AIMC Topic: Signal-To-Noise Ratio
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Illustration image style transfer method design based on improved cyclic consistent adversarial network.
PloS one
Jan 14, 2025
To improve the expressiveness and realism of illustration images, the experiment innovatively combines the attention mechanism with the cycle consistency adversarial network and proposes an efficient style transfer method for illustration images. The...
Optimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction.
BMC medical imaging
Jan 13, 2025
BACKGROUND: Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. L...
Endpoint-aware audio-visual speech enhancement utilizing dynamic weight modulation based on SNR estimation.
Neural networks : the official journal of the International Neural Network Society
Jan 11, 2025
Integrating visual features has been proven effective for deep learning-based speech quality enhancement, particularly in highly noisy environments. However, these models may suffer from redundant information, resulting in performance deterioration w...
Accelerated High-resolution T1- and T2-weighted Breast MRI with Deep Learning Super-resolution Reconstruction.
Academic radiology
Jan 9, 2025
RATIONALE AND OBJECTIVES: To assess the performance of an industry-developed deep learning (DL) algorithm to reconstruct low-resolution Cartesian T1-weighted dynamic contrast-enhanced (T1w) and T2-weighted turbo-spin-echo (T2w) sequences and compare ...
Deep Equilibrium Unfolding Learning for Noise Estimation and Removal in Optical Molecular Imaging.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 8, 2025
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe no...
PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 8, 2025
Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkinson disease Denoising and Segmentation Network (PADS-Net), to simultaneously denoise and segment tra...
Deep Learning-Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction.
AJNR. American journal of neuroradiology
Jan 8, 2025
BACKGROUND AND PURPOSE: DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (D...
DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning-Based Image Quality Improvement for Vessel Wall MRI.
AJNR. American journal of neuroradiology
Jan 8, 2025
BACKGROUND AND PURPOSE: Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied t...
Deep Power-Aware Tunable Weighting for Ultrasound Microvascular Imaging.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Jan 8, 2025
Ultrasound microvascular imaging (UMI), including ultrafast power Doppler imaging (uPDI) and ultrasound localization microscopy (ULM), obtains blood flow information through plane wave (PW) transmissions at high frame rates. However, low signal-to-no...
Using deep learning to shorten the acquisition time of brain MRI in acute ischemic stroke: Synthetic T2W images generated from b0 images.
PloS one
Jan 6, 2025
OBJECTIVE: This study aimed to assess the feasibility of the deep learning in generating T2 weighted (T2W) images from diffusion-weighted imaging b0 images.
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