Nonlinear Phase Correction for MRI Velocity Measurement Accuracy
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Solution Overview
Problem
Accurate blood velocity measurement in cardiovascular Magnetic Resonance Imaging (MRI) is hindered by difficulties in calibration due to lack of stationary tissue near heart and great vessels, leading to errors and background phase bias from eddy-currents and noise, which conventional linear-fitting methods fail to adequately correct.
Innovation Solution
A self-calibrated, nonlinear phase correction method that uses a predetermined nonlinear term and additional basis functions like the concomitant field and eddy current field map, combined with iterative outlier removal and effect-size weighting to improve accuracy, preventing over-fitting and under-fitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional linear-fitting velocity correction is used, then the method is simple and easy to implement, but it results in under-fitting and fails to adequately correct background phase bias
Solution Approach 1:
The patent changes the mathematical model from linear fitting to nonlinear fitting by incorporating higher-order spatial basis functions. The phase correction model is extended to include quadratic and cubic terms (x², y², xy, x, y, constant) to capture the nonlinear background phase variations caused by eddy currents and scanner imperfections, thereby improving velocity measurement accuracy without requiring complex hardware modifications
Solution Approach 2:
The patent uses polynomial basis functions as mathematical copies or representations of the underlying physical phase distortion patterns. By fitting these predefined basis functions to the observed phase data in static tissue regions, the method creates a mathematical model that replicates the background phase behavior, enabling accurate correction while maintaining computational efficiency
2Measurement precision
If higher spatial orders of basis functions are used for fitting, then background phase bias correction may be improved, but over-fitting occurs due to lack of data points near vessels
Solution Approach 1:
The patent segments the image into two distinct regions: static tissue regions and vascular regions of interest. The fitting process is applied only to the static tissue regions where sufficient data points exist, while the vascular regions are protected from over-fitting by excluding them from the fitting calculation. This segmentation allows the use of higher-order basis functions without compromising the accuracy of velocity measurements in the vessels
Solution Approach 2:
The patent applies different processing strategies to different regions of the image. In static tissue regions, full nonlinear fitting is performed to accurately model background phase variations. In vascular regions, the fitted model is applied without additional fitting to preserve the true velocity signals. This local differentiation ensures that each region receives the appropriate level of correction without introducing artifacts
3Measurement precision
If no stationary tissue is available near heart and great vessels, then calibration reference is unavailable, but velocity offsets still need to be corrected
Solution Approach 1:
The patent enables the system to perform self-calibration by automatically identifying static tissue regions within the field of view and using them as reference points for determining background phase offsets. The method does not require external calibration phantoms or manual intervention to locate reference regions. The algorithm autonomously segments static tissue, fits the phase model, and applies corrections, making the calibration process self-sufficient and applicable to routine clinical scans without additional setup
Solution Approach 2:
The patent introduces polynomial basis functions as mathematical intermediaries that bridge the gap between the available static tissue phase data and the unknown background phase offsets in vascular regions. These basis functions serve as a mediating model that captures the spatial structure of phase distortions, allowing the system to infer calibration parameters from static tissue and apply them to correct velocity measurements in vessels where direct measurement is not possible
Data Source
AI summary
A system and method of self-calibrated correction for residual phase in phase-contrast magnetic resonance (PCMR) imaging data. The method includes receiving PCMR image data from an MR scanner system, segmenting static tissue from non-static cardiovascular elements of the image data, calculating a non-linear fitted-phase basis function, the non-linear fitted-phase basis function based on system artifacts of the PCMR system, adding the non-linear fitted-phase basis function to linear fit terms, and subtracting the result of the adding step from the PCMR imaging data. The system includes a PCMR scanning apparatus configured to provide PCMR image data, a scanner control circuit configured to control the scanning apparatus during image acquisition, the scanner control circuitry in communication with a control processor, the control processor configured to execute computer-readable instructions that cause the control processor to perform the method. A non-transitory computer-readable medium is also disclosed.