Grant: Operating Grant: Industry-partnered Collaborative Research - Other Partner

An increasing incidence of abdominal aortic aneurysms (AAA) is observed in our ageing population. Since it is less invasive, endovascular repair (EVAR) by stent-graft (SG) insertion is frequently indicated in patients with intermediate and high-operative risk. Patient selection and stent planning for EVAR is done on contrast-enhanced CT-scans based on specific anatomic criteria. Since SG implantation are performed under digital substraction angiography (DSA) and fluoroscopic guidance, no soft-tissue differentiation is available during the intervention and important information are lacking such as thrombus and aneurysm extension close to proximal and distal landing zones. More recently, complex EVAR procedures involving preservation of renal or digestive arteries by SG fenestration (fenestrated SG) or internal iliac arteries (branched iliac SG) are performed. Accurate deployment of these devices are particularly challenging under fluoroscopy. Enhanced visualization combining fluoroscopy, DSA and CT-scan data could tremendously shorten procedure time. Our team have recently patented and validated a software allowing a semi-automated 3D segmentation of AAA (lumen and thrombus). More recently, catheterization laboratories are equipped with flat panel detectors allowing to combine DSA and C-arm CT acquisitions. The objectives of this study are to: 1. Develop an integrated software allowing registration of preoperative CT-scanner with per-operative C-arm CT using our segmentation algorithm. 2. Merge these two data set with a correction process to compensate for the deformation of the AAA induced by the SG delivery system 3. Register this merged CT-data set with DSA and fluoroscopy to improve visualization during SG delivery. This study will be conducted in partnership with Object Research System (ORS), a small Canadian company specialized in the visualization and analysis of 3D/4D medical imaging datasets in collaboration with Siemens Medical.