Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI.
Journal:
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PMID:
38211658
Abstract
BACKGROUND: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segmentation derived from a registered cine MRI acquisition. This requires an additional acquisition and is prone to imperfect spatial and temporal inter-scan alignment. Therefore, in this work we developed and evaluated deep learning-based methods to segment the left ventricle (LV) from 4D flow MRI directly.
Authors
Keywords
Automation
Blood Flow Velocity
Coronary Circulation
Databases, Factual
Deep Learning
Female
Heart Ventricles
Humans
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging, Cine
Male
Middle Aged
Myocardial Perfusion Imaging
Predictive Value of Tests
Reproducibility of Results
Ventricular Function, Left