N+1D Reconstruction Kernel for MR Parallel Imaging
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Solution Overview
Problem
Current parallel imaging methods in magnetic resonance tomography (MRT) face challenges with increasing reconstruction artifacts and a drastic drop in signal-to-noise ratio (SNR) as reduction factors increase, particularly with eight coil elements, limiting their use in clinical applications.
Innovation Solution
The method employs N+1-dimensional reconstruction kernels with a standard geometry that includes all spatial dimensions and the temporal domain, allowing for the reconstruction of all missing data points within a target area using a single kernel geometry, which is then shifted to reconstruct successive target areas, thereby minimizing computation time and artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel imaging with increasing reduction factors is used to accelerate data acquisition, then productivity is improved, but manufacturing precision deteriorates due to increased reconstruction artifacts and decreased signal-to-noise ratio
Solution Approach 1:
The patent extends the reconstruction kernel from traditional 2D spatial dimensions to N+1 dimensions by incorporating temporal information. This dimensional extension allows the kernel to utilize both spatial and temporal correlations, enabling higher reduction factors while maintaining image quality through improved exploitation of available data across multiple time points
Solution Approach 2:
The patent combines spatial and temporal information within a unified N+1 dimensional reconstruction kernel. By merging data from adjacent temporal measurements with spatial encoding information, the method creates a more comprehensive data structure that improves reconstruction accuracy and maintains signal-to-noise ratio even at higher reduction factors
2Productivity
If multiple kernels with different geometries are used for reconstruction as in kt-GRAPPA, then productivity is improved through temporal information utilization, but device complexity increases leading to systematic errors and longer computation times
Solution Approach 1:
The patent employs a single universal N+1 dimensional reconstruction kernel geometry that can be applied across all temporal and spatial dimensions. This universal kernel eliminates the need for multiple different kernel geometries, reducing computational complexity while maintaining the ability to utilize temporal information for accelerated imaging
Solution Approach 2:
The patent changes the parameters of the reconstruction kernel by extending it to N+1 dimensions and applying it consistently across all target areas. By standardizing the kernel geometry and adjusting its dimensional parameters rather than using multiple different geometries, the method reduces computational overhead and systematic errors while maintaining productivity benefits
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 optimizes the signal-to-noise ratio and reduces reconstruction artifacts, enabling the use of higher reduction factors and shorter computation times, resulting in improved image quality even with higher reduction factors compared to conventional methods.
Implementation Method 1
Acquisition of MR signals from a sample volume by parallel imaging using multiple receiver coils
Data Source
AI summary
A method for time-resolved imaging of N-dimensional magnetic resonance (=MR) with the following steps:Acquisition of MR signals from a sample volume by parallel imaging, wherein N-dimensional data matrices (M1, M2, . . . MNt) in k-space is acquired undersampled from each receiver coil, wherein the acquisition of the MR signals is performed according to an acquisition scheme that is periodic over time and describes the time sequence of the undersampled data matrices (M1, M2, . . . Mn) andreconstruction of missing data points (FP) of the acquisition scheme using a set of coil weighting factors (CW, <CW>) and using N+1-dimensional reconstruction kernels (RK, RK′ RK″) is characterized in that reconstruction of the missing data points (FP) is performed using a single reconstruction geometry, wherein each reconstruction kernel comprises an (N+1)-dimensional target area (TB), wherein all non-acquired data points (TP) are reconstructed within the associated target area (TB) using each reconstruction kernel (RK, RK′ RK″), and wherein the target area (TB) exhibits at least the extent (nR×mR) in the ky-t plane of the acquisition scheme. This can shorten the computation time for reconstruction and reduce reconstruction artifacts and optimize the signal-to-noise ratio.