Real-time 3D Shape Instantiation for Partially-deployed Stent Segment from a Single 2D Fluoroscopic Image in Robot-assisted Fenestrated Endovascular Aortic Repair.

In 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 two-dimensional (2-D) fluoroscopic images, which lacks 3-D anatomical information, thus causing a longer operation time and high risks of radiation exposure. Previously, 3-D shape instantiation frameworks for real-time 3-D shape reconstruction of fully deployed or fully compressed stent grafts from a single 2-D fluoroscopic image have been proposed for 3-D navigation in FEVAR. However, these methods could not instantiate partially deployed stent segments, as the 3-D marker references are unknown. In this letter, an adapted graph convolutional network (GCN) is proposed to predict 3-D partially deployed marker references from 3-D fully deployed marker references. As the original GCN is for classification, in this letter, the coarsening layers are removed and the softmax function at the network end is replaced with linear mapping for regression. The derived 3-D marker references and the 2-D marker positions are used to instantiate the partially deployed stent segment, combined with the previous 3-D shape instantiation framework. Validations were performed on three typical stent grafts and five patient-specific 3-D printed aortic aneurysm phantoms. Reasonable performances with average mesh distance errors from 1.0 to 2.4 mm and average angular errors around $7.2^\circ$ were achieved.