AIMC Topic: Signal-To-Noise Ratio
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Denoising Diffusion Probabilistic Model to Simulate Contrast-enhanced spinal MRI of Spinal Tumors: A Multi-Center Study.
Academic radiology
Feb 28, 2025
RATIONALE AND OBJECTIVES: To generate virtual T1 contrast-enhanced (T1CE) sequences from plain spinal MRI sequences using the denoising diffusion probabilistic model (DDPM) and to compare its performance against one baseline model pix2pix and three a...
Middle Aged
Contrast Media
Magnetic Resonance Imaging
Adult
Sensitivity and Specificity
Male
Spinal Neoplasms
Image Enhancement
Signal-To-Noise Ratio
Diffusion Magnetic Resonance Imaging
Models, Statistical
Image Interpretation, Computer-Assisted
Artifacts
Humans
Algorithms
Reproducibility of Results
Aged
Computer Simulation
Female
A hybrid network based on multi-scale convolutional neural network and bidirectional gated recurrent unit for EEG denoising.
Neuroscience
Feb 28, 2025
Electroencephalogram (EEG) signals are time series data containing abundant brain information. However, EEG frequently contains various artifacts, such as electromyographic, electrooculographic, and electrocardiographic. These artifacts can change EE...
Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.
European journal of nuclear medicine and molecular imaging
Feb 26, 2025
PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion artifacts, but the shortened scanning time increases statistical noise and may affect diagnostic qu...
An enhanced denoising system for mammogram images using deep transformer model with fusion of local and global features.
Scientific reports
Feb 24, 2025
Image denoising is a critical problem in low-level computer vision, where the aim is to reconstruct a clean, noise-free image from a noisy input, such as a mammogram image. In recent years, deep learning, particularly convolutional neural networks (C...
Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.
Magma (New York, N.Y.)
Feb 18, 2025
OBJECTIVE: Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.
Accelerating veterinary low field MRI acquisitions using the deep learning based denoising solution HawkAI.
Scientific reports
Feb 18, 2025
Magnetic resonance imaging (MRI) has changed veterinary diagnosis but its long-sequence time can be problematic, especially because animals need to be sedated during the exam. Unfortunately, shorter scan times implies a fall in overall image quality ...
Sub-1-min relaxation-enhanced non-contrast non-triggered cervical MRA using compressed SENSE with deep learning reconstruction in healthy volunteers.
European radiology experimental
Feb 18, 2025
BACKGROUND: We evaluated the acceleration of a three-dimensional isotropic flow-independent magnetic resonance angiography (MRA) (relaxation-enhanced angiography without contrast and triggering, REACT) of neck arteries using compressed SENSE (CS) com...
Accelerated Multi-b-Value DWI Using Deep Learning Reconstruction: Image Quality Improvement and Microvascular Invasion Prediction in BCLC Stage A Hepatocellular Carcinoma.
Academic radiology
Feb 17, 2025
RATIONALE AND OBJECTIVES: To investigate the effect of accelerated deep-learning (DL) multi-b-value DWI (Mb-DWI) on acquisition time, image quality, and predictive ability of microvascular invasion (MVI) in BCLC stage A hepatocellular carcinoma (HCC)...
MLAR-UNet: LDCT image denoising based on U-Net with multiple lightweight attention-based modules and residual reinforcement.
Physics in medicine and biology
Feb 13, 2025
Computed tomography (CT) is a crucial medical imaging technique which uses x-ray radiation to identify cancer tissues. Since radiation poses a significant health risk, low dose acquisition procedures need to be adopted. However, low-dose CT (LDCT) ca...
Generative adversarial networks with fully connected layers to denoise PPG signals.
Physiological measurement
Feb 11, 2025
The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/...
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