Although increasingly indicated, this technique does have some limitations. Because intervention is commonly performed under fluoroscopic control, 2-D visualization of the aneurysm requires the injection of a contrast agent. The projective nature of this imaging modality inevitably leads to topographic errors, and does not give information on arterial wall quality at the time of deployment. A specially adapted intraoperative navigation interface could increase deployment accuracy and reveal such information, which preoperative 3-D imaging might otherwise provide. One difficulty is the precise matching of preoperative data (images and models) and intraoperative observations affected by anatomical deformations due to tool-tissue interactions. Our proposed solution involves a finite-element-based preoperative simulation of tool-tissue interactions, its adaptive tuning regarding patient specific data, and the matching with intraoperative data. The biomechanical model was first tuned on a group of ten patients and assessed on a second group of eight patients.

Patient‐specific simulation of guidewire deformation during transcatheter aortic valve implantation

“…Similar studies were performed by Dumenil et al and Gindre et al for the insertion of guidewires during endovascular aneurysm repair . Other studies were dedicated to the insertion of catheter during mini‐invasive treatment of cerebrovascular disease .…”

“…A distance map was computed from the 2D aortic contour with the Chamfer operator. The minimization of the distance criterion was iteratively performed by using a Powell optimization algorithm until the estimate of the 3D/2D transform was satisfactory . The resulting projection is shown in Figure A.…”

Finite element simulation of the insertion of guidewires during an EVAR procedure: example of a complex patient case, a first step toward patient‐specific parameterized models

“…The model's parameters were tuned manually by trial and error in order to reduce the simulation error defined for each guidewire by the MHD to the real intraoperative guidewire. The calibration was considered to be acceptable once the error for each guidewire became less than 3 mm, which corresponds to the error level obtained by projecting the results of simulations performed by Dumenil et al onto 2D projection images (2.8 mm average). The set of parameters obtained after this calibration phase is called the ‘nominal configuration’.…”

Ein Prototyp für die navigierte Implantation von Aortenstentprothesen zur Reduzierung der Kontrastmittel- und Strahlenbelastung: Das Nav-CARS-EVAR-Konzept (Navigated-Contrast-Agent and Radiation Sparing Endovascular Aortic Repair)

“…Dies liegt vor allem an der Deformierbarkeit der Gefäße und des umliegenden Gewebes. Diese Deformationen entstehen auf der einen Seite durch die Applikation der Stentprothese und zum anderen durch Atembewegungen des Patienten [30].…”