AIMC Topic: Magnetic Resonance Imaging, Cine

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Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging.

The international journal of cardiovascular imaging
Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-dependent performance differences when using deep learning for automatic LV segmentation. Retrospective ana...

Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults.

Radiology
Background Obtaining ventricular volumetry and mass is key to most cardiac MRI but challenged by long multibreath-hold acquisitions. Purpose To assess the image quality and performance of a highly accelerated, free-breathing, two-dimensional cine car...

Suppression of artifact-generating echoes in cine DENSE using deep learning.

Magnetic resonance in medicine
PURPOSE: To use deep learning for suppression of the artifact-generating T -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time.

Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI.

Magnetic resonance in medicine
PURPOSE: Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pat...

Multiscale attention guided U-Net architecture for cardiac segmentation in short-axis MRI images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic cardiac segmentation plays an utmost role in the diagnosis and quantification of cardiovascular diseases.

Ground-truth-free deep learning for artefacts reduction in 2D radial cardiac cine MRI using a synthetically generated dataset.

Physics in medicine and biology
In this work, we consider the task of image reconstruction in 2D radial cardiac cine MRI using deep learning (DL)-based regularization. As the regularization is achieved by employing an image-prior predicted by a pre-trained convolutional neural netw...

An end-to-end-trainable iterative network architecture for accelerated radial multi-coil 2D cine MR image reconstruction.

Medical physics
PURPOSE: Iterative convolutional neural networks (CNNs) which resemble unrolled learned iterative schemes have shown to consistently deliver state-of-the-art results for image reconstruction problems across different imaging modalities. However, beca...

Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmentation of the left ventricular (LV) myocardium (Myo) and RV endocardium on cine cardiac magnetic resonance (CMR) images represents an essential step for cardiac-function evaluation and diagnosis. In order to have a com...

Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global an...