AIMC Topic: Cone-Beam Computed Tomography

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Can convolutional neural networks identify external carotid artery calcifications?

Oral surgery, oral medicine, oral pathology and oral radiology Apr 20, 2023
OBJECTIVE: We developed and evaluated the accuracy and reliability of a convolutional neural network (CNN) in detecting external carotid artery calcifications (ECACs) in cone beam computed tomography scans.
Carotid Artery, External Carotid Artery Diseases Humans Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted Vascular Calcification Cone-Beam Computed Tomography Calcinosis Male Aged Female Middle Aged Reproducibility of Results
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Self-supervised denoising of projection data for low-dose cone-beam CT.

Medical physics Apr 20, 2023
BACKGROUND: Convolutional neural networks (CNNs) have shown promising results in image denoising tasks. While most existing CNN-based methods depend on supervised learning by directly mapping noisy inputs to clean targets, high-quality references are...
Retrospective Studies Cone-Beam Computed Tomography Phantoms, Imaging Image Processing, Computer-Assisted Neural Networks, Computer Algorithms Humans
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Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review.

Medicina (Kaunas, Lithuania) Apr 15, 2023
This study aims to evaluate the diagnostic accuracy of artificial intelligence in detecting apical pathosis on periapical radiographs. A total of twenty anonymized periapical radiographs were retrieved from the database of Poznan University of Medica...
Periapical Periodontitis Humans Artificial Intelligence Cone-Beam Computed Tomography Diagnostic Tests, Routine Retrospective Studies
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Deep learning synthesis of cone-beam computed tomography from zero echo time magnetic resonance imaging.

Scientific reports Apr 13, 2023
Cone-beam computed tomography (CBCT) produces high-resolution of hard tissue even in small voxel size, but the process is associated with radiation exposure and poor soft tissue imaging. Thus, we synthesized a CBCT image from the magnetic resonance i...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Deep Learning Cone-Beam Computed Tomography Artifacts Phantoms, Imaging Humans
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Inter-fraction deformable image registration using unsupervised deep learning for CBCT-guided abdominal radiotherapy.

Physics in medicine and biology Apr 13, 2023
. CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fractional anatomic changes, e.g. tumor shrinkage, and daily OAR variation t...
Cone-Beam Computed Tomography Radiotherapy Planning, Computer-Assisted Image Processing, Computer-Assisted Radiotherapy, Image-Guided Spiral Cone-Beam Computed Tomography Humans Neoplasms Deep Learning
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Evaluation of an artificial intelligence-based algorithm for automated localization of craniofacial landmarks.

Clinical oral investigations Apr 4, 2023
OBJECTIVES: Due to advancing digitalisation, it is of interest to develop standardised and reproducible fully automated analysis methods of cranial structures in order to reduce the workload in diagnosis and treatment planning and to generate objecti...
Artificial Intelligence Algorithms Reproducibility of Results Anatomic Landmarks Cephalometry Cone-Beam Computed Tomography Imaging, Three-Dimensional
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Synthetic CT generation from CBCT using double-chain-CycleGAN.

Computers in biology and medicine Apr 3, 2023
PURPOSE: Cone-beam CT (CBCT) has the advantage of being less expensive, lower radiation dose, less harm to patients, and higher spatial resolution. However, noticeable noise and defects, such as bone and metal artifacts, limit its clinical applicatio...
Deep Learning Cone-Beam Computed Tomography Signal-To-Noise Ratio Humans Spiral Cone-Beam Computed Tomography Image Processing, Computer-Assisted Radiotherapy Planning, Computer-Assisted
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Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence.

BMC oral health Apr 1, 2023
BACKGROUND: The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis.
Radiography Cephalometry Artificial Intelligence Software Reproducibility of Results Cone-Beam Computed Tomography Imaging, Three-Dimensional
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Deep learning-based fast volumetric imaging using kV and MV projection images for lung cancer radiotherapy: A feasibility study.

Medical physics Mar 24, 2023
PURPOSE: The long acquisition time of CBCT discourages repeat verification imaging, therefore increasing treatment uncertainty. In this study, we present a fast volumetric imaging method for lung cancer radiation therapy using an orthogonal 2D kV/MV ...
Lung Neoplasms Deep Learning Phantoms, Imaging Feasibility Studies Cone-Beam Computed Tomography Humans Lung
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Convolutional neural network-based automated maxillary alveolar bone segmentation on cone-beam computed tomography images.

Clinical oral implants research Mar 23, 2023
OBJECTIVES: To develop and assess the performance of a novel artificial intelligence (AI)-driven convolutional neural network (CNN)-based tool for automated three-dimensional (3D) maxillary alveolar bone segmentation on cone-beam computed tomography ...
Cone-Beam Computed Tomography Image Processing, Computer-Assisted Neural Networks, Computer Artificial Intelligence
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