Research graph
References from Artificial intelligence for reducing metal artifacts in dental CBCT images: a systematic review. Local targets link to admitted publications; unresolved targets remain external evidence.
Level of knowledge of dental practitioners in Isfahan, Iran about cone-beam computed tomography and digital radiography
10.5624/isd.2015.45.2.133 · 2015 · External reference
U-net based metal segmentation on projection domain for metal artifact reduction in dental CT
10.1007/s13534-019-00110-2 · 2019 · External reference
Reducing metal artifacts in cone-beam CT images by preprocessing projection data
10.1016/j.ijrobp.2006.09.045 · 2007 · External reference
Metal streak artifacts in X-ray computed tomography: a simulation study
1998 · External reference
Metal artifact reduction in CT: where are we after four decades?
10.1109/access.2016.2608621 · 2016 · External reference
CT artifacts: causes and reduction techniques
10.2217/iim.12.13 · 2012 · External reference
Unresolved reference
2003 · External reference
Nonlinear partial volume artifacts in x-ray computed tomography
10.1118/1.594678 · 1980 · External reference
Convolutional neural networks: an overview and application in radiology
10.1007/s13244-018-0639-9 · 2018 · External reference
Generative adversarial networks and its applications in biomedical informatics
10.3389/fpubh.2020.00164 · 2020 · External reference
SEA-Net: structure-enhanced attention network for limited-angle CBCT reconstruction of clinical projection data
2023 · External reference
A fidelity-embedded learning for metal artifact reduction in dental CBCT
10.1002/mp.15720 · 2022 · External reference
Feasibility of image quality improvement for high-speed CBCT imaging using deep convolutional neural network for image-guided radiotherapy in prostate cancer
10.1016/j.ejmp.2020.10.012 · 2020 · External reference
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
10.7326/0003-4819-155-8-201110180-00009 · 2011 · External reference
Implicit neural representation-based method for metal-induced beam hardening artifact reduction in X-ray CT imaging
10.1002/mp.17649 · 2025 · External reference
The impact of AI on metal artifacts in CBCT oral cavity imaging
10.3390/diagnostics14121280 · 2024 · External reference
Convolutional neural network–based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme
10.1002/mp.15884 · 2022 · External reference
Deep-learning-based metal artefact reduction with unsupervised domain adaptation regularization for practical CT images
10.1109/tmi.2023.3244252 · 2023 · External reference
b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT
10.1088/1361-6560/ad3c0a · 2024 · External reference
A new dental CBCT metal artifact reduction method based on a dual-domain processing framework
10.1088/1361-6560/acec29 · 2023 · External reference
Hybrid model-based and deep learning-based metal artifact reduction method in dental cone-beam computed tomography
10.1016/j.net.2023.05.016 · 2023 · External reference
Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan
10.1088/1361-6560/ac8852 · 2022 · External reference
A quantitative evaluation of the effectiveness of the metal artifact reduction algorithm in cone beam computed tomographic images with stainless steel orthodontic brackets and arch wires: an ex vivo study
10.3390/diagnostics14020159 · 2024 · External reference
Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network
10.1002/mp.16277 · 2023 · External reference
Is AI the way forward for reducing metal artifacts in CT? Development of a generic deep learning-based method and initial evaluation in patients with sacroiliac joint implants
10.1016/j.ejrad.2023.110844 · 2023 · External reference