AIMC Topic: Signal-To-Noise Ratio

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A unified beamforming and source separation model for static and dynamic human-robot interaction.

JASA express letters
This paper presents a unified model for combining beamforming and blind source separation (BSS). The validity of the model's assumptions is confirmed by recovering target speech information in noise accurately using Oracle information. Using real sta...

Convolutional transformer-driven robust electrocardiogram signal denoising framework with adaptive parametric ReLU.

Mathematical biosciences and engineering : MBE
The electrocardiogram (ECG) is a widely used diagnostic tool for cardiovascular diseases. However, ECG recording is often subject to various noises, which can limit its clinical evaluation. To address this issue, we propose a novel Transformer-based ...

scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision.

Bioinformatics (Oxford, England)
MOTIVATION: Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell data analysis. Although a host of works have been developed, the low signal-to-noise-ratio single-cell RNA-sequencing data that suffers from batch effects...

Untrained Network for Super-resolution for Non-contrast-enhanced Wholeheart MRI Acquired using Cardiac-triggered REACT (SRNN-REACT).

Current medical imaging
BACKGROUND: Three-dimensional (3D) whole-heart magnetic resonance imaging (MRI) is an excellent tool to check the heart anatomy of patients with congenital and acquired heart disease. However, most 3D whole-heart MRI acquisitions take a long time to ...

Improving Image Quality and Diagnostic Performance of CCTA in Patients with Challenging Heart Rate Conditions using a Deep Learning-based Motion Correction Algorithm.

Current medical imaging
OBJECTIVE: Challenging HR conditions, such as elevated Heart Rate (HR) and Heart Rate Variability (HRV), are major contributors to motion artifacts in Coronary Computed Tomography Angiography (CCTA). This study aims to assess the impact of a deep lea...

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.

Journal of X-ray science and technology
OBJECTIVES: To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive s...

Content-Noise Feature Fusion Neural Network for Image Denoising in Magnetic Particle Imaging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Magnetic particle imaging (MPI) is a tomographic imaging method that quantitatively determines the distribution of magnetic nanoparticles (MNPs). However, the performance of MPI is primarily limited by the noise in the receive coil and electronic dev...

[Quality of Images Reconstructed by Deep Learning Reconstruction Algorithm for Head and Neck CT Angiography at 100 kVp].

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
Objective To evaluate the impact of deep learning reconstruction algorithm on the image quality of head and neck CT angiography (CTA) at 100 kVp. Methods CT scanning was performed at 100 kVp for the 37 patients who underwent head and neck CTA in PUMC...

Benchmarking the Impact of Noise on Deep Learning-Based Classification of Atrial Fibrillation in 12-Lead ECG.

Studies in health technology and informatics
Electrocardiography analysis is widely used in various clinical applications and Deep Learning models for classification tasks are currently in the focus of research. Due to their data-driven character, they bear the potential to handle signal noise ...

RESEARCH PROGRESS OF DEEP LEARNING IN LOW-DOSE CT IMAGE DENOISING.

Radiation protection dosimetry
Low-dose computed tomography (CT) will increase noise and artefacts while reducing the radiation dose, which will adversely affect the diagnosis of radiologists. Low-dose CT image denoising is a challenging task. There are essential differences betwe...