Today, 2D+T fluoroscopy is usually used for image guidance in interventional radiology. For challenging procedures, 4D (3D+T) image guidance would be advantageous. The difficulty in realizing X-ray-based 4D interventional guidance lies in the development of a very dose efficient reconstruction algorithm. To this end, we improve on a previously presented algorithm for the reconstruction of interventional tools. By incorporating temporal information into a 3D convolutional neural network, we reduce the number of X-ray projections that need to be acquired for the 3D reconstruction of guidewires from four to two, thereby halving dose and decreasing the demands put on imaging devices implementing the algorithm. In experiments with two moving guidewires in an anthropomorphic phantom, we observe little deviation of our 3D reconstructions from the ground truth.
|