AIMC Topic: Signal-To-Noise Ratio

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Improving the accuracy of single-trial fMRI response estimates using GLMsingle.

eLife
Advances in artificial intelligence have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to thousands of naturalistic visual stim...

Technical note: Phantom-based training framework for convolutional neural network CT noise reduction.

Medical physics
BACKGROUND: Deep artificial neural networks such as convolutional neural networks (CNNs) have been shown to be effective models for reducing noise in CT images while preserving anatomic details. A practical bottleneck for developing CNN-based denoisi...

Personalized synthetic MR imaging with deep learning enhancements.

Magnetic resonance in medicine
PURPOSE: Personalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric maps, from where MR images of that individual at other...

Ultrasound Imaging With a Flexible Probe Based on Element Array Geometry Estimation Using Deep Neural Network.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Conventionally, ultrasound (US) diagnosis is performed using hand-held rigid probes. Such devices are difficult to be used for long-term monitoring because they need to be continuously pressed against the body to remove the air between the probe and ...

Real-time noise cancellation with deep learning.

PloS one
Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so ...

Multimodal medical image fusion algorithm based on pulse coupled neural networks and nonsubsampled contourlet transform.

Medical & biological engineering & computing
Combining two medical images from different modalities is more helpful for using the resulting image in the healthcare field. Medical image fusion means combining two or more images coming from multiple sensors. This technology obtains an output imag...

Video Super-Resolution Method Using Deformable Convolution-Based Alignment Network.

Sensors (Basel, Switzerland)
With the advancement of sensors, image and video processing have developed for use in the visual sensing area. Among them, video super-resolution (VSR) aims to reconstruct high-resolution sequences from low-resolution sequences. To use consecutive co...

Deep-Learning-Based Electrical Noise Removal Enables High Spectral Optoacoustic Contrast in Deep Tissue.

IEEE transactions on medical imaging
Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Previously employed signal processing techniques have proven insufficient to remove the ...

Towards Image Guided Magnetic Resonance Elastography via Active Driver Positioning Robot.

IEEE transactions on bio-medical engineering
UNLABELLED: Magnetic Resonance Elastography (MRE) is a developing imaging technique that enables non-invasive estimation of tissue mechanical properties through the combination of induced mechanical displacements in the tissue and Magnetic Resonance ...

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network.

Sensors (Basel, Switzerland)
Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current eng...