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Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review
March 28, 2024, 4:45 a.m. | Mohammadreza Amirian, Daniel Barco, Ivo Herzig, Frank-Peter Schilling
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 as image-guided radiation therapy, implant dentistry or orthopaedics. In particular, while deep learning methods have been applied to reduce various types of CBCT image artifacts arising from motion, metal objects, or low-dose acquisition, a comprehensive review summarizing the successes and shortcomings of these approaches, with a primary focus on the …
abstract applications artifact arxiv cs.cv deep learning image images imaging implant medical medical imaging quality review therapy type
More from arxiv.org / cs.CV updates on arXiv.org
Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs
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