This project investigates the feasibility of directly regressing pose and position of metallic implants from multiple calibrated X-Ray images. In the context of intraoperative Cone-beam Computed Tomography (CBCT) imaging, this information can be used to avoid metal artifacts by adapting the scanning trajectory such as discussed in this previous work .
By formulating the problem as a set prediction problem, we can build on previous works such as DETR  to design an algorithm which directly models the depicted metallic objects. In addition to applying these existing works, which were developed for and tested on day-to-day optical images, we assess the possibility of incorporating additional knowledge about the relative geometry between the X-Ray images into the model architecture.
 Cone-beam CT trajectory optimization for metal artifact avoidance using ellipsoidal object parameterizations (spiedigitallibrary.org)
 [2005.12872] End-to-End Object Detection with Transformers (arxiv.org)