AIMC Topic: Cone-Beam Computed Tomography
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Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain.
Medical physics
Mar 21, 2023
BACKGROUND: Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (...
A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Mar 8, 2023
OBJECTIVE: Preoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time-consuming. Automated methods for segmenting multiple geometrically complex structures ca...
Deep learning for automated segmentation of the temporomandibular joint.
Journal of dentistry
Mar 2, 2023
OBJECTIVE: Quantitative analysis of the volume and shape of the temporomandibular joint (TMJ) using cone-beam computed tomography (CBCT) requires accurate segmentation of the mandibular condyles and the glenoid fossae. This study aimed to develop and...
Enhancing Nodule Biopsy Through Technology Integration.
Innovations (Philadelphia, Pa.)
Feb 18, 2023
Technology in navigating to peripheral pulmonary nodules has improved in recent years. The recent integration of a robotic platform using shape-sensing technology and mobile cone-beam computed tomography imaging technology has enhanced confidence in ...
Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.
Medical physics
Feb 14, 2023
BACKGROUND: Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, h...
Dental innovations which will influence the oral health care of baby boomers.
Special care in dentistry : official publication of the American Association of Hospital Dentists, the Academy of Dentistry for the Handicapped, and the American Society for Geriatric Dentistry
Feb 13, 2023
From the widespread use of smartphones and tablets to the multitude of applications available, older adults are showing an interest in utilizing technology to maintain their independence and to improve their quality of life. As technology continues t...
Deep learning based direct segmentation assisted by deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy.
Physics in medicine and biology
Feb 10, 2023
Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians. However, deep learning (DL)-based direct segmentation of CBCT images is a challenging task, mainly due to the poor ima...
An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.
Journal of digital imaging
Jan 30, 2023
The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning mod...
Deep learning for x-ray scatter correction in dedicated breast CT.
Medical physics
Jan 7, 2023
BACKGROUND: Accurate correction of x-ray scatter in dedicated breast computed tomography (bCT) imaging may result in improved visual interpretation and is crucial to achieve quantitative accuracy during image reconstruction and analysis.
Deep Learning for Detection of Periapical Radiolucent Lesions: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy.
Journal of endodontics
Dec 21, 2022
INTRODUCTION: The aim of this systematic review and meta-analysis was to investigate the overall accuracy of deep learning models in detecting periapical (PA) radiolucent lesions in dental radiographs, when compared to expert clinicians.
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