AIMC Topic: Signal-To-Noise Ratio

Clear Filters Showing 71 to 80 of 953 articles

A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.

Computers in biology and medicine
Deep learning has been used extensively for medical image analysis applications, assuming the training and test data adhere to the same probability distributions. However, a common challenge arises when dealing with medical images generated by differ...

Deep Learning-Based Reconstruction for Accelerated Cervical Spine MRI: Utility in the Evaluation of Myelopathy and Degenerative Diseases.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Deep learning (DL)-based reconstruction enables improving the quality of MR images acquired with a short scan time. We aimed to prospectively compare the image quality and diagnostic performance in evaluating cervical degenera...

Semi-supervised spatial-frequency transformer for metal artifact reduction in maxillofacial CT and evaluation with intraoral scan.

European journal of radiology
PURPOSE: To develop a semi-supervised domain adaptation technique for metal artifact reduction with a spatial-frequency transformer (SFTrans) model (Semi-SFTrans), and to quantitatively compare its performance with supervised models (Sup-SFTrans and ...

A review of denoising methods in single-particle cryo-EM.

Micron (Oxford, England : 1993)
Cryo-EM has become a vital technique for resolving macromolecular structures at near-atomic resolution, enabling the visualization of proteins and large molecular complexes. However, the images are often accompanied by extremely low SNR, which poses ...

Deep structural brain imaging via computational three-photon microscopy.

Journal of biomedical optics
SIGNIFICANCE: High-resolution optical imaging at significant depths is challenging due to scattering, which impairs image quality in living matter with complex structures. We address the need for improved imaging techniques in deep tissues.

Stable heteroclinic channels as a decision-making model: overcoming low signal-to-noise ratio with mutual inhibition.

Bioinspiration & biomimetics
Bio-inspired robot controllers are becoming more complex as we strive to make them more robust to, and flexible in, noisy, real-world environments. A stable heteroclinic network (SHN) is a dynamical system that produces cyclical state transitions usi...

Using samples with label noise for robust continual learning.

Neural networks : the official journal of the International Neural Network Society
Recent studies have shown that effectively leveraging samples with label noise can enhance model robustness by uncovering more reliable feature patterns. While existing methods, such as label correction methods and loss correction techniques, have de...

Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment.

BMC medical imaging
BACKGROUND: To evaluate the performance of a deep learning reconstruction (DLR) based on Adaptive-Compressed sensing (CS)-Network for brain MRI and validate it in a clinical setting.

Preliminary phantom study of four-dimensional computed tomographic angiography for renal artery mapping: Low-tube voltage and low-contrast volume imaging with deep learning-based reconstruction.

Radiography (London, England : 1995)
INTRODUCTION: A hybrid angio-CT system with 320-row detectors and deep learning-based reconstruction (DLR), provides additional imaging via 4D-CT angiography (CTA), potentially shortening procedure time and reducing DSA acquisitions, contrast media, ...

Enhancing registration accuracy and eminence of multispectral transmission breast images by fusing multi-wavelength gen using vision transformer and LSTM.

Scientific reports
Enduring studies in the field of early breast cancer screening are investigating the use of multispectral transmission imaging. The frame accumulation system handles multispectral transmission images with deprived grayscale and unsatisfactory resolut...