3D Catheter Shape Reconstruction Using Electromagnetic and Image Sensors
Abstract
In current practice, fluoroscopy remains the gold standard for guiding surgeons during endovascular catheterization. The poor visibility of anatomical structures and the absence of depth information make accurate catheter localization and manipulation a difficult task. Overexposure to radiation and use of risk-prone contrast agent also compromise surgeons’ and patients’ health. Alternative approaches using embedded electromagnetic (EM) sensors have been developed to overcome the limitations of fluoroscopy-based interventions. As only a finite number of sensors can be integrated within a catheter, methods that rely on such sensors require the use of interpolation schemes to recover the catheter shape. Since EM sensors are sensitive to external interferences, the outcome is not robust. This paper introduces a probabilistic framework that improves the catheter localization and reduces the dependency on fluoroscopy and contrast agents. Within this framework, the dense 2D information extracted from fluoroscopic images is combined with the discrete pose information of EM sensors to provide a reliable reconstruction of the full three-dimensional catheter shape. Validation in a physics-based simulation environment and in a real-world experimental setup provides promising results and indicates that the proposed framework allows reconstructing the 3D catheter shape with a median root-mean-square error of 3.7mm with an interquartile range of 0.3mm.
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