Analysis of cardiac dynamic global function
Leon Axel1, Mikael Kanski1, Amit Jhaveri1
1Department of Radiology and Department of Medicine, New York University Grossman School of Medicine, New York NY, USA.
Insights
This study introduces a systematic approach for analyzing dynamic global cardiac function using imaging data. It enables detailed assessment of cardiac volumes and function over the cardiac cycle, moving beyond traditional ejection fraction calculations.
Area of Science:
- Cardiovascular Imaging and Analysis
- Artificial Intelligence in Medicine
- Biomedical Engineering
Background:
- Conventional cardiac function analysis from images is limited to end-diastolic and end-systolic volumes and ejection fraction due to time-consuming segmentation.
- Advances in AI-assisted segmentation offer potential for detailed analysis of dynamic cardiac volume changes.
- Standardized methods for analyzing such dynamic data are currently lacking.
Purpose of the Study:
- To propose a systematic approach for analyzing dynamic global cardiac function from imaging data.
- To enable more detailed characterization of cardiac function beyond traditional metrics.
- To develop methods applicable to various imaging modalities, including cardiac magnetic resonance imaging (CMR).
Main Methods:
- Utilized cardiac magnetic resonance imaging (CMR) data from normal subjects and patients with heart failure with preserved ejection fraction.
- Analyzed ventricular volumes throughout the cardiac cycle.
- Calculated a set of dynamic global function variables, focusing on timing and rates of ventricular emptying and filling.
Main Results:
- A set of representative measures for timing and rates of ventricular emptying and filling was developed.
- These measures show promise as compact characterizations of dynamic global cardiac function.
- The approach was illustrated using CMR data, demonstrating its technical feasibility.
Conclusions:
- Efficient cardiac segmentation can significantly enhance the characterization of dynamic global cardiac function.
- The proposed measures of ventricular timing and rates offer a novel way to assess cardiac performance.
- This systematic approach has the potential to improve the understanding and management of cardiovascular diseases.
Objectives:
Although cardiac function is truly vital, and can be adversely affected by many diseases, conventional quantitative global function analysis from images is largely limited to assessing the cardiac volumes at end-diastole and end-systole, and the associated ejection fraction, due to the time-consuming associated image segmentation. Advances in AI-assisted cardiac image segmentation can potentially enable more detailed analysis of the dynamic changes in cardiac volumes over the cardiac cycle, in clinically practical times, but there are now no standardized ways to analyze such data. In this work, we propose a systematic approach to the analysis of dynamic global cardiac function from imaging data.
Design:
We use some cardiac magnetic resonance imaging (CMR) data here to illustrate this approach, but the methods are not limited to MRI. The focus here is on the technical approach, rather than on potential clinical applications. Representative short-axis cine CMRs from 19 normal subjects and 22 patients with clinical diagnosis of "heart failure with preserved ejection fraction" were analyzed for ventricular volumes over the cardiac cycle. The resulting data were used to calculate a set of dynamic global function variables.
Results:
A set of representative measures of the timing and rates of ventricular emptying and filling is promising as compact means to characterize dynamic global function.
Conclusions:
More efficient cardiac segmentation offers the potential to characterize dynamic global cardiac function, through a set of representative measures of the timing and rates of ventricular emptying and filling.
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