Deep learning based approaches have been used to improve image quality in cone-beam
computed tomography (CBCT), a medical imaging technique often used in applications such …

Robot-guided computed tomography enables the inspection of parts that are too large for
conventional systems and allows, for instance, the non-destructive and volumetric …

Metal artifacts present a challenge to cone-beam CT (CBCT) image-guided surgery,
obscuring visualization of metal instruments and adjacent anatomy—often in the very region …

Purpose During spinal fusion surgery, screws are placed close to critical nerves suggesting
the need for highly accurate screw placement. Verifying screw placement on high-quality …

Cone beam computed tomography (CBCT) has become a vital tool in interventional
radiology. Usually, a circular source-detector trajectory is used to acquire a three …

Industrial computed tomography systems usually implement circular or helical trajectories,
which are inherently inefficient since they are applied independently of the inspection task …

Metallic objects in the volume of a CBCT system can cause various artifacts after image
reconstruction such as bright and dark streaks, local distortions of CT values and …

Metal Artifacts remain a problem in Cone-Beam CT (CBCT) imaging, especially reducing
clinical value in trauma applications by obscuring the important area around implants …

Purpose: To develop and validate a deep-learning-based algorithm (DLA) that is designed
to segment and classify metallic objects in topograms of abdominal and spinal CT. Methods …

Intraoperative Cone Beam CT (CBCT) is routinely used for implant placement verification but
is confounded by metal artifacts obscuring the clinically relevant area around implants. This …