AIMC Topic: Cone-Beam Computed Tomography

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Deep learning framework to improve the quality of cone-beam computed tomography for radiotherapy scenarios.

Medical physics
BACKGROUND: The application of cone-beam computed tomography (CBCT) in image-guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality.

A deep learning-based automatic segmentation of zygomatic bones from cone-beam computed tomography images: A proof of concept.

Journal of dentistry
OBJECTIVES: To investigate the efficiency and accuracy of a deep learning-based automatic segmentation method for zygomatic bones from cone-beam computed tomography (CBCT) images.

Accuracy and safety of a haptic operated and machine vision controlled collaborative robot for dental implant placement: A translational study.

Clinical oral implants research
OBJECTIVES: Multiple generations of medical robots have revolutionized surgery. Their application to dental implants is still in its infancy. Co-operating robots (cobots) have great potential to improve the accuracy of implant placement, overcoming t...

Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

Journal of applied clinical medical physics
PURPOSE: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients.

Deep learning-based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks.

Medical physics
BACKGROUND: Radiotherapy (RT) is involved in about 50% of all cancer patients, making it a very important treatment modality. The most common type of RT is external beam RT, which consists of delivering the radiation to the tumor from outside the bod...

Artificial intelligence: is it more accurate than endodontists in root canal therapy?

Evidence-based dentistry
DATA SOURCES: The following databases were electronically searched (up to 20 March 2022): PubMed, Scopus, Google Scholar, and Cochrane Library. This was followed by hand-searching the reference lists of the included articles. The search was restricte...

Deep learning based de-overlapping correction of projections from a flat-panel micro array X-ray source: Simulation study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Flat-panel X-ray source is an experimental X-ray emitter with target application of static computer tomography (CT), which can save imaging space and time. However, the X-ray cone beams emitted by the densely arranged micro-ray sources are o...

Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT.

European radiology
OBJECTIVES: To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans.

Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy.

Physics in medicine and biology
. Adaptive radiotherapy workflows require images with the quality of computed tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve the quality of on-board cone beam CT (CBCT) images for dose calcul...

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning.

IEEE transactions on medical imaging
A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before bein...