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Spiral Cone-Beam Computed Tomography

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Performance of artificial intelligence using oral and maxillofacial CBCT images: A systematic review and meta-analysis.

Nigerian journal of clinical practice
BACKGROUND: Artificial intelligence (AI) has the potential to enhance health care efficiency and diagnostic accuracy.

Clinical feasibility of deep learning-based automatic head CBCT image segmentation and landmark detection in computer-aided surgical simulation for orthognathic surgery.

International journal of oral and maxillofacial surgery
The purpose of this ambispective study was to investigate whether deep learning-based automatic segmentation and landmark detection, the SkullEngine, could be used for orthognathic surgical planning. Sixty-one sets of cone beam computed tomography (C...

Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans.

Scientific reports
Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce. To validate the mandibular canal localization accuracy ...

Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction.

Medical physics
BACKGROUND: Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBC...

Evaluation of generalization ability for deep learning-based auto-segmentation accuracy in limited field of view CBCT of male pelvic region.

Journal of applied clinical medical physics
PURPOSE: The aim of this study was to evaluate generalization ability of segmentation accuracy for limited FOV CBCT in the male pelvic region using a full-image CNN. Auto-segmentation accuracy was evaluated using various datasets with different inten...

Synthetic CT generation from CBCT using double-chain-CycleGAN.

Computers in biology and medicine
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...

Inter-fraction deformable image registration using unsupervised deep learning for CBCT-guided abdominal radiotherapy.

Physics in medicine and biology
. 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...

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning.

Scientific reports
The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to v...

A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration.

Physics in medicine and biology
. This study aimed to develop a novel method for generating synthetic CT (sCT) from cone-beam CT (CBCT) of the abdomen/pelvis with bowel gas pockets to facilitate estimation of proton ranges.. CBCT, the same-day repeat CT, and the planning CT (pCT) o...

Generation of synthetic CT from CBCT using deep learning approaches for head and neck cancer patients.

Biomedical physics & engineering express
To create a synthetic CT (sCT) from daily CBCT using either deep residual U-Net (DRUnet), or conditional generative adversarial network (cGAN) for adaptive radiotherapy planning (ART).First fraction CBCT and planning CT (pCT) were collected from 93 H...