AIMC Topic: Signal-To-Noise Ratio

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Use of deep learning-accelerated T2 TSE for prostate MRI: Comparison with and without hyoscine butylbromide admission.

Magnetic resonance imaging Feb 10, 2025
OBJECTIVE: To investigate the use of deep learning (DL) T2-weighted turbo spin echo (TSE) imaging sequence with deep learning acceleration (T2DL) in prostate MRI regarding the necessity of hyoscine butylbromide (HBB) administration for high image qua...
Butylscopolammonium Bromide Humans Middle Aged Deep Learning Aged Prostate Magnetic Resonance Imaging Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Aged, 80 and over Prostatic Neoplasms Signal-To-Noise Ratio Male
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Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP.

Scientific reports Feb 9, 2025
To accelerate the clinical adoption of quantitative magnetic resonance imaging (qMRI), frameworks are needed that not only allow for rapid acquisition, but also flexibility, cost efficiency, and high accuracy in parameter mapping. In this study, feed...
Monte Carlo Method Computer Simulation Humans Magnetic Resonance Imaging Deep Learning Neural Networks, Computer Cost-Benefit Analysis Image Processing, Computer-Assisted Brain Signal-To-Noise Ratio
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Diffused Multi-scale Generative Adversarial Network for low-dose PET images reconstruction.

Biomedical engineering online Feb 9, 2025
PURPOSE: The aim of this study is to convert low-dose PET (L-PET) images to full-dose PET (F-PET) images based on our Diffused Multi-scale Generative Adversarial Network (DMGAN) to offer a potential balance between reducing radiation exposure and mai...
Radiation Dosage Signal-To-Noise Ratio Generative Adversarial Networks Neural Networks, Computer Image Processing, Computer-Assisted Positron-Emission Tomography Humans
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Using deep feature distances for evaluating the perceptual quality of MR image reconstructions.

Magnetic resonance in medicine Feb 8, 2025
PURPOSE: Commonly used MR image quality (IQ) metrics have poor concordance with radiologist-perceived diagnostic IQ. Here, we develop and explore deep feature distances (DFDs)-distances computed in a lower-dimensional feature space encoded by a convo...
Humans Neural Networks, Computer Algorithms Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Brain Deep Learning Image Processing, Computer-Assisted
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DeepReducer: A linear transformer-based model for MEG denoising.

NeuroImage Feb 8, 2025
Measuring event-related magnetic fields (ERFs) in magnetoencephalography (MEG) is crucial for investigating perceptual and cognitive information processing in both neuroscience research and clinical practice. However, the magnitude of the ERF in cort...
Humans Brain Deep Learning Signal Processing, Computer-Assisted Artifacts Signal-To-Noise Ratio Magnetoencephalography
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Fast In Vivo Two-Photon Fluorescence Imaging via Lateral and Axial Resolution Restoration With Self-Supervised Learning.

Journal of biophotonics Feb 5, 2025
Two-photon fluorescence (TPF) imaging opens a new avenue to achieve high resolution at extended penetration depths. However, it is difficult for conventional TPF imaging systems to simultaneously achieve high resolution and speed. In this work, we de...
Animals Deep Learning Time Factors Microscopy, Fluorescence, Multiphoton Supervised Machine Learning Image Processing, Computer-Assisted Signal-To-Noise Ratio Mice Optical Imaging
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Quantification of tissue stiffness with magnetic resonance elastography and finite difference time domain (FDTD) simulation-based spatiotemporal neural network.

Magnetic resonance imaging Feb 4, 2025
Quantification of tissue stiffness with magnetic resonance elastography (MRE) is an inverse problem that is sensitive to noise. Conventional methods for the purpose include direct inversion (DI) and local frequency estimation (LFE). In this study, we...
Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Computer Simulation Reproducibility of Results Phantoms, Imaging Elastic Modulus Elasticity Imaging Techniques Humans Neural Networks, Computer Algorithms
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FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.

Journal of neural engineering Feb 4, 2025
Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel metho...
Attention Artifacts Humans Signal-To-Noise Ratio Neural Networks, Computer Deep Learning Electroencephalography Signal Processing, Computer-Assisted
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Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jan 30, 2025
Magnetic Resonance Imaging (MRI) generates medical images of multiple sequences, i.e., multimodal, from different contrasts. However, noise will reduce the quality of MR images, and then affect the doctor's diagnosis of diseases. Existing filtering m...
Magnetic Resonance Imaging Algorithms Humans Signal-To-Noise Ratio Deep Learning Neural Networks, Computer Image Processing, Computer-Assisted
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Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.

Medical physics Jan 30, 2025
BACKGROUND: Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.
Signal-To-Noise Ratio Humans Phantoms, Imaging Image Processing, Computer-Assisted Tomography, X-Ray Computed Liver Deep Learning Liver Neoplasms
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