AIMC Topic: Cone-Beam Computed Tomography

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ScatterNet: A convolutional neural network for cone-beam CT intensity correction.

Medical physics Oct 8, 2018
PURPOSE: To demonstrate a proof-of-concept for fast cone-beam CT (CBCT) intensity correction in projection space by the use of deep learning.
Cone-Beam Computed Tomography Image Processing, Computer-Assisted Radiometry Neural Networks, Computer
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An unsupervised convolutional neural network-based algorithm for deformable image registration.

Physics in medicine and biology Sep 17, 2018
The purpose of the work is to develop a deep unsupervised learning strategy for cone-beam CT (CBCT) to CT deformable image registration (DIR). This technique uses a deep convolutional inverse graphics network (DCIGN) based DIR algorithm implemented o...
Neural Networks, Computer Algorithms Cone-Beam Computed Tomography Humans Image Processing, Computer-Assisted Head and Neck Neoplasms
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H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes.

IEEE transactions on medical imaging Jun 11, 2018
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Rece...
Liver Image Interpretation, Computer-Assisted Humans Deep Learning Algorithms Liver Neoplasms Cone-Beam Computed Tomography
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Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

AJR. American journal of roentgenology May 24, 2018
OBJECTIVE: The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers.
Radiographic Image Interpretation, Computer-Assisted Iohexol Cone-Beam Computed Tomography Contrast Media Predictive Value of Tests Machine Learning Prospective Studies Female Sensitivity and Specificity Breast Neoplasms Middle Aged Humans
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A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society May 1, 2018
OBJECTIVE: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifie...
Humans Adult Male Case-Control Studies Neural Networks, Computer Biomarkers Female Osteoarthritis Temporomandibular Joint Disorders Radiographic Image Interpretation, Computer-Assisted Internet Surveys and Questionnaires Cone-Beam Computed Tomography Imaging, Three-Dimensional
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Cleft Skeletal Asymmetry: Asymmetry Index, Classification and Application.

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association Dec 14, 2017
OBJECTIVE: To quantitatively measure the extent of 3D asymmetry of the facial skeleton in patients with unilateral cleft lip and palate (UCLP) using an asymmetry index (AI) approach, and to illustrate the applicability of the index in guiding and mea...
Facial Asymmetry Orthodontics, Corrective Orthognathic Surgical Procedures Humans Young Adult Cone-Beam Computed Tomography Male Imaging, Three-Dimensional Skull Female Adolescent Anatomic Landmarks Cleft Lip Cleft Palate
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Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

Medical physics Dec 11, 2017
PURPOSE: This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images.
Machine Learning Movement Fluoroscopy Cone-Beam Computed Tomography Four-Dimensional Computed Tomography Image Processing, Computer-Assisted Artifacts
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A robotic C-arm cone beam CT system for image-guided proton therapy: design and performance.

The British journal of radiology Oct 9, 2017
OBJECTIVE: A ceiling-mounted robotic C-arm cone beam CT (CBCT) system was developed for use with a 190° proton gantry system and a 6-degree-of-freedom robotic patient positioner. We report on the mechanical design, system accuracy, image quality, ima...
Phantoms, Imaging Proton Therapy Radiographic Image Enhancement Robotics Equipment Design Reproducibility of Results Radiotherapy, Intensity-Modulated Cone-Beam Computed Tomography Contrast Media
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Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning.

IEEE transactions on medical imaging Oct 5, 2017
Despite the rapid developments of X-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this paper, a sparse constraint...
Female Male Cone-Beam Computed Tomography Image Processing, Computer-Assisted Prostate Head Machine Learning Algorithms Databases, Factual Humans
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Accuracy of video observation and a three-dimensional head tracking system for detecting and quantifying robot-simulated head movements in cone beam computed tomography.

Oral surgery, oral medicine, oral pathology and oral radiology Feb 28, 2017
OBJECTIVES: To assess the accuracy of detecting robot-simulated head movements using video observation (VO) and 3-dimensional head tracking (HT) in a cone beam computed tomography examination setup.
Head Movements Humans Sensitivity and Specificity Cone-Beam Computed Tomography Video Recording Imaging, Three-Dimensional Observation Manikins Robotics Rotation
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