AIMC Topic: Cone-Beam Computed Tomography

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Development and Validation of a Visually Explainable Deep Learning Model for Classification of C-shaped Canals of the Mandibular Second Molars in Periapical and Panoramic Dental Radiographs.

Journal of endodontics
INTRODUCTION: The purpose of this study was to develop and validate a visually explainable deep learning model for the classification of C-shaped canals of the mandibular second molars in dental radiographs.

AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography.

Tomography (Ann Arbor, Mich.)
(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed in...

Patient-specific deep learning model to enhance 4D-CBCT image for radiomics analysis.

Physics in medicine and biology
4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic feat...

Dosimetric assessment of patient dose calculation on a deep learning-based synthesized computed tomography image for adaptive radiotherapy.

Journal of applied clinical medical physics
PURPOSE: Dose computation using cone beam computed tomography (CBCT) images is inaccurate for the purpose of adaptive treatment planning. The main goal of this study is to assess the dosimetric accuracy of synthetic computed tomography (CT)-based cal...

Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy.

Frontiers in public health
PURPOSE: The purpose of this study was to evaluate the accuracy of a lung stereotactic body radiotherapy (SBRT) treatment plan with the target of a newly predicted internal target volume (ITV) and the feasibility of its clinical application. ITV was ...

Construction of an end-to-end regression neural network for the determination of a quantitative index sagittal root inclination.

Journal of periodontology
BACKGROUND: Immediate implant placement in the esthetic area requires comprehensive assessments with nearly 30 quantitative indexes. Most artificial intelligence (AI)-driven measurements of quantitative indexes depend on segmentation or landmark dete...

Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network.

Medical physics
PURPOSE: Image guidance is used to improve the accuracy of radiation therapy delivery but results in increased dose to patients. This is of particular concern in children who need be treated per Pediatric Image Gently Protocols due to long-term risks...

Updates in Endovascular Procedural Navigation.

The Canadian journal of cardiology
There have been significant advancements in endovascular technology over the past decade. Increasingly complex disease processes are being addressed in a less invasive fashion, while still relying on standard 2-dimensional greyscale fluoroscopy imagi...

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography.

Scientific reports
Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were t...

Virtual monoenergetic micro-CT imaging in mice with artificial intelligence.

Scientific reports
Micro cone-beam computed tomography (µCBCT) imaging is of utmost importance for carrying out extensive preclinical research in rodents. The imaging of animals is an essential step prior to preclinical precision irradiation, but also in the longitudin...