AIMC Topic: Magnetic Resonance Imaging, Cine
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Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.
Med (New York, N.Y.)
Mar 29, 2023
BACKGROUND: Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size a...
Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass.
Nature communications
Mar 21, 2023
Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use...
Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Feb 24, 2023
In this investigation, an image-based method has been developed to estimate the volume of the left ventricular cavity using cardiac magnetic resonance (CMR) imaging data. Deep learning and Gaussian processes have been applied to bring the estimations...
Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population; a common mistake.
The international journal of cardiovascular imaging
Feb 24, 2023
Reliability (repeatability or agreement) is assessed by different statistical tests including Pearson r or Spearman rho which is one of the common mistakes in reliability analysis. Pearson r or Spearman rho correlation coefficient only assesses the l...
Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging.
The international journal of cardiovascular imaging
Feb 10, 2023
PURPOSE: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow.
Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population.
The international journal of cardiovascular imaging
Jan 17, 2023
This study aimed to assess the image quality and accuracy of respiratory-gated real-time two-dimensional (2D) cine incorporating deep learning reconstruction (DLR) for the quantification of biventricular volumes and function compared with those of th...
4D segmentation of the thoracic aorta from 4D flow MRI using deep learning.
Magnetic resonance imaging
Jan 5, 2023
BACKGROUND: 4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For personalized management of TAA, new biomarkers are required to analyze the effect of fluid structure it...
Compensation for respiratory motion-induced signal loss and phase corruption in free-breathing self-navigated cine DENSE using deep learning.
Magnetic resonance in medicine
Jan 5, 2023
PURPOSE: To introduce a model that describes the effects of rigid translation due to respiratory motion in displacement encoding with stimulated echoes (DENSE) and to use the model to develop a deep convolutional neural network to aid in first-order ...
Artificial Intelligence to Extract Endogenous Tissue Characteristics: Has the Future Free From Gadolinium Contrast Arrived?
Circulation
Nov 14, 2022
Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.
European radiology
Oct 29, 2022
OBJECTIVES: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to ...
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