AIMC Topic: Signal-To-Noise Ratio

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The heart sound classification of congenital heart disease by using median EEMD-Hurst and threshold denoising method.

Medical & biological engineering & computing Aug 5, 2024
Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limited by noise during heart sound acquisition. A limitation of existing noise reduction schemes is that ...
Signal-To-Noise Ratio Heart Defects, Congenital Heart Sounds Neural Networks, Computer Algorithms Signal Processing, Computer-Assisted Humans
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Deep learning reconstruction for optimized bone assessment in zero echo time MR imaging of the knee.

European journal of radiology Aug 4, 2024
PURPOSE: To evaluate the impact of deep learning-based reconstruction (DLRecon) on bone assessment in zero echo-time (ZTE) MRI of the knee at 1.5 Tesla.
Humans Retrospective Studies Female Deep Learning Magnetic Resonance Imaging Knee Joint Signal-To-Noise Ratio Image Interpretation, Computer-Assisted Adult Artifacts Male Aged Middle Aged Aged, 80 and over Young Adult
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Intelligent skin-removal photoacoustic computed tomography for human based on deep learning.

Journal of biophotonics Aug 2, 2024
Photoacoustic computed tomography (PACT) has centimeter-level imaging ability and can be used to detect the human body. However, strong photoacoustic signals from skin cover deep tissue information, hindering the frontal display and analysis of photo...
Image Processing, Computer-Assisted Skin Humans Signal-To-Noise Ratio Tomography, X-Ray Computed Deep Learning Photoacoustic Techniques
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Improving human activity classification based on micro-doppler signatures of FMCW radar with the effect of noise.

PloS one Aug 1, 2024
Nowadays, classifying human activities is applied in many essential fields, such as healthcare, security monitoring, and search and rescue missions. Radar sensor-based human activity classification is regarded as a superior approach in comparison to ...
Humans Doppler Effect Neural Networks, Computer Algorithms Signal-To-Noise Ratio Human Activities Radar
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AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery.

Journal of imaging informatics in medicine Jul 30, 2024
Geometric distortions in brain MRI images arising from susceptibility artifacts at air-tissue interfaces pose a significant challenge for high-precision radiation therapy modalities like stereotactic radiosurgery, necessitating sub-millimeter accurac...
Neuroma, Acoustic Humans Artifacts Magnetic Resonance Imaging Brain Deep Learning Unsupervised Machine Learning Signal-To-Noise Ratio Image Processing, Computer-Assisted Radiosurgery
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Continual medical image denoising based on triplet neural networks collaboration.

Computers in biology and medicine Jul 24, 2024
BACKGROUND: When multiple tasks are learned consecutively, the old model parameters may be overwritten by the new data, resulting in the phenomenon that the new task is learned and the old task is forgotten, which leads to catastrophic forgetting. Mo...
Humans Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Signal-To-Noise Ratio
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Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression.

Investigative radiology Jul 23, 2024
OBJECTIVE: Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerate...
Magnetic Resonance Imaging Pancreatic Diseases Humans Image Interpretation, Computer-Assisted Prospective Studies Signal-To-Noise Ratio Female Middle Aged Aged, 80 and over Aged Breath Holding Deep Learning Artifacts Adult Pancreas Male Image Processing, Computer-Assisted
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Improved quantitative parameter estimation for prostate T relaxometry using convolutional neural networks.

Magma (New York, N.Y.) Jul 23, 2024
OBJECTIVE: Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) f...
Magnetic Resonance Imaging Reproducibility of Results Prostatic Neoplasms Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Male Humans Prostate Image Processing, Computer-Assisted Neural Networks, Computer Algorithms
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Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity.

Acta radiologica (Stockholm, Sweden : 1987) Jul 21, 2024
BACKGROUND: The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined.
Radiography, Dual-Energy Scanned Projection Male Middle Aged Deep Learning Signal-To-Noise Ratio Radiography, Abdominal Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Aged Aged, 80 and over Adult Humans Retrospective Studies Algorithms Tomography, X-Ray Computed Female
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Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder.

Magnetic resonance in medicine Jul 19, 2024
PURPOSE: To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state-of-the-art denoising methods.
Deep Learning Image Processing, Computer-Assisted Mice Algorithms Animals Phantoms, Imaging Magnetic Resonance Imaging Principal Component Analysis Signal-To-Noise Ratio
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