AIMC Topic: Cone-Beam Computed Tomography

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Neural networks-based regularization for large-scale medical image reconstruction.

Physics in medicine and biology
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs ha...

Deep Learning model for markerless tracking in spinal SBRT.

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)
Stereotactic Body Radiation Therapy (SBRT), alternatively termed Stereotactic ABlative Radiotherapy (SABR) or Stereotactic RadioSurgery (SRS), delivers high dose with a sub-millimeter accuracy. It requires meticulous precautions on positioning, as sh...

Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images.

Journal of endodontics
INTRODUCTION: The aim of this study was to use a Deep Learning (DL) algorithm for the automated segmentation of cone-beam computed tomographic (CBCT) images and the detection of periapical lesions.

Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy.

Physics in medicine and biology
In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation d...

Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes.

Scientific reports
Accurate localisation of mandibular canals in lower jaws is important in dental implantology, in which the implant position and dimensions are currently determined manually from 3D CT images by medical experts to avoid damaging the mandibular nerve i...

Automatic mandibular canal detection using a deep convolutional neural network.

Scientific reports
The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques fo...

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network.

Medical physics
PURPOSE: To design a convolutional recurrent neural network (CRNN) that calculates three-dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruc...

Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy.

Physics in medicine and biology
To improve image quality and CT number accuracy of fast-scan low-dose cone-beam computed tomography (CBCT) through a deep-learning convolutional neural network (CNN) methodology for head-and-neck (HN) radiotherapy. Fifty-five paired CBCT and CT image...

Synthetic CT generation from CBCT images via deep learning.

Medical physics
PURPOSE: Cone-beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on-treatment CBCT) for accurate patient setup in image-guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as d...

Visual enhancement of Cone-beam CT by use of CycleGAN.

Medical physics
PURPOSE: Cone-beam computed tomography (CBCT) offers advantages over conventional fan-beam CT in that it requires a shorter time and less exposure to obtain images. However, CBCT images suffer from low soft-tissue contrast, noise, and artifacts compa...