Four-Dimensional Flow MRI Phase Unwrapping in Low-Venc Scans
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 4D flow MRI systems lack robust and reliable methods for phase unwrapping, particularly in low-venc acquisitions with large-aliased areas or repeatedly wrapped regions, leading to unreliable velocity measurements due to phase noise and increased scan times.
Innovation Solution
A flow-physics constrained weighted least-squares (CWLS) method is introduced, incorporating divergence-free constraints and phase variation uncertainty to optimize phase unwrapping, using weighted least-squares computations to generate unwrapped phase data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional phase unwrapping methods are used in low-venc acquisitions, then velocity measurements can be obtained, but the measurements become unreliable due to phase noise and aliasing errors
Solution Approach 1:
The patent transforms the phase unwrapping problem from a local gradient-based approach to a global parameter optimization problem. By formulating the unwrapping as a weighted least-squares minimization with divergence-free constraints, the method changes the fundamental parameters of the solution approach, enabling reliable velocity measurements even in low-venc conditions with significant aliasing.
Solution Approach 2:
The patent incorporates feedback through the use of magnitude data to weight the phase differences in the least-squares formulation. Regions with low signal-to-noise ratio receive lower weights, allowing the algorithm to adaptively respond to local quality variations and produce more reliable velocity measurements throughout the field of view.
2Productivity
If existing phase unwrapping algorithms are applied to large-aliased areas, then computational processing can be performed, but the results contain increased errors and reduced success rates
Solution Approach 1:
The patent performs preliminary action by incorporating the divergence-free constraint into the phase unwrapping formulation before solving. This pre-constraining of the solution space based on the physical property of incompressible flow eliminates the need for subsequent correction steps and directly produces accurate velocity fields even in challenging aliasing conditions.
Solution Approach 2:
The patent changes the mathematical parameters of the unwrapping algorithm from simple gradient integration to a constrained least-squares optimization. This parameter transformation allows the algorithm to handle large-aliased areas effectively by finding the globally optimal solution that satisfies both the measured phase differences and the divergence-free condition.
3Measurement precision
If robust phase unwrapping methods are implemented, then measurement accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the complex 4D flow unwrapping problem into manageable components: spatial phase differences, temporal phase differences, and divergence-free constraints. By breaking down the problem into these distinct elements and solving them through a structured least-squares formulation, the method achieves high accuracy while maintaining computational tractability.
Solution Approach 2:
The patent replaces traditional mechanical gradient-based unwrapping methods with a mathematical optimization approach. By substituting the direct gradient integration mechanism with a least-squares minimization framework, the algorithm achieves greater robustness and accuracy while the structured mathematical formulation keeps computational complexity manageable.
4Measurement precision
If low-venc acquisitions are performed to improve velocity resolution, then aliasing increases, but the ability to measure high-velocity flow is reduced
Solution Approach 1:
The patent changes the fundamental parameter relationship by decoupling velocity resolution from venc setting. Through the phase unwrapping algorithm that incorporates divergence-free constraints, the system can use low venc values to achieve high velocity resolution while the unwrapping process recovers the true high-velocity information that would otherwise be aliased, effectively expanding the measurable velocity range.
Solution Approach 2:
The patent introduces the phase unwrapping algorithm as an intermediary between the low-venc phase measurements and the final velocity field. This intermediary process interprets the wrapped phase data in the context of divergence-free flow, enabling the system to achieve both high resolution and wide measurement range that would normally be mutually exclusive.
Data Source
AI summary
A method of processing data by an imaging system is described. The imaging system generates a velocity data set and magnitude data set representative of a fluid. The method includes receiving velocity data set from the imaging system, calculating a phase variation data set from a wrapped phase field data set associated with the velocity data set, calculating a phase difference uncertainty data set from the magnitude data set, using the phase variation-data set and the phase difference uncertainty data set, performing a computational reconstruction of the phase field, data set to generate an unwrapped phase data set, converting the unwrapped phase to a first velocity field data set; and outputting a resultant velocity field set based upon the first velocity field data set.