AIMC Topic: Magnetic Resonance Imaging, Cine

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An iterative multi-path fully convolutional neural network for automatic cardiac segmentation in cine MR images.

Medical physics Nov 1, 2019
PURPOSE: Segmentation of the left ventricle (LV), right ventricle (RV) cavities and the myocardium (MYO) from cine cardiac magnetic resonance (MR) images is an important step for diagnosis and monitoring cardiac diseases. Spatial context information ...
Humans Heart Automation Magnetic Resonance Imaging, Cine Neural Networks, Computer Databases, Factual Image Processing, Computer-Assisted
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A distance map regularized CNN for cardiac cine MR image segmentation.

Medical physics Oct 31, 2019
PURPOSE: Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV), and the m...
Image Processing, Computer-Assisted Heart Magnetic Resonance Imaging, Cine Neural Networks, Computer
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Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Oct 7, 2019
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improvi...
Myocardial Perfusion Imaging Magnetic Resonance Imaging, Cine Image Interpretation, Computer-Assisted Coronary Circulation Deep Learning Unsupervised Machine Learning Reproducibility of Results Predictive Value of Tests Cardiovascular Diseases Humans Myocardium Diagnosis, Computer-Assisted Machine Learning Supervised Machine Learning
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Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning.

Medical image analysis Oct 4, 2019
Accurate and simultaneous segmentation and full quantification (all indices are required in a clinical assessment) of the myocardial infarction (MI) area are crucial for early diagnosis and surgical planning. Current clinical methods remain subject t...
Neural Networks, Computer Myocardial Infarction Image Interpretation, Computer-Assisted Humans Image Enhancement Magnetic Resonance Imaging, Cine
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Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis: A Solid Basis for Future Work.

Circulation. Cardiovascular imaging Sep 24, 2019
Magnetic Resonance Imaging, Cine Humans Machine Learning Biomarkers Radionuclide Imaging
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Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.

Physics in medicine and biology Aug 21, 2019
To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for 4D target tracking. One phase of a prior 4D-MRI is set as the prior phase, MRI. Pr...
Magnetic Resonance Imaging, Cine Feasibility Studies Respiration Phantoms, Imaging Imaging, Three-Dimensional Principal Component Analysis Humans Neural Networks, Computer Time Factors Liver Neoplasms
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Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI With Limited Training Data.

IEEE transactions on medical imaging Aug 9, 2019
In this work we reduce undersampling artefacts in two-dimensional (2D) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. The network is trained on 2D spatio-temporal slices which are previously extracted from the image...
Heart Algorithms Magnetic Resonance Imaging, Cine Humans Deep Learning Imaging, Three-Dimensional
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Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.

JACC. Cardiovascular imaging Jul 17, 2019
OBJECTIVES: This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output.
Ventricular Function, Left Magnetic Resonance Imaging, Cine Humans Stroke Volume Ventricular Function, Right Female Quality Indicators, Health Care Middle Aged Quality Control Reproducibility of Results Automation Aged Heart Diseases Diagnosis, Computer-Assisted Male Image Interpretation, Computer-Assisted Deep Learning Predictive Value of Tests
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Cine MRI analysis by deep learning of optical flow: Adding the temporal dimension.

Computers in biology and medicine Jul 12, 2019
Accurate segmentation of the left ventricle (LV) from cine magnetic resonance imaging (MRI) is an important step in the reliable assessment of cardiac function in cardiovascular disease patients. Several deep learning convolutional neural network (CN...
Algorithms Deep Learning Image Processing, Computer-Assisted Heart Heart Ventricles Humans Magnetic Resonance Imaging, Cine
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Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow.

Medical image analysis Jun 6, 2019
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amou...
Image Interpretation, Computer-Assisted Supervised Machine Learning Humans Magnetic Resonance Imaging, Cine Motion Image Processing, Computer-Assisted Heart Diseases
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