  In robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR), accurate
alignment of stent graft fenestrations or scallops with aortic branches is
essential for establishing complete blood flow perfusion. Current navigation is
largely based on 2D fluoroscopic images, which lacks 3D anatomical information,
thus causing longer operation time as well as high risks of radiation exposure.
Previously, 3D shape instantiation frameworks for real-time 3D shape
reconstruction of fully-deployed or fully-compressed stent graft from a single
2D fluoroscopic image have been proposed for 3D navigation in robot-assisted
FEVAR. However, these methods could not instantiate partially-deployed stent
segments, as the 3D marker references are unknown. In this paper, an adapted
Graph Convolutional Network (GCN) is proposed to predict 3D marker references
from 3D fully-deployed markers. As original GCN is for classification, in this
paper, the coarsening layers are removed and the softmax function at the
network end is replaced with linear mapping for the regression task. The
derived 3D and the 2D marker references are used to instantiate
partially-deployed stent segment shape with the existing 3D shape instantiation
framework. Validations were performed on three commonly used stent grafts and
five patient-specific 3D printed aortic aneurysm phantoms. Comparable
performances with average mesh distance errors of 1$\sim$3mm and average
angular errors around 7degree were achieved.
