Iterative Reconstruction Kernel for MRI Phase Calibration
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Solution Overview
Problem
Existing MRI techniques struggle with accurately reconstructing images from k-space data, especially in low signal-to-noise ratio (SNR) conditions and high spatial resolution imaging, due to temporal fluctuations in relative phase differences between aliased slices, which affect the separation of signals in multiband and phase-encoding undersampling methods.
Innovation Solution
An iteratively calibrated reconstruction kernel is used to correct for relative phase shifts in multiband MRI systems, where an initial kernel is generated from k-space data, updated with phase-shifted values, and applied to produce unaliased images, accounting for changes in phase and motion variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional GRAPPA or slice-separation algorithms are used for multiband reconstruction, then reconstruction speed is improved, but image accuracy deteriorates in low SNR conditions due to temporal fluctuations in relative phase differences
Solution Approach 1:
The patent applies preliminary phase calibration by acquiring a separate calibration dataset to determine phase offsets between slices before performing the actual multiband reconstruction. This preliminary action separates the phase calibration step from the reconstruction step, allowing the reconstruction algorithm to use pre-determined phase information rather than attempting to resolve phase fluctuations during the reconstruction process itself, thereby maintaining both speed and accuracy
Solution Approach 2:
The patent implements dynamic phase correction by updating phase calibration information across multiple acquisitions. Instead of using a static phase correction, the system dynamically adjusts phase offsets based on calibration data acquired at different time points, accounting for temporal fluctuations in relative phase differences while maintaining reconstruction accuracy in low SNR conditions
2Productivity
If multiband acceleration is applied to reduce acquisition time, then productivity is improved, but signal-to-noise ratio deteriorates due to simultaneous excitation of multiple slices
Solution Approach 1:
The patent performs preliminary phase calibration using a dedicated calibration acquisition before the actual multiband imaging. This preliminary step characterizes the phase relationships between simultaneously excited slices, allowing subsequent reconstructions to compensate for phase-related SNR losses and maintain signal fidelity despite the acceleration
Solution Approach 2:
The system uses calibration data as feedback to inform the reconstruction process. By measuring phase offsets during calibration and feeding this information back into the reconstruction algorithm, the system can correct for SNR degradation caused by multiband excitation, effectively using the calibration feedback to maintain signal quality across accelerated acquisitions
3Loss of time
If phase-encoding undersampling is used to accelerate imaging, then acquisition time is reduced, but image quality deteriorates due to aliasing artifacts
Solution Approach 1:
The patent applies preliminary phase calibration to establish accurate phase relationships between slices before undersampled acquisition. This preliminary characterization enables the reconstruction algorithm to properly separate aliased signals from multiple slices, reducing aliasing artifacts and maintaining image quality despite the reduced sampling
Solution Approach 2:
The patent introduces phase calibration data as an intermediary element that mediates between the undersampled k-space data and the final image reconstruction. This intermediary calibration information provides the additional constraints needed to resolve aliasing artifacts that would otherwise be present due to phase inconsistencies, enabling accurate reconstruction from undersampled data
Data Source
AI summary
A method for iteratively calibrating a reconstruction kernel for use in accelerated magnetic resonance imaging (MRI) is provided. An MRI system is used to acquire k-space data from multiple slice locations following the application of a multiband radio frequency (RF) excitation pulse. An initial reconstruction kernel is generated from the acquired k-space data, and this initial reconstruction kernel is used to produce an initial image for each of the multiple slice locations by applying the initial reconstruction kernel to the acquired k-space data. The average phase of each slice location is then calculated from these images, and used to shift the phase values of the subsequently acquired k-space data. From the phase-shifted k-space data, an updated reconstruction kernel is then generated. This process is repeated iteratively until a stopping criterion is satisfied.