AIMC Topic: Signal-To-Noise Ratio

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Generation of quantification maps and weighted images from synthetic magnetic resonance imaging using deep learning network.

Physics in medicine and biology
The generation of quantification maps and weighted images in synthetic MRI techniques is based on complex fitting equations. This process requires longer image generation times. The objective of this study is to evaluate the feasibility of deep learn...

Evaluation on the generalization of a learned convolutional neural network for MRI reconstruction.

Magnetic resonance imaging
Recently, deep learning approaches with various network architectures have drawn significant attention from the magnetic resonance imaging (MRI) community because of their great potential for image reconstruction from undersampled k-space data in fas...

MR spectroscopy frequency and phase correction using convolutional neural networks.

Magnetic resonance in medicine
PURPOSE: To introduce a novel convolutional neural network (CNN)-based approach for frequency-and-phase correction (FPC) of MR spectroscopy (MRS) spectra to achieve fast and accurate FPC of single-voxel MEGA-PRESS MRS data.

Magnetic Resonance Imaging Images under Deep Learning in the Identification of Tuberculosis and Pneumonia.

Journal of healthcare engineering
This work aimed to explore the application value of deep learning-based magnetic resonance imaging (MRI) images in the identification of tuberculosis and pneumonia, in order to provide a certain reference basis for clinical identification. In this st...

The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment.

Sensors (Basel, Switzerland)
This paper proposes a new method called independent component analysis-variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and d...

Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction.

Tomography (Ann Arbor, Mich.)
Sparse-view CT reconstruction is a fundamental task in computed tomography to overcome undesired artifacts and recover the details of textual structure in degraded CT images. Recently, many deep learning-based networks have achieved desirable perform...

The effect of a post-scan processing denoising system on image quality and morphometric analysis.

Journal of neuroradiology = Journal de neuroradiologie
PURPOSE: MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial intelligence (AI)-based post-scan processing denoising system, intelli...

Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography.

Japanese journal of radiology
PURPOSE: The purpose of this study was to evaluate whether deep learning reconstruction (DLR) improves the image quality of intracranial magnetic resonance angiography (MRA) at 1.5 T.

A Hybrid Approach for Noise Reduction in Acoustic Signal of Machining Process Using Neural Networks and ARMA Model.

Sensors (Basel, Switzerland)
Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a v...

Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising.

IEEE transactions on medical imaging
The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened public concern over the patient's associated radiation dose. On the other hand, reducing the radiation dose leads to increased noise and artifacts, w...