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 [1].
By formulating the problem as a set prediction problem, we can build on previous works such as DETR [1] 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.
[1] Cone-beam CT trajectory optimization for metal artifact avoidance using ellipsoidal object parameterizations (spiedigitallibrary.org)
[2] [2005.12872] End-to-End Object Detection with Transformers (arxiv.org)