AIMC Topic: Signal-To-Noise Ratio

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Using Deep Learning to Emulate the Use of an External Contrast Agent in Cardiovascular 4D Flow MRI.

Journal of magnetic resonance imaging : JMRI Feb 25, 2021
BACKGROUND: Although contrast agents would be beneficial, they are seldom used in four-dimensional (4D) flow magnetic resonance imaging (MRI) due to potential side effects and contraindications.
Retrospective Studies Deep Learning Humans Magnetic Resonance Imaging Signal-To-Noise Ratio Contrast Media
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Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network.

European radiology Feb 25, 2021
OBJECTIVES: To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks.
Tomography, X-Ray Computed Animals Humans Contrast Media Deep Learning Signal-To-Noise Ratio Image Processing, Computer-Assisted Drug Tapering
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Low-dose PET image noise reduction using deep learning: application to cardiac viability FDG imaging in patients with ischemic heart disease.

Physics in medicine and biology Feb 25, 2021
INTRODUCTION: Cardiac [F]FDG-PET is widely used for viability testing in patients with chronic ischemic heart disease. Guidelines recommend injection of 200-350 MBq [F]FDG, however, a reduction of radiation exposure has become increasingly important,...
Middle Aged Deep Learning Image Processing, Computer-Assisted Male Stroke Volume Signal-To-Noise Ratio Myocardial Ischemia Tissue Survival Fluorodeoxyglucose F18 Radiation Dosage Positron-Emission Tomography Humans Adult
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Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism.

PloS one Feb 25, 2021
Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heter...
Magnetic Resonance Imaging Neuroimaging Normal Distribution Signal-To-Noise Ratio Deep Learning Humans Dyslexia Biomarkers Image Processing, Computer-Assisted Databases, Factual
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Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection.

The British journal of radiology Feb 22, 2021
OBJECTIVES: To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR).
Tomography, X-Ray Computed Female Aged Algorithms Prospective Studies Male Middle Aged Neoplasms Aged, 80 and over Adult Humans Signal-To-Noise Ratio Postoperative Period Whole Body Imaging Radiographic Image Interpretation, Computer-Assisted Quality Control Deep Learning Radiation Dosage Contrast Media
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Image synthesis of monoenergetic CT image in dual-energy CT using kilovoltage CT with deep convolutional generative adversarial networks.

Journal of applied clinical medical physics Feb 18, 2021
PURPOSE: To synthesize a dual-energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV-CT) image using a deep convolutional adversarial network.
Tomography, X-Ray Computed Image Processing, Computer-Assisted Signal-To-Noise Ratio Humans Neural Networks, Computer
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Recurrent neural network with noise rejection for cyclic motion generation of robotic manipulators.

Neural networks : the official journal of the International Neural Network Society Feb 17, 2021
Recurrent neural network (RNN), as a kind of neural network with outstanding computing capability, improvability, and hardware realizability, has been widely used in various fields, especially in robotics. In this paper, an RNN with noise rejection i...
Robotics Motion Signal-To-Noise Ratio Neural Networks, Computer Reproducibility of Results
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Unsupervised anomaly detection using generative adversarial networks in H-MRS of the brain.

Journal of magnetic resonance (San Diego, Calif. : 1997) Feb 13, 2021
The applicability of generative adversarial networks (GANs) capable of unsupervised anomaly detection (AnoGAN) was investigated in the management of quality of H-MRS human brain spectra at 3.0 T. The AnoGAN was trained in an unsupervised manner solel...
Brain Chemistry Proton Magnetic Resonance Spectroscopy Deep Learning Artifacts Humans Signal-To-Noise Ratio Computer Simulation
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DeepHCS: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening.

Medical image analysis Feb 12, 2021
In this paper, we propose a novel microscopy image translation method for transforming a bright-field microscopy image into three different fluorescence images to observe the apoptosis, nuclei, and cytoplasm of cells, which visualize dead cells, nucl...
Image Processing, Computer-Assisted Microscopy, Fluorescence Signal-To-Noise Ratio Humans Neural Networks, Computer
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Introducing Swish and Parallelized Blind Removal Improves the Performance of a Convolutional Neural Network in Denoising MR Images.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine Feb 11, 2021
PURPOSE: To improve the performance of a denoising convolutional neural network (DnCNN) and to make it applicable to images with inhomogeneous noise, a refinement involving an activation function (AF) and an application of the refined method for inho...
Neural Networks, Computer Image Processing, Computer-Assisted Signal-To-Noise Ratio Noise
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