AIMC Topic: Signal-To-Noise Ratio
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Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study.
Journal of computer assisted tomography
Nov 5, 2024
OBJECTIVE: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volumetry (CAD v ) software for filtered back projection (FBP), hybrid-type iterative reconstruction (IR...
Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction.
Clinical imaging
Nov 5, 2024
PURPOSE: To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.
Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.
Journal of neural engineering
Nov 5, 2024
The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoisin...
A unified noise and watermark removal from information bottleneck-based modeling.
Neural networks : the official journal of the International Neural Network Society
Nov 4, 2024
Both image denoising and watermark removal aim to restore a clean image from an observed noisy or watermarked one. The past research consists of the non-learning type with limited effectiveness or the learning types with limited interpretability. To ...
An energy-aware protocol in wireless sensor networks using the scattered search algorithm and fuzzy logic.
PloS one
Nov 4, 2024
Given the resource limitations of wireless sensor networks (WSNs), energy conservation is of utmost importance. Moreover, minimizing data collection delays is crucial to maintaining data freshness. Additionally, it is desirable to increase the number...
Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network.
Sensors (Basel, Switzerland)
Oct 31, 2024
The classification of ECG signals is a critical process because it guides the diagnosis of the proper treatment process for the patient. However, any form of disturbance with ECG signals can be highly conspicuous because of the mechanics involved in ...
Exploring the feasibility of FOCUS DWI with deep learning reconstruction for breast cancer diagnosis: A comparative study with conventional DWI.
PloS one
Oct 31, 2024
PURPOSE: This study compared field-of-view (FOV) optimized and constrained undistorted single-shot diffusion-weighted imaging (FOCUS DWI) with deep-learning-based reconstruction (DLR) to conventional DWI for breast imaging.
Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.
IEEE transactions on neural networks and learning systems
Oct 29, 2024
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challengi...
Impact of deep Learning-enhanced contrast on diagnostic accuracy in stroke CT angiography.
European journal of radiology
Oct 28, 2024
PURPOSE: To examine the impact of deep learning-augmented contrast enhancement on image quality and diagnostic accuracy of poorly contrasted CT angiography in patients with suspected stroke.
Signal-To-Noise Ratio
Computed Tomography Angiography
Cerebral Angiography
Radiographic Image Enhancement
Contrast Media
Radiographic Image Interpretation, Computer-Assisted
Stroke
Female
Male
Reproducibility of Results
Middle Aged
Aged
Deep Learning
Sensitivity and Specificity
Humans
Retrospective Studies
Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.
European radiology
Oct 23, 2024
OBJECTIVES: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.
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