Phantom-Based Ultrasound-ECG Deep Learning Framework for Prospective Cardiac Computed Tomography | JoVE Visualize

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiology

Background:

Computed tomography angiography (CTA) gating relies on predicting cardiac quiescent periods (QPs) for diagnostic image quality.
Current single-modality gating methods, often ECG-based, struggle with variable heart rates and arrhythmias.
Optimizing CTA gating is crucial to avoid non-diagnostic scans due to motion artifacts.

Purpose of the Study:

To develop and validate a novel multimodal deep learning framework for enhanced cardiac quiescent period (QP) prediction.
To integrate ultrasound (US) and electrocardiography (ECG) data for more robust QP prediction in cardiac computed tomography angiography (CTA) gating.
To improve the accuracy and reliability of CTA gating, particularly in patients with arrhythmias and variable heart rates.

Main Methods:

A multimodal deep learning framework was developed, integrating a 3D convolutional neural network (CNN) for US data and an artificial neural network (ANN) for ECG data.
The framework was validated using a dynamic heart motion phantom simulating diverse cardiac conditions, including arrhythmias.
Performance was evaluated across various QP lengths, cardiac segments, and motion patterns to mimic real-world clinical scenarios.

Main Results:

The multimodal US-ECG 3D CNN-ANN framework achieved 96.87% accuracy in QP prediction, significantly outperforming single-modality ECG-only gating (85.56%).
The framework demonstrated superior performance in predicting QPs during arrhythmic conditions and for longer durations (100-200 ms).
Accurate QP prediction was consistently achieved across different cardiac regions, with an average accuracy of 92% in clinically relevant echocardiographic views.

Conclusions:

Combining US and ECG data via a multimodal deep learning framework significantly enhances QP prediction accuracy, especially under variable cardiac motion and in arrhythmic patients.
This multimodal approach offers a promising solution for improving diagnostic scan rates in CTA by providing more reliable gating.
The framework's robustness across various cardiac conditions suggests potential for widespread clinical adoption in improving cardiac imaging quality.