Automatic Cine DENSE Strain Analysis via Phase Unwrapping
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Solution Overview
Problem
Current DENSE analysis methods, including cine DENSE, require user-defined myocardial contours and are prone to phase wrapping issues, which limits their automation and accuracy in myocardial strain analysis.
Innovation Solution
A method for automatic cine DENSE strain analysis using phase unwrapping with region growing along multiple pathways based on phase predictions, which identifies initial unwrapped regions, iteratively determines phase-wrapped pixel perimeters, and adds candidate pixels through spatiotemporal linear prediction analysis, reducing computation time and improving unwrapping success rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If user-defined myocardial contours are used in current DENSE analysis methods, then manual control and segmentation are achieved, but automation is limited and analysis time increases
Solution Approach 1:
The system performs self-service by automatically identifying myocardial contours and performing phase unwrapping without user intervention. The algorithm autonomously segments the myocardium, identifies initial unwrapped regions, and propagates solutions through the phase-encoded data set, eliminating the need for manual contour definition while reducing analysis time
Solution Approach 2:
The method applies preliminary action by first identifying initial regions where phase wrapping has not occurred before performing the main phase unwrapping operation. This preliminary identification of unwrapped regions enables the subsequent automatic propagation of phase information through the entire myocardium, streamlining the overall analysis process
2Measurement precision
If conventional phase unwrapping algorithms are used, then phase wrapping issues are addressed, but accuracy is reduced due to phase wrapping during systolic phases
Solution Approach 1:
The method segments the phase unwrapping process into distinct stages: identifying initial unwrapped regions, determining perimeters of phase-wrapped pixels, evaluating candidate growth pixels through spatiotemporal linear prediction, and iteratively adding pixels based on reliability thresholds. This segmentation enables more accurate and reliable phase unwrapping by systematically addressing different aspects of the problem
Solution Approach 2:
The system implements feedback through iterative evaluation of candidate pixels using spatiotemporal linear prediction analysis and reliability thresholds. Pixels are added to the unwrapped region only when they meet the reliability criteria, and the process iterates to continuously refine the unwrapped region boundary, improving both accuracy and success rate
3Productivity
If existing phase unwrapping methodologies are used, then phase encoding data is processed, but computation time increases due to selection of extramyocardial pixels
Solution Approach 1:
The method applies local quality by focusing computational resources only on myocardial pixels through automatic contour identification and region growing within the myocardium. By evaluating candidate growth pixels only within the identified myocardial boundaries and using reliability thresholds specific to myocardial tissue, the system avoids processing extramyocardial pixels, thereby improving computation efficiency
Solution Approach 2:
The system performs partial action by selectively processing only the necessary myocardial pixels for phase unwrapping rather than all pixels in the field of view. The region growing approach expands the unwrapped region only where needed within the myocardium, avoiding unnecessary computation on extramyocardial areas while ensuring complete coverage of the myocardial tissue
Data Source
AI summary
In one aspect the disclosed technology relates to embodiments of a method (e.g., for automatic cine DENSE strain analysis) which includes acquiring magnetic resonance data associated with a physiological activity in an area of interest of a subject where the acquired magnetic resonance data includes one or more phase-encoded data sets. The method also includes determining, from at least the one or more phase-encoded data sets, a data set corresponding to the physiological activity in the area of interest where the reconstruction comprises performing phase unwrapping of the phase-encoded data set using region growing along multiple pathways based on phase predictions.