Cardiac MRI Mis-Trigger Detection from ECG Timing Data
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Solution Overview
Problem
Existing methods struggle to identify and exclude MR images acquired based on incorrect trigger events in cardiac MR data sets, especially due to cardiac motion variations and mis-triggering, which affects motion compensation and post-processing in dynamic contrast-enhanced MRI.
Innovation Solution
A method to process cardiac MR data sets by analyzing time differences between acquisitions based on timestamps, identifying pairs of incorrect time differences to exclude MR data sets affected by incorrect trigger events, without requiring image reconstruction or user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high acceleration factors are used to increase image resolution and morphologic coverage, then image quality and coverage are improved, but motion artifacts increase and reconstruction complexity increases
Solution Approach 1:
The patent applies preliminary action by detecting and flagging mis-triggered frames before the actual image reconstruction process. By analyzing ECG trigger timestamps and identifying frames with incorrect trigger events in advance, the system prepares a corrected data set that can be reconstructed without motion artifacts, thus resolving the contradiction between using high acceleration factors and managing reconstruction complexity
2Reliability
If motion compensation algorithms are applied to correct cardiac motion, then image quality is improved, but algorithm complexity and computational load increase
Solution Approach 1:
The patent extracts and removes the problematic mis-triggered frames from the data set before reconstruction, rather than applying complex motion compensation algorithms to correct them. By taking out the corrupted data points that cause motion artifacts, the system achieves reliable image quality without the need for sophisticated motion correction algorithms
Solution Approach 2:
The patent converts the harmful effect of mis-triggered frames into a benefit by using the ECG trigger timestamp information to identify and exclude these frames. The problem of incorrect triggering is transformed into a detectable pattern in the timestamp data, allowing automatic correction without complex algorithms
3Measurement precision
If neural networks are used to classify and reject mis-triggered data, then accuracy in identifying incorrect frames is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent uses simple, computationally inexpensive timestamp analysis instead of expensive neural network models. By leveraging the readily available ECG trigger timestamps and applying basic comparison logic against reference intervals, the system achieves accurate detection of mis-triggered frames without the heavy computational burden of machine learning approaches
Solution Approach 2:
The patent replaces the complex 'mechanical system' of neural network processing with a simpler information-based approach using ECG timestamp analysis. Instead of using computational heavyweights, the system substitutes a lightweight method that compares timestamp intervals to detect anomalies, dramatically reducing processing time while maintaining detection accuracy
4Productivity
If a priori motion assumptions are made in reconstruction algorithms, then reconstruction speed is improved, but accuracy in handling extreme cardiac motion deteriorates
Solution Approach 1:
The patent applies preliminary action by identifying and removing frames with extreme or incorrect cardiac motion before reconstruction. By using ECG trigger timestamps to detect mis-triggered frames that exhibit abnormal motion patterns, the system prepares a cleaned data set that can be reconstructed with standard algorithms, achieving both speed and accuracy without requiring complex motion-adaptive reconstruction methods
Data Source
AI summary
Cardiac MR data sets are acquired over cardiac cycles and based on a trigger event, with each cardiac MR data set having a corresponding time stamp. A series of time differences is determined between acquisitions of the cardiac MR data sets based on the time stamps, and a default time difference representing the time difference between two consecutive cardiac MR data sets is determined, which were acquired using correct trigger events. A pair of incorrect time differences is determined, each incorrect time difference representing a time difference between two consecutive MR data sets in which at least one MR data set was acquired using an incorrect trigger event. Approved MR data sets are determined comprising cardiac MR data sets from the cardiac MR data sets which were acquired using the correct trigger event, based on the pair of incorrect time differences, and the approved MR data sets are further processed.