MRI K-space Data Interpolation for Artifact Reduction
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Solution Overview
Problem
Magnetic resonance imaging (MRI) systems face challenges in generating high-quality images due to undersampling in k-space data, leading to artifacts and reduced reconstruction accuracy when signal intensity varies significantly over time, especially in techniques like parallel imaging and k-t SENSE.
Innovation Solution
The MRI system generates additional k-space data to be added to undersampled data, ensuring continuity of signal intensity functions, thereby preventing artifacts and improving reconstruction accuracy by applying Fourier transforms and inverse transforms in specific directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If k-space data is undersampled to reduce acquisition time, then imaging speed is improved, but reconstruction accuracy deteriorates due to artifacts
Solution Approach 1:
The patent applies preliminary action by generating additional k-space data before the actual imaging acquisition. Specifically, it creates virtual k-space data points by interpolating between existing sampled points, preparing a complete k-space dataset in advance that can be used for accurate reconstruction without requiring full sampling during acquisition.
Solution Approach 2:
The patent uses copying by creating virtual copies of k-space data points through interpolation. Instead of acquiring every point directly, it generates synthetic k-space data points by copying and interpolating information from existing sampled points, thereby reconstructing the complete k-space data without full acquisition.
2Manufacturing precision
If additional k-space data is generated through interpolation, then reconstruction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the k-space data into discrete points and processing them individually through interpolation. Instead of handling the entire k-space dataset as one complex operation, it segments the data into individual points that can be interpolated separately using predefined patterns, reducing overall computational complexity.
Solution Approach 2:
The patent uses parameter changes by varying interpolation parameters such as the number of virtual points to generate and the interpolation order. By adjusting these parameters, the system can optimize the balance between reconstruction accuracy and computational complexity, selecting appropriate parameter values for different imaging scenarios.
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 enhances image quality by maintaining signal intensity continuity, reducing artifacts, and improving reconstruction accuracy in MRI systems, especially during time-series imaging.
Implementation Method 1
applies Fourier transform to the k-space data so as to produce magnetic resonance (MR) images
Implementation Method 2
generates an MR image group by performing a reconstruction process on the third k-space data group
Data Source
AI summary
A magnetic resonance (MR) imaging apparatus of embodiments includes processing circuitry. The processing circuitry generates a third k-space data group including a first k-space data group and a second k-space data group, by adding the second k-space data group that is arranged in a second range adjacent to a first range, to the first k-space data group that is arranged in the first range and that is undersampled along at least one of the axes in k-space as well as in any axis that is different from the axes in the k-space. The processing circuitry generates an MR image group by performing a reconstruction process on the third k-space data group.