In this large meta-analysis of patients undergoing EVAR, we found that several comorbidity and postoperative factors were associated with postoperative sac shrinkage. These findings may contribute to a better understanding of the shrinkage process of patients undergoing EVAR.

We presented a versatile 3D/2D rigid registration applicable to all intraoperative scenes and usable to guide an EVAR procedure by augmented reality.

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.

This paper presents a new approach dealing with virtual exploratory navigation inside vascular structures. It is based on the notion of active vision in which only visual perception drives the motion of the virtual angioscope. The proposed fly-through approach does not require a premodeling of the volume dataset or an interactive control of the virtual sensor during the fly-through. Active navigation combines the on-line computation of the scene view and its analysis, to automatically define the three-dimensional sensor path. The navigation environment and the camera-like model are first sketched. The basic stages of the active navigation framework are then described: the virtual image computation (based on ray casting), the scene analysis process (using depth map), the navigation strategy, and the virtual path estimation. Experimental results obtained from phantom model and patient computed tomography data are finally reported.