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Assessing Cardiac Reprogramming using High Content Imaging Analysis

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Fully Automatic Quantitative Measurement of Equilibrium Radionuclide Angiocardiography Using a Convolutional Neural

Sejin Ha¹, Seung Yeon Seo², Byung Soo Park¹

¹From the Department of Nuclear Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.

Clinical Nuclear Medicine

|July 5, 2024

Summary

Deep learning accurately generated left ventricular regions of interest (ROIs) from equilibrium radionuclide angiography scans. This method enhances the convenience and reproducibility of left ventricular ejection fraction (LVEF) measurements.

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Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Left ventricular ejection fraction (LVEF) is a critical indicator of cardiac function.
Accurate LVEF measurement relies on precise delineation of left ventricular regions of interest (ROIs).
Traditional manual ROI delineation is time-consuming and prone to inter-observer variability.

Purpose of the Study:

To develop and validate a deep learning model for automated ROI generation in equilibrium radionuclide angiography (ERNA).
To assess the performance of deep learning-based ROIs (dlROIs) compared to manually drawn ROIs (mROIs) and preprocessed ROIs (pROIs) for LVEF measurement.

Main Methods:

A 2D U-Net convolutional neural network was trained to generate dlROIs from ERNA datasets.
Ground truth ROIs (pROIs) were derived from manually drawn ROIs (mROIs) using a 41% threshold.
Model performance was evaluated using Lin's concordance correlation coefficient (CCC) and Bland-Altman analysis.

Main Results:

The study included 41,462 scans from 19,309 patients.
Strong concordance was observed between LVEF measurements from dlROIs and pROIs (CCC=85.6%) and dlROIs and mROIs (CCC=86.1%).
The mean LVEF difference between dlROIs and mROIs was -0.4%, with 91% of scans showing an absolute difference <5%.

Conclusions:

The 2D U-Net model demonstrated excellent performance in generating LV ROIs from ERNA scans.
Automated dlROI generation can significantly improve the convenience and reproducibility of LVEF measurements.
This deep learning approach holds promise for routine clinical application in cardiac imaging.