AIMC Topic: Magnetic Resonance Imaging, Cine

Clear Filters Showing 121 to 130 of 187 articles

Temporally coherent cardiac motion tracking from cine MRI: Traditional registration method and modern CNN method.

Medical physics
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...

Deep Learning Single-Frame and Multiframe Super-Resolution for Cardiac MRI.

Radiology
Background Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution. Purpose To ex...

Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiovascular diseases can be effectively prevented from worsening through early diagnosis. To this end, various methods have been proposed to detect the disease source by analyzing cardiac magnetic resonance images (MRI), wherein left ventricular s...

A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventric...

Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture.

Biomedical physics & engineering express
BACKGROUND: Magnetic resonance cine imaging is the accepted standard for cardiac functional assessment. Left ventricular (LV) segmentation plays a key role in volumetric functional quantification of the heart. Conventional manual analysis is time-con...

Pyramid feature adaptation for semi-supervised cardiac bi-ventricle segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac bi-ventricle segmentation (BVS) is an essential task for assessing cardiac indices, such as the ejection fraction and volume of the left ventricle (LV) and right ventricle (RV). However, BVS is extremely challenging due to the high variabilit...

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

Medical physics
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 ...

A distance map regularized CNN for cardiac cine MR image segmentation.

Medical physics
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...

Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
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...