AIMC Topic: Signal-To-Noise Ratio

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Ultrasonic image denoising using machine learning in point contact excitation and detection method.

Ultrasonics
A point contact/Coulomb coupling technique is generally used for visualizing the ultrasonic waves in Lead Zirconate Titanate (PZT) ceramics. The point contact and delta pulse excitation produce a broadband frequency spectrum and wide directional wave...

Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning.

Nature communications
Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applie...

A Joint Automatic Modulation Classification Scheme in Spatial Cognitive Communication.

Sensors (Basel, Switzerland)
Automatic modulation discrimination (AMC) is one of the critical technologies in spatial cognitive communication systems. Building a high-performance AMC model in intelligent receivers can help to realize adaptive signal synchronization and demodulat...

Improving Image Quality and Reducing Scan Time for Synthetic MRI of Breast by Using Deep Learning Reconstruction.

BioMed research international
OBJECTIVES: To investigate a deep learning reconstruction algorithm to reduce the time of synthetic MRI (SynMRI) scanning on the breast and improve the image quality.

A Deep Q-Network-Based Algorithm for Multi-Connectivity Optimization in Heterogeneous Cellular-Networks.

Sensors (Basel, Switzerland)
The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires ...

Virtual high-count PET image generation using a deep learning method.

Medical physics
PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan tim...

Masked Joint Bilateral Filtering via Deep Image Prior for Digital X-Ray Image Denoising.

IEEE journal of biomedical and health informatics
Medical image denoising faces great challenges. Although deep learning methods have shown great potential, their efficiency is severely affected by millions of trainable parameters. The non-linearity of neural networks also makes them difficult to be...

Learning low-dose CT degradation from unpaired data with flow-based model.

Medical physics
BACKGROUND: There has been growing interest in low-dose computed tomography (LDCT) for reducing the X-ray radiation to patients. However, LDCT always suffers from complex noise in reconstructed images. Although deep learning-based methods have shown ...

MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging.

Magnetic resonance imaging
PURPOSE: To compare capabilities of compressed sensing (CS) with and without deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) with and without DLR for improving examination time and image quality of shoulder MRI for...