  Image guidance for minimally invasive interventions is usually performed by
acquiring fluoroscopic images using a C-arm system. However, the projective
data provide only limited information about the spatial structure and position
of interventional tools such as stents, guide wires or coils. In this work we
propose a deep learning-based pipeline for real-time tomographic
(four-dimensional) interventional guidance at acceptable dose levels. In the
first step, interventional tools are extracted from four cone-beam CT
projections using a deep convolutional neural network (CNN). These projections
are then reconstructed and fed into a second CNN, which maps this highly
undersampled reconstruction to a segmentation of the interventional tools. Our
pipeline is capable of reconstructing interventional tools from only four x-ray
projections without the need for a patient prior with very high accuracy.
Therefore, the proposed approach is capable of overcoming the drawbacks of
today's interventional guidance and could enable the development of new
minimally invasive radiological interventions by providing full spatiotemporal
information about the interventional tools.
