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Gadolinium

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[Late gadolinium enhancement and T1 mapping for the diagnosis of cardiac amyloidosis].

Zhonghua wei zhong bing ji jiu yi xue
OBJECTIVE: To explore the role of late gadolinium enhancement (LGE) and T1 mapping for detection of cardiac amyloidosis.

Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network.

IEEE transactions on medical imaging
Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atri...

Detection and characterization of MRI breast lesions using deep learning.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions using magnetic resonance imaging (MRI) and characterize different histological subtypes of breast lesi...

Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI.

Diagnostic and interventional imaging
OBJECTIVE: To assess the diagnostic value of machine learning-based texture feature analysis of late gadolinium enhancement images on cardiac magnetic resonance imaging (MRI) for assessing the presence of ventricular tachyarrhythmia (VT) in patients ...

Deep complex convolutional network for fast reconstruction of 3D late gadolinium enhancement cardiac MRI.

NMR in biomedicine
Several deep-learning models have been proposed to shorten MRI scan time. Prior deep-learning models that utilize real-valued kernels have limited capability to learn rich representations of complex MRI data. In this work, we utilize a complex-valued...

Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis.

Multiple sclerosis (Houndmills, Basingstoke, England)
OBJECTIVE: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients.

Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging.

NeuroImage
We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Estimating lesion age is an important step when studying the longitudinal behavior of MS lesions and ca...

Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification.

European radiology
OBJECTIVES: The aim of this study was to assess the effect of a deep learning (DL)-based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification.

Improved Quantification of Myocardium Scar in Late Gadolinium Enhancement Images: Deep Learning Based Image Fusion Approach.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Quantification of myocardium scarring in late gadolinium enhanced (LGE) cardiac magnetic resonance imaging can be challenging due to low scar-to-background contrast and low image quality. To resolve ambiguous LGE regions, experienced read...

Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence.

European radiology experimental
After an ischemic event, disruptive changes in the healthy myocardium may gradually develop and may ultimately turn into fibrotic scar. While these structural changes have been described by conventional imaging modalities mostly on a macroscopic scal...