MRI Phase Correction Using Position-Dependent Weighting
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Solution Overview
Problem
Conventional phase correction methods in MRI fail to effectively reduce phase differences between MR signal data with opposite polarities of the readout gradient magnetic field, leading to image degradation in regions of interest.
Innovation Solution
A phase correction method that involves acquiring k-space data in opposite readout directions, weighting real space data with varying coefficients based on pixel position, and calculating a correction amount to adjust the phase difference between data with opposite polarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional phase correction methods (multi-order phase correction or Ahn-Cho method) are used, then phase difference between MR signal data with opposite polarities is corrected, but phase correction fails in some cases causing image degradation in regions of interest
Solution Approach 1:
The patent applies local quality by using weighting coefficients that vary depending on pixel position in the readout direction. Regions with large phase difference variance are assigned lower weights while regions of interest maintain higher weights, enabling localized optimization of phase correction reliability without compromising image quality in diagnostic areas.
Solution Approach 2:
The patent changes the parameter of weighting coefficients from uniform to position-dependent values. By adjusting the weighting coefficient based on phase difference variance at each pixel position, the system adapts the phase correction process to local conditions, improving both reliability and image quality in regions of interest.
2Device complexity
If uniform weighting is applied to all real space data during phase correction, then processing is simple, but phase correction fails in regions with large phase difference variance
Solution Approach 1:
The patent replaces uniform weighting with position-dependent weighting coefficients that adapt to local phase difference characteristics. Each pixel position receives a weighting coefficient based on its phase difference variance, allowing the system to handle regions with large phase variations differently from stable regions, thereby improving phase correction success rate.
Solution Approach 2:
The patent introduces dynamic weighting coefficients that are calculated based on the phase difference variance at each pixel position. This dynamic adjustment allows the phase correction process to adapt to varying local conditions, improving reliability without requiring overly complex processing structures.
Data Source
AI summary
In one embodiment, a phase correction method comprising: acquiring first k-space data acquired in a first readout direction and second k-space data acquired in a second readout direction that is opposite to the first readout direction; weighting first real space data obtained from the first k-space data to generate first adjusted data with a predetermined weighting in which weight coefficients vary depending on a pixel position in a readout direction and become lower in a region where a variance of a phase difference is larger than a predetermined variance; weighting second real space data obtained from the second k-space data to generate second adjusted data with the predetermined weighting; calculating a correction amount for correcting a phase difference; and correcting a phase difference between data that are different from each other in polarity of a gradient pulse in the readout direction during acquisition, by using the correction amount.