Catheter Tracking and Data Fusion for reducing the X-ray exposition in an Interventional Radiology procedure

Although the many advantages of Interventional Radiology not only being a minimally invasive surgery but also providing minimal risk of infection and better recuperation for the patient. However, this procedure can cause serious damage (cancer or burnt skin) to the patient and especially to the surgeons if they are exposed for long periods to the X-ray radiation. In the state of art, it has been found new remote catheter navigation system in which the equipment uses magnetic fields for controlling and moving the catheter from an external cabin. Additionally, large equipment needs to be installed in the operating room. In order to limit the doses of X-rays without installing large equipment in the operating room, the aim is to decrease the images coming from X rays imagers using sensors that can be integrated into the catheter (like Fibber Bragg Gratings Sensors inside the catheter or MEMS sensors) to reconstruct an image without the need of continuous imaging. In order to do that, accurate and reliable information on the position of the catheter is required to correct the drift of the catheter's sensors. This position can therefore be obtained by image processing on X-ray images (noisy with artefacts). Previous work done by the Medic@ team has shown that conventional image processing approaches are generally too slow or not precise enough. The use of a U-Net convolutional neural network is then a possible solution for detecting the entire catheter (body and head) and obtaining the coordinates of its end. In this article, we will explain and show our first results using the U-net architecture for detecting the tip and the body of the catheter and a kalman filter used for data fusion to evaluate its efficiency to reducing the quantity of images needed in a curvilinear vessel, using generated data.