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In robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR), accuratealignment of stent graft fenestrations or scallops with aortic branches isessential for establishing complete blood flow perfusion. Current navigation islargely 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 shapereconstruction of fully-deployed or fully-compressed stent graft from a single2D fluoroscopic image have been proposed for 3D navigation in robot-assistedFEVAR. However, these methods could not instantiate partially-deployed stentsegments, as the 3D marker references are unknown. In this paper, an adaptedGraph Convolutional Network (GCN) is proposed to predict 3D marker referencesfrom 3D fully-deployed markers. As original GCN is for classification, in thispaper, the coarsening layers are removed and the softmax function at thenetwork end is replaced with linear mapping for the regression task. Thederived 3D and the 2D marker references are used to instantiatepartially-deployed stent segment shape with the existing 3D shape instantiationframework. Validations were performed on three commonly used stent grafts andfive patient-specific 3D printed aortic aneurysm phantoms. Comparableperformances with average mesh distance errors of 1$\sim$3mm and averageangular errors around 7degree were achieved.

ieee robotics and automation letters

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