AIMC Topic: Myocardium
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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 : JMRI
Feb 17, 2021
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...
A deep learning approach with temporal consistency for automatic myocardial segmentation of quantitative myocardial contrast echocardiography.
The international journal of cardiovascular imaging
Feb 17, 2021
Quantitative myocardial contrast echocardiography (MCE) has been proved to be valuable in detecting myocardial ischemia. During quantitative MCE analysis, myocardial segmentation is a critical step in determining accurate region of interests (ROIs). ...
Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Dec 7, 2020
BACKGROUND: Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the...
Humans
Aged, 80 and over
Male
Ventricular Function, Left
Deep Learning
Female
Reproducibility of Results
Aged
Magnetic Resonance Imaging, Cine
Amyloid Neuropathies, Familial
Ventricular Remodeling
Immunoglobulin Light-chain Amyloidosis
Cardiomyopathy, Hypertrophic
Image Processing, Computer-Assisted
Hypertrophy, Left Ventricular
Predictive Value of Tests
Myocardium
Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification.
European radiology
Nov 21, 2020
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.
Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with MRI.
Computer methods and programs in biomedicine
Oct 27, 2020
BACKGROUND AND OBJECTIVE: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may...
Identifying gross post-mortem organ images using a pre-trained convolutional neural network.
Journal of forensic sciences
Oct 26, 2020
Identifying organs/tissue and pathology on radiological and microscopic images can be performed using convolutional neural networks (CNN). However, there are scant studies on applying CNN to post-mortem gross images of visceral organs. This proof-of-...
Detecting myocardial scar using electrocardiogram data and deep neural networks.
Biological chemistry
Oct 2, 2020
Ischaemic heart disease is among the most frequent causes of death. Early detection of myocardial pathologies can increase the benefit of therapy and reduce the number of lethal cases. Presence of myocardial scar is an indicator for developing ischae...
Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Aug 20, 2020
BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Nat...
Artificial Intelligence and Myocardial Contrast Enhancement Pattern.
Current cardiology reports
Jul 7, 2020
PURPOSE OF REVIEW: Machine learning (ML) and deep learning (DL) are two important categories of AI algorithms. Nowadays, AI technology has been gradually applied to cardiac magnetic resonance imaging (CMRI), covering the fields of myocardial contrast...
Temporally coherent cardiac motion tracking from cine MRI: Traditional registration method and modern CNN method.
Medical physics
Jul 6, 2020
PURPOSE: Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to perform manually. In this paper, we aim to develop and...
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