Neural Network ECG Peak Detection for Cardiac Imaging Synchronization
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Solution Overview
Problem
Current medical imaging systems face challenges in synchronizing data acquisition with the cardiac cycle, leading to motion-related artifacts and suboptimal image quality, particularly during cardiac imaging.
Innovation Solution
The implementation of a method using an electrocardiographic (ECG) signal to trigger data acquisition in MRI systems through deep learning classifiers, specifically convolutional neural networks, to detect R-peaks and synchronize data acquisition with the cardiac cycle, thereby minimizing motion artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gating techniques are used to synchronize data acquisition with the cardiac cycle, then motion-related artifacts are reduced, but the precision and robustness of peak detection are insufficient leading to suboptimal image quality
Solution Approach 1:
The patent replaces traditional mechanical and signal-processing-based peak detection systems with a neural network-based detection system. The neural network analyzes ECG signals to detect cardiac peaks with superior precision and robustness, eliminating the need for complex thresholding and filtering algorithms while improving detection accuracy under varying signal conditions.
Solution Approach 2:
The patent introduces a neural network as an intermediary component between the ECG signal acquisition and the data acquisition triggering system. This neural network intermediary processes the raw ECG signals, extracts meaningful peak information, and provides reliable triggering signals to the imaging system, thereby improving overall system performance without directly modifying the core imaging hardware.
2Productivity
If data acquisition is triggered without robust cardiac cycle synchronization, then acquisition speed is maintained, but motion artifacts increase reducing image quality
Solution Approach 1:
The patent implements preliminary action by continuously analyzing ECG signals and pre-determining optimal triggering points before data acquisition begins. The neural network processes cardiac cycle information in advance, identifies upcoming peaks, and prepares triggering signals, ensuring that data acquisition is always synchronized with the cardiac cycle without delaying the imaging process.
3Reliability
If traditional peak detection methods are used, then system simplicity is maintained, but detection reliability varies under different signal conditions
Solution Approach 1:
The patent applies parameter changes by training the neural network to adapt to varying ECG signal characteristics under different physiological conditions. The system dynamically adjusts its detection parameters based on the input signal properties, maintaining high reliability across diverse signal conditions without requiring manual calibration or complex rule-based adjustments.
Data Source
AI summary
Methods and systems are provided for cardiac triggering of an imaging system. a method for an imaging system comprises acquiring, during a scan of a subject, an electrical signal indicating a periodic physiological motion of an organ of the subject, inputting a sample of the electrical signal into a trained neural network to detect whether a peak is present in the sample, triggering acquisition of image data responsive to detecting the peak in the sample, and not triggering the acquisition of image data responsive to not detecting the peak in the sample. In this way, the timing of data acquisition may be optimally and robustly synchronized with a cardiac cycle.