AIMC Topic: Cone-Beam Computed Tomography

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Deep learning-based forward and cross-scatter correction in dual-source CT.

Medical physics Aug 13, 2021
PURPOSE: Dual-source computed tomography (DSCT) uses two source-detector pairs offset by about 90°. In addition to the well-known forward scatter, a special issue in DSCT is cross-scattered radiation from X-ray tube A detected in the detector of syst...
Image Processing, Computer-Assisted Artifacts Scattering, Radiation Phantoms, Imaging Algorithms Tomography, X-Ray Computed Deep Learning Cone-Beam Computed Tomography
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Managing tumor changes during radiotherapy using a deep learning model.

Medical physics Aug 11, 2021
PURPOSE: We propose a treatment planning framework that accounts for weekly lung tumor shrinkage using cone beam computed tomography (CBCT) images with a deep learning-based model.
Deep Learning Cone-Beam Computed Tomography Humans Male Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Lung Neoplasms Carcinoma, Non-Small-Cell Lung
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A geometry-guided deep learning technique for CBCT reconstruction.

Physics in medicine and biology Jul 30, 2021
Although deep learning (DL) technique has been successfully used for computed tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT) reconstruction is extremely challenging due to memory limitations. In this study, a novel DL techn...
Four-Dimensional Computed Tomography Spiral Cone-Beam Computed Tomography Image Processing, Computer-Assisted Phantoms, Imaging Algorithms Deep Learning Cone-Beam Computed Tomography
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Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT.

Clinical oral investigations Jul 27, 2021
OBJECTIVES: The objective of our study was to develop and validate a deep learning approach based on convolutional neural networks (CNNs) for automatic detection of the mandibular third molar (M3) and the mandibular canal (MC) and evaluation of the r...
Molar Spiral Cone-Beam Computed Tomography Cone-Beam Computed Tomography Deep Learning Mandibular Canal Molar, Third
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Distinguishing benign and malignant lesions on contrast-enhanced breast cone-beam CT with deep learning neural architecture search.

European journal of radiology Jul 27, 2021
PURPOSE: To utilize a neural architecture search (NAS) approach to develop a convolutional neural network (CNN) method for distinguishing benign and malignant lesions on breast cone-beam CT (BCBCT).
Deep Learning Cone-Beam Computed Tomography Humans Neural Networks, Computer Radiologists Breast
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Clinically applicable artificial intelligence system for dental diagnosis with CBCT.

Scientific reports Jul 22, 2021
In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinica...
Stomatognathic Diseases Disease Management Observer Variation Cone-Beam Computed Tomography Image Processing, Computer-Assisted Artificial Intelligence Sensitivity and Specificity Humans
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Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction.

Physics in medicine and biology Jul 1, 2021
High cone-angle artifacts (HCAAs) appear frequently in circular cone-beam computed tomography (CBCT) images and can heavily affect diagnosis and treatment planning. To reduce HCAAs in CBCT scans, we propose a novel deep learning approach that reduces...
Cone-Beam Computed Tomography Image Processing, Computer-Assisted Artifacts Neural Networks, Computer Deep Learning
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Synthetic CT generation from CBCT images via unsupervised deep learning.

Physics in medicine and biology May 31, 2021
Adaptive-radiation-therapy (ART) is applied to account for anatomical variations observed over the treatment course. Daily or weekly cone-beam computed tomography (CBCT) is commonly used in clinic for patient positioning, but CBCT's inaccuracy in Hou...
Radiotherapy Planning, Computer-Assisted Spiral Cone-Beam Computed Tomography Deep Learning Humans Cone-Beam Computed Tomography
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Automatic segmentation of the pharyngeal airway space with convolutional neural network.

Journal of dentistry May 30, 2021
OBJECTIVES: This study proposed and investigated the performance of a deep learning based three-dimensional (3D) convolutional neural network (CNN) model for automatic segmentation of the pharyngeal airway space (PAS).
Cone-Beam Computed Tomography Databases, Factual Humans Image Processing, Computer-Assisted Finland Neural Networks, Computer Tomography, X-Ray Computed
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Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation.

Medical physics May 25, 2021
PURPOSE: Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reli...
Lung Neoplasms Cone-Beam Computed Tomography Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Deep Learning
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