Journal of X-ray science and technology
Jan 1, 2022
OBJECTIVE: To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V).
Geophysical inversion is often ill-posed because of its nonlinearity and the ordinary measured data of measured data. To deal with these problems, an artificial neural network (ANN) has been introduced with the capability of a nonlinear and complex p...
OBJECTIVE: Pedicle screw insertion for stabilization after lumbar fusion surgery is commonly performed by spine surgeons. With the advent of navigation technology, the accuracy of pedicle screw insertion has increased. Robotic guidance has revolution...
OBJECTIVE: The aim of the study was to verify the ability of the deep learning model to identify five subtypes and normal images in non-contrast enhancement CT of intracranial hemorrhage.
Journal of X-ray science and technology
Jan 1, 2022
Presence of plaque and coronary artery stenosis are the main causes of coronary heart disease. Detection of plaque and coronary artery segmentation have become the first choice in detecting coronary artery disease. The purpose of this study is to inv...
AIMS: To evaluate an automated ASPECTS (ASPECTS-a) software against two radiologists' reading of CT scans requested from the Emergency Department. Describe the most frequent failures of the ASPECTS-a.
OBJECTIVE: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcrip...
Artificial intelligence is an important technology, with rapidly expanding applications for cardiac PET. We review the common terminology, including methods for training and testing, which are fundamental to understanding artificial intelligence. Nex...
Journal of X-ray science and technology
Jan 1, 2022
BACKGROUND: The fusion of computer tomography and deep learning is an effective way of achieving improved image quality and artifact reduction in reconstructed images.
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