Physiological Signal Gating for Motion-Synchronized Medical Imaging
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Solution Overview
Problem
Medical procedures, such as MRI and PET scans, are often affected by physiological motions like heartbeats and breathing, leading to poor image quality due to inaccurate detection of R waves, especially in patients with heart conditions or strong magnetic fields.
Innovation Solution
A system using a trained machine learning model to acquire physiological data, determine feature data, and generate a trigger pulse signal to synchronize imaging device scans with physiological motion, improving image data acquisition by reducing motion artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a gating acquisition technique is used to reduce the effects of physiological motion, then image quality is improved, but the accuracy of R wave detection deteriorates due to factors like heart disease and magnetic field interference
Solution Approach 1:
The patent introduces an intermediary signal processing system that uses a combination of ECG signals and respiratory signals as mediators to detect physiological motion states. This intermediary detection mechanism overcomes the limitation of direct R wave detection by using multiple signal sources to indirectly determine the optimal imaging timing, thereby resolving the contradiction between image quality improvement and detection accuracy
Solution Approach 2:
The patent replaces the traditional mechanical/electrical R wave detection system with a machine learning-based signal processing system. The system uses trained models to analyze ECG and respiratory signals, substituting the direct electrical detection method with an intelligent analysis approach that is more robust to magnetic field interference and pathological conditions
2Device complexity
If traditional R wave detection methods are used, then the system complexity is low, but the reliability of image data acquisition deteriorates due to incorrect triggers
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors physiological signals, compares detected features with expected patterns, and adjusts the imaging trigger timing accordingly. The machine learning model provides feedback by predicting the optimal imaging window based on analyzed physiological data, creating a closed-loop system that improves reliability through continuous optimization
Solution Approach 2:
The system performs preliminary analysis of physiological signals before triggering the imaging sequence. By using trained machine learning models to predict optimal imaging timing in advance based on pre-acquired ECG and respiratory data, the system prepares the imaging trigger timing beforehand, ensuring reliable data acquisition while maintaining manageable system complexity
Data Source
AI summary
The present disclosure provides a system and method for image data acquisition. The method may include acquiring physiological data of a subject. The physiological data may correspond to a motion of the subject over time. The method may include obtaining a trained machine learning model configured to detect feature data represented in the physiological data. The method may include determining, based on the physiological data, an output result of the trained machine learning model that is generated based on the feature data. The method may include acquiring, based on the output result, image data of the subject using an imaging device.