Multimodal Sensing Guidewire for C-Arm Navigation with Random UV Enhanced Optical Sensors Using Spatio-Temporal Networks

Abstract

Percutaneous transluminal angioplasty (PTA) revascularization is a common minimally invasive treatment for occlusions in peripheral arteries, but it’s success in long occlusions is limited by technical challenges associated with crossing occluded vessels and lumen re-entry. Revascularization needs to be guided closely using ionizing imaging such as fluoroscopy, while intravascular guidewires lack the capability of characterizing physiological conditions near occlusions, such as blood flow. We propose a multimodal sensing framework to infer both three-dimensional shape and vascular flow from an optical fiber device using random optical gratings enhanced with ultraviolet exposure, allowing a fully-distributed strain sensor. A two-branch spatio-temporal neural network is proposed to process a generated optical signal trajectory from scattered wavelength distributions. A shape network is first used in combination with the pre-procedural 3D angiography image to track the 3D shape related to backscattered wavelength shift, while a flow velocity network trained on 4D-MRI measurements allows to extract vascular flow. A final refinement is performed to adjust the 3D-2D projection onto C-arm images, allowing to correct for slight deviations of the sensed shape. Synthetic and porcine experiments were performed in a controlled environment setting, enabling to measure the accuracy of the 3D shape tracking and flow measurements, with errors of 2.4 ± 0.9 mm and flow differences below 2 cm/s, demonstrating the ability to provide anatomical and physiological properties during vascular procedures.