Real-time 3D Shape Instantiation from Single Fluoroscopy Projection for Fenestrated Stent Graft Deployment

TL;DR

This paper presents a real-time method to reconstruct the 3D shape of fenestrated stent grafts from a single fluoroscopic image, improving accuracy in robot-assisted vascular repair.

Contribution

It introduces a novel framework combining marker segmentation, pose estimation, and shape interpolation for 3D shape instantiation from minimal imaging data.

Findings

Achieved 1-3mm average distance error

Achieved 4-degree average angle error

Validated on patient-specific phantoms

Abstract

Robot-assisted deployment of fenestrated stent grafts in Fenestrated Endovascular Aortic Repair (FEVAR) requires accurate geometrical alignment. Currently, this process is guided by 2D fluoroscopy, which is uninformative and error prone. In this paper, a real-time framework is proposed to instantiate the 3D shape of a fenestrated stent graft based on only a single low-dose 2D fluoroscopic image. Firstly, the fenestrated stent graft was placed with markers. Secondly, the 3D pose of each stent segment was instantiated by the RPnP (Robust Perspective-n-Point) method. Thirdly, the 3D shape of the whole stent graft was instantiated via graft gap interpolation. Focal-Unet was proposed to segment the markers from 2D fluoroscopic images to achieve semi-automatic marker detection. The proposed framework was validated on five patient-specific 3D printed phantoms of aortic aneurysms and three stent grafts with new marker placements, showing an average distance error of 1-3mm and an average angle error of 4 degree.