AIMC Topic: Signal-To-Noise Ratio

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Enhanced parameter estimation in multiparametric arterial spin labeling using artificial neural networks.

Magnetic resonance in medicine
PURPOSE: Multiparametric arterial spin labeling (MP-ASL) can quantify cerebral blood flow (CBF) and arterial cerebral blood volume (CBV). However, its accuracy is compromised owing to its intrinsically low SNR, necessitating complex and time-consumin...

Verification of image quality improvement by deep learning reconstruction to 1.5 T MRI in T2-weighted images of the prostate gland.

Radiological physics and technology
This study aimed to evaluate whether the image quality of 1.5 T magnetic resonance imaging (MRI) of the prostate is equal to or higher than that of 3 T MRI by applying deep learning reconstruction (DLR). To objectively analyze the images from the 13 ...

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms.

Tomography (Ann Arbor, Mich.)
Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether t...

Deep learning-based low-dose CT simulator for non-linear reconstruction methods.

Medical physics
BACKGROUND: Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commerc...

Signal automatic modulation based on AMC neural network fusion.

PloS one
With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further i...

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction.

BMC medical imaging
BACKGROUND: The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning re...

Thin-slice 2D MR Imaging of the Shoulder Joint Using Denoising Deep Learning Reconstruction Provides Higher Image Quality Than 3D MR Imaging.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: This study was conducted to evaluate whether thin-slice 2D fat-saturated proton density-weighted images of the shoulder joint in three imaging planes combined with parallel imaging, partial Fourier technique, and denoising approach with deep...

Deep Learning Based Cystoscopy Image Enhancement.

Journal of endourology
Endoscopy image enhancement technology provides doctors with clearer and more detailed images for observation and diagnosis, allowing doctors to assess lesions more accurately. Unlike most other endoscopy images, cystoscopy images face more complex ...