Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomography
Michael D. Ketcha, Michael Marrama, André Souza, Ali Uneri, Pengwei Wu, Xiaoxuan Zhang, Patrick A. Helm, Jeffrey H. Siewerdsen
Abstract
Purpose: Cone-beam computed tomography (CBCT) is commonly used in the operating room to evaluate the placement of surgical implants in relation to critical anatomical structures. A particularly problematic setting, however, is the imaging of metallic implants, where strong artifacts can obscure visualization of both the implant and surrounding anatomy. Such artifacts are compounded when combined with low-dose imaging techniques such as sparse-view acquisition.
Topics & Concepts
Convolutional neural networkMedicineArtificial intelligenceComputer visionImaging phantomCone beam computed tomographyVisualizationArtifact (error)SegmentationIterative reconstructionBiomedical engineeringRadiologyComputer scienceComputed tomographyAdvanced X-ray and CT ImagingMedical Imaging Techniques and ApplicationsRadiation Dose and Imaging