AI Medical Compendium Topic:
Signal-To-Noise Ratio

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Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND AND OBJECTIVE: Cardiac magnetic resonance imaging (MRI) can assist in both functional and structural analysis of the heart, but due to hardware and physical limitations, high-resolution MRI scans is time consuming and peak signal-to-noise ...

Automatic multi-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks.

Medical physics
PURPOSE: Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissue...

Breast tumor classification through learning from noisy labeled ultrasound images.

Medical physics
PURPOSE: To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samples with clear labels. In general, biopsy results can be used as benign/malignant labels. However, m...

Synthesizing images from multiple kernels using a deep convolutional neural network.

Medical physics
PURPOSE: Filtering measured projections with a particular convolutional kernel is an essential step in analytic reconstruction of computed tomography (CT) images. A tradeoff between noise and spatial resolution exists for different choices of reconst...

Deep Convolutional Neural Network-Based Automatic Classification of Neonatal Hip Ultrasound Images: A Novel Data Augmentation Approach with Speckle Noise Reduction.

Ultrasound in medicine & biology
Neonatal hip ultrasound imaging has been widely used for a few decades in the diagnosis of developmental dysplasia of the hip (DDH). Graf's method of hip ultrasonography is still the most reproducible because of its classification system; yet, the re...

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning.

NMR in biomedicine
Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. Howev...

DeepCQ: Deep multi-task conditional quantification network for estimation of left ventricle parameters.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic cardiac left ventricle (LV) quantification plays an important role in assessing cardiac function. Although many advanced methods have been put forward to quantify related LV parameters, automatic cardiac LV quantif...

SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression Extension in PPG Signal Denoising.

Computational and mathematical methods in medicine
Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and ...

Dilated Residual Learning With Skip Connections for Real-Time Denoising of Laser Speckle Imaging of Blood Flow in a Log-Transformed Domain.

IEEE transactions on medical imaging
Laser speckle contrast imaging (LSCI) is a wide-field and noncontact imaging technology for mapping blood flow. Although the denoising method based on block-matching and three-dimensional transform-domain collaborative filtering (BM3D) was proposed t...