Iterative Multi-Shot MRI Reconstruction for Phase Correction
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Solution Overview
Problem
Current MRI techniques face challenges in acquiring high-resolution diffusion weighted data at high b-values, which results in decreased signal-to-noise ratio and image distortions due to phase inconsistencies between multiple shots and acquisitions, leading to signal loss and image quality issues.
Innovation Solution
The method involves acquiring MRI data in multiple shots and acquisitions, reconstructing and phase-correcting images separately, combining them to estimate full k-space data, and iteratively replacing unacquired data points to produce a hybrid k-space dataset, thereby correcting for phase inconsistencies and enhancing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple shots and acquisitions are used to acquire diffusion weighted MR data, then spatial resolution and b-value can be increased, but phase inconsistencies occur between shots and acquisitions leading to signal loss and image distortions
Solution Approach 1:
The patent segments the acquisition process into multiple shots and multiple acquisitions (NEX), where each shot acquires a portion of k-space. This segmentation allows high-resolution data collection while enabling separate phase correction processing for each segment, thereby maintaining image quality despite the divided acquisition approach.
Solution Approach 2:
The patent implements an iterative feedback mechanism where phase corrections are applied to images from each shot and NEX, then the corrected images are combined to estimate full k-space data. This estimated data feeds back into the next iteration for further refinement, continuously improving phase consistency and reducing distortions until convergence is achieved.
2Measurement precision
If multiple shots and acquisitions are used to acquire diffusion weighted MR data, then high b-values can be achieved, but signal-to-noise ratio decreases leading to signal loss
Solution Approach 1:
The patent merges data from multiple shots and multiple acquisitions by combining the phase-corrected images to estimate full k-space data. This combining process accumulates signal information across all shots and NEX, effectively increasing the signal-to-noise ratio while maintaining the high b-value capability necessary for diffusion weighting.
Solution Approach 2:
The patent applies phase corrections preliminarily to each shot and NEX before combining them. This preliminary phase correction prevents phase inconsistencies from causing signal cancellation during combination, thereby preserving signal intensity and improving the overall signal-to-noise ratio in the final reconstructed image.
3Device complexity
If conventional techniques are used to process multi-shot multi-acquisition data, then processing is simpler, but phase inconsistencies cause image distortions and reduced image quality
Solution Approach 1:
The patent employs iterative feedback processing where phase corrections are applied, images are combined, full k-space data is estimated, and the process repeats with the estimated data feeding back into the next iteration. This feedback loop systematically reduces phase inconsistencies and improves image quality, with the iteration count being adjustable to balance processing complexity and image quality requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the production of high-resolution diffusion images with improved signal-to-noise ratio and reduced distortions, facilitating diagnostic utility in fields like oncology and neuroscience by effectively combining multi-shot and multi-acquisition data.
Implementation Method 1
magnetic resonance imaging (MRI) examinations are based on the interactions among a primary magnetic field, a radiofrequency (RF) magnetic field, and time varying magnetic gradient fields with gyromagnetic material having nuclear spins within a subject of interest
Implementation Method 2
The precession of spins of these nuclei can be influenced by manipulation of the fields to produce RF signals that can be detected, processed, and used to reconstruct a useful image
Data Source
AI summary
A magnetic resonance (MR) imaging method performed by an MR imaging system includes acquiring MR data in multiple shots and multiple acquisitions (NEX), separately reconstructing the component magnitude and phase of images corresponding to the multiple shots and multiple NEX, removing the respective phase from each of the images, and combining, after removal of the respective phase, the shot images and the NEX images to produce a combined image. The method further includes using the combined image to calculate the full k-space data for each shot and NEX and replacing unacquired k-space data points with calculated k-space data points. The operations are repeated until the combined image reaches a convergence.