Direct Inversion Reconstruction for MRI Phase Velocity
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Solution Overview
Problem
Current phase-based dynamic magnetic resonance imaging (MRI) techniques face challenges in achieving high temporal resolution without introducing artifacts, particularly due to the assumption that velocities do not change significantly over consecutive measurements, leading to oscillatory distortions and loss in temporal resolution.
Innovation Solution
The Direct Inversion Reconstruction (DiR) method processes all MRI measurements across time jointly, using a regularized least squares approach to estimate phase and velocity, and an adaptive version (DiRa) adjusts regularization weights for each velocity component based on its temporal frequency content, allowing for more accurate and stable recovery of phase-based signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sliding window approach is used to improve temporal resolution, then the apparent resolution is improved, but strong oscillatory artifacts are introduced
Solution Approach 1:
The patent implements a dynamic windowing approach where the window length and step size are adaptively adjusted based on the local temporal frequency content of the velocity signal. This allows the reconstruction to accommodate rapid velocity changes without introducing artifacts, resolving the contradiction between high temporal resolution and artifact suppression.
Solution Approach 2:
The patent changes the parameters of the reconstruction process by using variable window lengths and step sizes instead of fixed parameters. This allows the system to optimize temporal resolution in regions of rapid change while maintaining stability in regions of slow change, thereby eliminating oscillatory artifacts.
2Loss of time
If four adjacent frames are processed together to estimate phase, then the acquisition time is saved, but the temporal resolution is reduced
Solution Approach 1:
The patent segments the processing into multiple passes: first processing four adjacent frames together to obtain a coarse phase estimate that saves acquisition time, then applying a refinement step using the adaptive windowing approach to recover high temporal resolution without increasing the fundamental acquisition time.
Solution Approach 2:
The patent maintains continuous processing by using an overlapping window approach where windows advance by a step of one frame instead of four, allowing reconstruction at a temporal grid four times finer while still processing data in efficient batches to minimize acquisition time.
3Device complexity
If the assumption that velocities do not change significantly is applied, then the processing is simplified, but the method fails when velocities change rapidly
Solution Approach 1:
The patent replaces the static assumption with a dynamic model that adapts to local velocity变化情况. The adaptive windowing approach automatically adjusts to rapid velocity changes by using shorter windows in regions of high temporal frequency, maintaining reliability without excessive processing complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the estimated velocity signal is analyzed to detect regions of rapid change, and the processing parameters are adjusted accordingly. This feedback loop ensures accuracy under rapid velocity changes while keeping the overall processing complexity manageable through selective application of refined processing only where needed.
Data Source
AI summary
MRI techniques seek to simultaneously measure physiological parameters in multiple directions, requiring the application of multiple encoding magnetic field gradient waveforms. The use of multiple encoding waveforms degrades the temporal resolution of the measurement, or may distort the results depending on the methodology used to derive physiological parameters from the measured data. The disclosed Direct Inversion Reconstruction Method (DiR) provides distortion-free velocity images with high temporal resolution, without changing the method of acquiring the phase data. The disclosed method provides a more stable and accurate recovery of phase-based dynamic magnetic resonance signals with higher temporal resolution than current state-of-the-art methods.