Project Details
Description
Summary/Abstract
In the presence of diseases such as ischemic heart disease (IHD), cardiac dyssynchrony deteriorates cardiac
function and often cannot be treated effectively. However, while imaging methods such as cardiovascular
magnetic resonance (CMR) can provide high quality images of the moving heart, conventional clinical
quantitative analysis of cardiac function is largely limited to global function analysis of the left ventricle (LV),
with only qualitative and subjective characterization of regional function. An obstacle to better quantification of
regional function is the complex 3D structure and motion of the heart wall, which has typically necessitated
time-consuming user-guided processing of the images to carry out the associated 3D-motion analysis.
Recent advances in machine-learning (ML) approaches for image analysis are promising as new means to
speed up the processing of cardiac images, as well as to analyze the underlying regional motion patterns.
However, current Deep ML (DML) approaches to image analysis largely function as “black boxes”, without
clear indications of which features contribute most to the analysis results, thus limiting their clinical utility. In
the initial funded period of this research project, we have been developing integrated approaches to the
segmentation, 3D reconstruction, and analysis of CMR data, with application to the evaluation of cardiac
dyssynchrony. Today, treatment of dyssynchrony in HF with cardiac resynchronization therapy (CRT) leads to
improvement in only ~2/3 patients selected with conventional criteria (usually by electrocardiogram [ECG]).
Our initial results show encouraging results of correlation between MRI evaluation of dyssynchrony and
cardiac resynchronization therapy (CRT) outcomes. In the new proposed research, we will further develop
these methods, with the goal of automating the cardiac analysis methods. This will include the introduction of
new ML-based methods, which will incorporate information on the specific cardiac motion factors that lead to
classification of different disease states in dyssynchrony. Our Hypothesis is that by using these new ML-based
methods for cardiac motion analysis, we will discover and evaluate significant quantitative correlations
between different cardiac dyssynchrony motion patterns and CRT outcomes. Also, late-gadolinium
enhancement (LGE) provides images for infarction visualization. Incorporation of tissue characterization into
the motion-pattern analysis could lead to increased understanding of how infarcted areas affect regional
motion in concert with dyssynchrony. The unearthing of these findings will allow us to validate them in future
clinical studies.
The project will also disseminate our novel, coupled DML and model-based methodology for quantifying and
classifying cardiac motion in diseases affecting regional wall motion. Other research groups can then apply our
tools to specifically study dyssynchrony, as well as other cardiac diseases affecting LV motion.
Status | Finished |
---|---|
Effective start/end date | 4/1/15 → 4/30/24 |
Funding
- National Heart, Lung, and Blood Institute: $589,455.00
- National Heart, Lung, and Blood Institute: $735,785.00
- National Heart, Lung, and Blood Institute: $593,963.00
- National Heart, Lung, and Blood Institute: $634,258.00
- National Heart, Lung, and Blood Institute: $727,889.00
- National Heart, Lung, and Blood Institute: $599,020.00
- National Heart, Lung, and Blood Institute: $731,935.00
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