Self-Navigated Segmented EPI Phase Correction
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Solution Overview
Problem
The sensitivity of EPI to field variations reduces the effectiveness of segmented MRI acquisitions, and the need for additional measurements to correct physiologically induced phase variations decreases efficiency by 30-50% and is SNR dependent.
Innovation Solution
A method for reconstructing MRI images by estimating reference phase maps for k-space segments, calculating phase difference values, and applying these to generate phase-corrected k-space data, eliminating the need for separate navigator scans and enabling self-navigation for correcting physiological changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If segmented EPI acquisition is used to improve imaging efficiency and coverage, then productivity increases, but manufacturing precision deteriorates due to phase corruptions from physiological changes
Solution Approach 1:
The patent implements self-navigation where the k-space data itself is used to estimate phase variations and generate correction factors. The method extracts navigational information directly from the segmented k-space segments without requiring external navigator signals, allowing the system to self-correct physiological phase variations while maintaining high imaging efficiency
Solution Approach 2:
The patent employs feedback by using the acquired k-space data to estimate phase variations, calculate correction factors, and apply these corrections back to the same data. This closed-loop approach continuously monitors and corrects physiological phase variations during the segmented EPI acquisition, ensuring image quality while maintaining productivity
2Manufacturing precision
If 2D navigator scans are added to correct phase variations, then manufacturing precision improves, but productivity deteriorates due to 30-50% efficiency loss
Solution Approach 1:
The patent merges the navigator function with the main imaging acquisition by using the same k-space segments for both imaging and phase estimation. Instead of separate navigator scans, the method combines navigational data extraction with the primary imaging data acquisition, eliminating the 30-50% efficiency loss while maintaining phase correction capability
Solution Approach 2:
The patent makes the k-space segments serve multiple functions: they simultaneously provide the primary imaging data and the navigational information needed for phase correction. This multi-functional approach eliminates the need for dedicated navigator scans, maintaining both image quality and acquisition efficiency
3Area of stationary object
If segmented EPI is used to increase FOV coverage, then area increases, but manufacturing precision deteriorates due to accumulated phase errors across segments
Solution Approach 1:
The patent divides the large FOV acquisition into multiple segmented k-space segments that can be independently phase-corrected. Each segment is processed individually to estimate and correct its specific phase variations, preventing the accumulation of phase errors across segments while maintaining comprehensive FOV coverage
Solution Approach 2:
The patent applies local phase correction by estimating phase variations specific to each k-space segment and applying segment-dependent correction factors. This localized approach ensures that phase consistency is maintained within each segment while accommodating the large overall FOV coverage, preventing accumulated phase errors
Data Source
AI summary
Magnetic resonance imaging (“MRI”) data are corrected from corruptions due to physiological changes using a self-navigated phase correction technique. Unlike motion correction techniques, the effects of physiological changes (e.g., breathing and respiration) are corrected by making the MRI data self-consistent relative to an absolute uncorrupted phase reference. This phase correction information can be extracted from the acquisition itself, thereby eliminating the need for a separate navigator scan, and establishing an accelerated acquisition. This absolute reference can be computed in a data segmented space, and the subsequent data can be corrected relative to this absolute reference with low-resolution phases.