AIMC Topic: Signal-To-Noise Ratio

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Robust CNN multi-nested-LSTM framework with compound loss for patch-based multi-push ultrasound shear wave imaging and segmentation.

Physics in medicine and biology
Ultrasound shear wave imaging enables noninvasive, quantitative assessment of tissue pathology with mechanical elasticity measurements. However, shear wave elastography (SWE) reconstructions are challenged by noise sensitivity, inefficient multi-push...

RAUM-GANs: a multi-layer GAN-enhanced framework for accurate multiple sclerosis lesion segmentation in MRI.

Scientific reports
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by inflammatory brain lesions, making MRI-based lesion segmentation challenging due to noise, missing data, and limited availability of high-quality labeled images. This paper pres...

A Deep Learning Model for Efficient Nontargeted Screening of New Psychoactive Substances with Benchtop Nuclear Magnetic Resonance Devices.

Analytical chemistry
Benchtop nuclear magnetic resonance (NMR) devices enable rapid on-site detection of new psychoactive substances (NPS) at customs or mobile checkpoints, addressing the urgent need for real-time screening in combating illicit drug trafficking. Benchtop...

MR-based synthetic CT generation using dual-attention enhanced 3D Conditional GAN for head and neck radiotherapy.

Biomedical physics & engineering express
. This study aims to synthesize CT from MR images for radiotherapy planning of head and neck tumor using an improved three-dimensional conditional generative adversarial network (3D cGAN) based on dual-attention modules.. A total of 212 paired CT and...

Usefulness of Data Simulation for Training Deep Learning Denoising Algorithms in Infrared Spectral Histology.

Analytical chemistry
This study investigates the use of simulated data to train deep learning models for denoising infrared spectral images of paraffin-embedded tissue sections in clinical applications. Noise in Fourier-transform infrared spectroscopy poses significant c...

Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging.

Proceedings of the National Academy of Sciences of the United States of America
Light-field microscopy (LFM) enables high-throughput functional imaging by scanlessly encoding entire volumes in single snapshots. However, LFM's computational burden and vulnerability to scattering limit its application to biological imaging. We pre...

End-to-end EEG artifact removal method via nested generative adversarial network.

Biomedical physics & engineering express
As physiological artifacts commonly overlap with EEG signals in both time and frequency domains, developing an effective end-to-end EEG artifact removal method is essential for a brain-computer interface (BCI) system. An end-to-end artifact removal m...

A low-latency neural inference framework for real-time handwriting recognition from EEG signals on an edge device.

Scientific reports
Brain-computer interfaces (BCIs) hold significant promise for restoring communication in individuals with severe motor or speech impairments. Imagined handwriting, as a form of motor imagery, offers an intuitive paradigm for character-level neural de...

Deep learning-based noise reduction method for the system matrix in magnetic particle imaging.

Physics in medicine and biology
. Magnetic particle imaging (MPI) is an emerging imaging technique based on superparamagnetic iron oxide nanoparticles, offering high sensitivity and rapid imaging. However, in measurement-based MPI, image quality is degraded by noise arising during ...

Whistles characterisation using artificial intelligence reveals responses of short-beaked common dolphins to a bio-inspired acoustic mitigation device for fishing nets.

Scientific reports
Understanding cetacean whistles is crucial for assessing their social interactions, behaviours, and responses to anthropic activities. Identifying the various types of whistles present in acoustic recordings is often challenging, but necessary for th...