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Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

Journal of cardiovascular magnetic resonance, 2019-01, Vol.21 (1), p.7-7, Article 7

2019

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Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy

PloS one, 2019-08, Vol.14 (8), p.e0221061-e0221061

2019

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Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study

Radiology, 2020-01, Vol.294 (1), p.52-60

2020

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

Journal of magnetic resonance imaging, 2021-07, Vol.54 (1), p.303-312

2021

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Automated Cardiac MR Scar Quantification in Hypertrophic Cardiomyopathy Using Deep Convolutional Neural Networks

JACC. Cardiovascular imaging, 2018-12, Vol.11 (12), p.1917-1918

2018

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Scanner-Independent MyoMapNet for Accelerated Cardiac MRI T 1 Mapping Across Vendors and Field Strengths

Journal of magnetic resonance imaging, 2024-01, Vol.59 (1), p.179-189

2024

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Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy

Journal of cardiovascular magnetic resonance, 2022-06, Vol.24 (1), p.40-40, Article 40

2022

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Detecting liver fibrosis using a machine learning‐based approach to the quantification of the heart‐induced deformation in tagged MR images

NMR in biomedicine, 2020-01, Vol.33 (1), p.e4215-n/a

2020

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Gray blood late gadolinium enhancement cardiovascular magnetic resonance for improved detection of myocardial scar

Journal of cardiovascular magnetic resonance, 2018-03, Vol.20 (1), p.22-22, Article 22

2018

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Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy

Frontiers in cardiovascular medicine, 2021-05, Vol.8, p.647857-647857

2021

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Automated analysis of cardiovascular magnetic resonance myocardial native T 1 mapping images using fully convolutional neural networks

Journal of cardiovascular magnetic resonance, 2019-01, Vol.21 (1), p.7

2019

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Fast Localization of the Optic Disc Using Projection of Image Features

IEEE transactions on image processing, 2010-12, Vol.19 (12), p.3285-3289

2010

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Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Magnetic resonance in medicine, 2021-03, Vol.85 (3), p.1195-1208

2021

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Deep convolution neural networks based artifact suppression in under-sampled radial acquisitions of myocardial T 1 mapping images

Physics in medicine & biology, 2020-11, Vol.65 (22), p.225024-225024

2020

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Assessment of cardiac mass from tagged magnetic resonance images

Japanese journal of radiology, 2016-02, Vol.34 (2), p.158-165

2016

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Cardiovascular Magnetic Resonance Radiomics to Identify Components of the Extracellular Matrix in Dilated Cardiomyopathy

Circulation (New York, N.Y.), 2024-07, Vol.150 (1), p.7-18

2024

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Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction

Journal of magnetic resonance imaging, 2022-06, Vol.55 (6), p.1812-1825

2022

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Non‐contrast myocardial infarct scar assessment using a hybrid native T1 and magnetization transfer imaging sequence at 1.5T

Magnetic resonance in medicine, 2019-05, Vol.81 (5), p.3192-3201

2019

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A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

Medical image analysis, 2014-01, Vol.18 (1), p.50-62

2014

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Radiomics of Late Gadolinium Enhancement Reveals Prognostic Value of Myocardial Scar Heterogeneity in Hypertrophic Cardiomyopathy

JACC. Cardiovascular imaging, 2024-01, Vol.17 (1), p.16-27

2024

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Detection of Cardiac Function Abnormality from MRI Images Using Normalized Wall Thickness Temporal Patterns

International journal of biomedical imaging, 2016-01, Vol.2016 (2016), p.1-6

2016

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Imaging sequence for joint myocardial T1 mapping and fat/water separation

Magnetic resonance in medicine, 2019-01, Vol.81 (1), p.486-494

2019

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Imaging sequence for joint myocardial T 1 mapping and fat/water separation

Magnetic resonance in medicine, 2019-01, Vol.81 (1), p.486-494

2019

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Nonrigid active shape model–based registration framework for motion correction of cardiac T1 mapping

Magnetic resonance in medicine, 2018-08, Vol.80 (2), p.780-791

2018

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Scanner‐Independent MyoMapNet for Accelerated Cardiac MRI T1 Mapping Across Vendors and Field Strengths

Journal of magnetic resonance imaging, 2024-01, Vol.59 (1), p.179-189

2024

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Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy

Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study

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

Automated Cardiac MR Scar Quantification in Hypertrophic Cardiomyopathy Using Deep Convolutional Neural Networks

Scanner-Independent MyoMapNet for Accelerated Cardiac MRI T 1 Mapping Across Vendors and Field Strengths

Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy

Detecting liver fibrosis using a machine learning‐based approach to the quantification of the heart‐induced deformation in tagged MR images

Gray blood late gadolinium enhancement cardiovascular magnetic resonance for improved detection of myocardial scar

Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy

Automated analysis of cardiovascular magnetic resonance myocardial native T 1 mapping images using fully convolutional neural networks

Fast Localization of the Optic Disc Using Projection of Image Features

Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Deep convolution neural networks based artifact suppression in under-sampled radial acquisitions of myocardial T 1 mapping images

Assessment of cardiac mass from tagged magnetic resonance images

Cardiovascular Magnetic Resonance Radiomics to Identify Components of the Extracellular Matrix in Dilated Cardiomyopathy

Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction

Non‐contrast myocardial infarct scar assessment using a hybrid native T1 and magnetization transfer imaging sequence at 1.5T

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

Radiomics of Late Gadolinium Enhancement Reveals Prognostic Value of Myocardial Scar Heterogeneity in Hypertrophic Cardiomyopathy

Detection of Cardiac Function Abnormality from MRI Images Using Normalized Wall Thickness Temporal Patterns

Imaging sequence for joint myocardial T1 mapping and fat/water separation

Imaging sequence for joint myocardial T 1 mapping and fat/water separation

Nonrigid active shape model–based registration framework for motion correction of cardiac T1 mapping

Scanner‐Independent MyoMapNet for Accelerated Cardiac MRI T1 Mapping Across Vendors and Field Strengths

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