AIMC Topic: Signal-To-Noise Ratio

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A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models.

Computational intelligence and neuroscience
In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, C...

k-Space-based coil combination via geometric deep learning for reconstruction of non-Cartesian MRSI data.

Magnetic resonance in medicine
PURPOSE: State-of-the-art whole-brain MRSI with spatial-spectral encoding and multichannel acquisition generates huge amounts of data, which must be efficiently processed to stay within reasonable reconstruction times. Although coil combination signi...

Enhancing the X-Ray Differential Phase Contrast Image Quality With Deep Learning Technique.

IEEE transactions on bio-medical engineering
OBJECTIVE: The purpose of this work is to investigate the feasibility of using deep convolutional neural network (CNN) to improve the image quality of a grating-based X-ray differential phase contrast imaging (XPCI) system.

EMDS-5: Environmental Microorganism image dataset Fifth Version for multiple image analysis tasks.

PloS one
Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and...

Feasibility of high-resolution magnetic resonance imaging of the liver using deep learning reconstruction based on the deep learning denoising technique.

Magnetic resonance imaging
PURPOSE: To evaluate the feasibility of High-resolution (HR) magnetic resonance imaging (MRI) of the liver using deep learning reconstruction (DLR) based on a deep learning denoising technique compared with standard-resolution (SR) imaging.

Improving phase-based conductivity reconstruction by means of deep learning-based denoising of phase data for 3T MRI.

Magnetic resonance in medicine
PURPOSE: To denoise phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system.

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction.

Computational intelligence and neuroscience
Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive perform...

Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography.

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: To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (SECT) images for improved pancreatic cancer imaging quality.

ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Computational and mathematical methods in medicine
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique r...

A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI.

Magnetic resonance in medicine
PURPOSE: With rising safety concerns over the use of gadolinium-based contrast agents (GBCAs) in contrast-enhanced MRI, there is a need for dose reduction while maintaining diagnostic capability. This work proposes comprehensive technical solutions f...