AIMC Topic: Cone-Beam Computed Tomography
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Multiresolution residual deep neural network for improving pelvic CBCT image quality.
Medical physics
Jan 27, 2022
PURPOSE: Cone-beam computed tomography (CBCT) is frequently used for accurate image-guided radiation therapy. However, the poor CBCT image quality prevents its further clinical use. Thus, it is important to improve the HU accuracy and structure prese...
Artificial Intelligence-Based Assistance System for Visual Inspection of X-ray Scatter Grids.
Sensors (Basel, Switzerland)
Jan 21, 2022
Convolutional neural network (CNN)-based approaches have recently led to major performance steps in visual recognition tasks. However, only a few industrial applications are described in the literature. In this paper, an object detection application ...
Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network.
Clinical oral investigations
Jan 15, 2022
OBJECTIVES: To propose and evaluate a convolutional neural network (CNN) algorithm for automatic detection and segmentation of mucosal thickening (MT) and mucosal retention cysts (MRCs) in the maxillary sinus on low-dose and full-dose cone-beam compu...
Development of a multi-stage model for intelligent and quantitative appraising of skeletal maturity using cervical vertebras cone-beam CT images of Chinese girls.
International journal of computer assisted radiology and surgery
Jan 4, 2022
PURPOSE: Nowadays, the integration of Artificial intelligence algorithms and quantified radiographic imaging-based diagnostic procedures is hailing amplified deliberation particularly in assessment of skeletal maturity. So we intend to formulate a lo...
The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review.
International journal of environmental research and public health
Jan 4, 2022
This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well as to present their diagnostic accuracy. Articles related to inferior alveolar nerve/canal localizati...
Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach.
Journal of vascular and interventional radiology : JVIR
Dec 16, 2021
PURPOSE: To show that a deep learning (DL)-based, automated model for Lipiodol (Guerbet Pharmaceuticals, Paris, France) segmentation on cone-beam computed tomography (CT) after conventional transarterial chemoembolization performs closer to the "grou...
Multi-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model.
Dento maxillo facial radiology
Nov 23, 2021
OBJECTIVES: Ultrasound emerges as a complement to cone-beam computed tomography in dentistry, but struggles with artifacts like reverberation and shadowing. This study seeks to help novice users recognize soft tissue, bone, and crown of a dental sono...
Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio).
Medical physics
Nov 19, 2021
PURPOSE: Recently, two-dimensional-to-three-dimensional (2D-3D) deformable registration has been applied to deform liver tumor contours from prior reference images onto estimated cone-beam computed tomography (CBCT) target images to automate on-board...
Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer.
Medical physics
Nov 16, 2021
PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation...
Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT.
Journal of dentistry
Nov 13, 2021
OBJECTIVES: The objective of this study is the development and validation of a novel artificial intelligence driven tool for fast and accurate mandibular canal segmentation on cone beam computed tomography (CBCT).
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