Through-time Non-Cartesian GRAPPA Calibration for MRI
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Solution Overview
Problem
Conventional non-Cartesian GRAPPA techniques face limitations in high acceleration factors, leading to artifacts and sub-optimal results due to assumptions about closely spaced acquisition paths, which break down at larger distances and edge regions of k-space, limiting the maximal undersampling possible.
Innovation Solution
Through-time calibration for non-Cartesian GRAPPA, where calibration data is acquired at different points in time to derive exact reconstruction kernels for each acquisition path element, allowing for accurate reconstruction even at higher acceleration factors by ensuring multiple copies of calibration data are acquired in the same configuration as reconstruction elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional non-Cartesian GRAPPA techniques are used with high acceleration factors, then acquisition speed is improved, but image quality deteriorates due to artifacts and sub-optimal reconstruction results
Solution Approach 1:
The patent applies preliminary action by acquiring calibration data at multiple time points before the actual imaging acquisition. This through-time calibration process prepares accurate GRAPPA weights in advance, which are then used during the high-speed undersampled acquisition to maintain image quality while achieving high acceleration factors
Solution Approach 2:
The patent changes the parameter of calibration data acquisition by collecting calibration data at multiple different time points rather than at a single time point. This temporal diversification of calibration data allows the system to adapt to time-varying conditions and maintain reconstruction accuracy at high acceleration factors
2Device complexity
If conventional GRAPPA assumes closely spaced acquisition paths, then reconstruction is simpler, but accuracy deteriorates at larger distances and edge regions of k-space
Solution Approach 1:
The patent applies local quality by computing separate GRAPPA weights for different regions of k-space, particularly for edge regions and centers of spirals, rather than using a single global set of weights. This region-specific weighting improves reconstruction accuracy in areas where the closely-spaced assumption breaks down
Solution Approach 2:
The patent segments the k-space into different regions (edge regions, center regions, different spiral interleaves) and applies different calibration strategies to each segment. This segmentation allows the system to handle the varying accuracy requirements of different k-space regions independently
3Loss of time
If under-sampled radial or spiral acquisition is used, then scan time is reduced, but missing acquisition path elements cannot be accurately reconstructed when rays or spirals are far apart
Solution Approach 1:
The patent performs preliminary calibration at multiple time points to capture the system's behavior under different conditions. These pre-acquired calibration data sets enable reliable reconstruction of missing path elements even when the undersampling factor is high and rays or spirals are far apart
Solution Approach 2:
The patent creates multiple copies of calibration data at different time points and uses these copies to derive robust GRAPPA weights. By having multiple temporal copies of calibration information, the system can reliably reconstruct missing data even when spatial copies (neighboring rays/spirals) are far apart
Data Source
AI summary
Example systems and methods control a parallel magnetic resonance imaging (pMRI) apparatus to acquire non-Cartesian (e.g., spiral) calibration data sets throughout time. Example systems and methods also control the pMRI apparatus to acquire an under-sampled non-Cartesian data set from the object to be imaged. Example systems and methods then control the pMRI apparatus to reconstruct an image of the object to be imaged from the under-sampled non-Cartesian data set. The reconstruction depends, at least in part, on a through-time non-Cartesian GRAPPA calibration where a value for a point missing from k-space in the under-sampled non-Cartesian data set is computed using a GRAPPA weight set calibrated and applied for the missing point. The GRAPPA weight set is computed from data in the non-Cartesian calibration data sets.