AIMC Topic: Signal-To-Noise Ratio

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Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis.

Abdominal radiology (New York) Sep 14, 2023
PURPOSE: To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting algorithm (DLICA) for hypovascular hepatic metastases.
Deep Learning Iodine Liver Neoplasms Radiographic Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Radiography, Dual-Energy Scanned Projection Algorithms Retrospective Studies Tomography, X-Ray Computed Humans
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Unsupervised learning-based dual-domain method for low-dose CT denoising.

Physics in medicine and biology Sep 8, 2023
. Low-dose CT (LDCT) is an important research topic in the field of CT imaging because of its ability to reduce radiation damage in clinical diagnosis. In recent years, deep learning techniques have been widely applied in LDCT imaging and a large num...
Image Processing, Computer-Assisted Algorithms Unsupervised Machine Learning Tomography, X-Ray Computed Signal-To-Noise Ratio
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Deep learning-based reconstruction can improve canine thoracolumbar magnetic resonance image quality and reduce slice thickness.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association Sep 5, 2023
In veterinary practice, thin-sliced thoracolumbar MRI is useful in detecting small lesions, especially in small-breed dogs. However, it is challenging due to the partial volume averaging effect and increase in scan time. Currently, deep learning-base...
Radiographic Image Interpretation, Computer-Assisted Dogs Radiation Dosage Artificial Intelligence Prospective Studies Deep Learning Animals Magnetic Resonance Imaging Signal-To-Noise Ratio
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Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine Sep 1, 2023
PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in differ...
Female Aged Aged, 80 and over Humans Retrospective Studies Lung Deep Learning Imaging, Three-Dimensional Adult Male Middle Aged Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Multidetector Computed Tomography Carcinoma, Non-Small-Cell Lung Contrast Media Magnetic Resonance Imaging Image Processing, Computer-Assisted Lung Neoplasms
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Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning.

IEEE transactions on medical imaging Aug 31, 2023
Deep learning (DL) based image processing methods have been successfully applied to low-dose x-ray images based on the assumption that the feature distribution of the training data is consistent with that of the test data. However, low-dose computed ...
Signal-To-Noise Ratio Algorithms Computer Simulation Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted
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Accelerated 3D MR neurography of the brachial plexus using deep learning-constrained compressed sensing.

European radiology Aug 22, 2023
OBJECTIVES: To explore the use of deep learning-constrained compressed sensing (DLCS) in improving image quality and acquisition time for 3D MRI of the brachial plexus.
Brachial Plexus Humans Deep Learning Imaging, Three-Dimensional Magnetic Resonance Imaging Signal-To-Noise Ratio
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Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors.

Magnetic resonance in medicine Aug 15, 2023
PURPOSE: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.
Neural Networks, Computer Signal-To-Noise Ratio Image Processing, Computer-Assisted Bayes Theorem Uncertainty
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Deep learning-based reconstruction for canine brain magnetic resonance imaging could improve image quality while reducing scan time.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association Aug 15, 2023
Optimal magnetic resonance imaging (MRI) quality and shorter scan time are challenging to achieve in veterinary practices. Recently, deep learning-based reconstruction (DLR) has been proposed for ideal image quality. We hypothesized that DLR-based MR...
Dogs Prospective Studies Animals Brain Signal-To-Noise Ratio Deep Learning Magnetic Resonance Imaging
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An artificial intelligence algorithm for pulmonary embolism detection on polychromatic computed tomography: performance on virtual monochromatic images.

European radiology Aug 5, 2023
OBJECTIVES: Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to their different appearances, the performance of artificial intelligence (AI) trained on conventional CT...
Humans Artificial Intelligence Algorithms Computed Tomography Angiography Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Pulmonary Embolism Tomography, X-Ray Computed Signal-To-Noise Ratio
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Feature-guided deep learning reduces signal loss and increases lesion CNR in diffusion-weighted imaging of the liver.

Zeitschrift fur medizinische Physik Aug 4, 2023
PURPOSE: This research aims to develop a feature-guided deep learning approach and compare it with an optimized conventional post-processing algorithm in order to enhance the image quality of diffusion-weighted liver images and, in particular, to red...
Liver Diseases Signal-To-Noise Ratio Diffusion Magnetic Resonance Imaging Liver Neoplasms Image Processing, Computer-Assisted Liver Middle Aged Deep Learning Algorithms Humans Female Male
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