MRI K-Space Interpolation Using Undersampled Data and OMP Algorithm
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Solution Overview
Problem
Magnetic Resonance Imaging (MRI) systems face challenges in generating high-quality MR images in a short time due to limitations in acquiring k-space data efficiently, leading to incomplete or undersampled data sets.
Innovation Solution
An MRI apparatus and method that undersamples MR signals at multiple time points to acquire k-space data, with an image processor interpolating missing lines using weights calculated from center line data in adjacent k-spaces, applying the Orthogonal Matching Pursuit (OMP) algorithm to enhance data interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If undersampling is used to reduce acquisition time, then productivity is improved, but measurement precision deteriorates due to incomplete k-space data
Solution Approach 1:
The patent introduces an intermediary process (interpolation algorithm) that mediates between the undersampled data and the final image reconstruction. The image processor fills in missing k-space lines by interpolating from adjacent acquired lines, effectively bridging the gap between incomplete data and complete image reconstruction, thereby maintaining image quality despite reduced sampling
Solution Approach 2:
The patent creates copies of acquired k-space lines to reconstruct missing information. By copying and interpolating data from adjacent acquired lines into missing lines, the system generates synthetic data that replicates the information content of fully sampled data, enabling high-quality image reconstruction from undersampled measurements
2Loss of time
If undersampling is applied to multiple k-spaces, then loss of time is reduced, but loss of information increases due to missing data lines
Solution Approach 1:
The patent merges information from multiple undersampled k-spaces by combining their acquired lines and using interpolation to fill missing lines across all k-spaces. This merging process integrates partial information from different undersampled datasets to reconstruct complete k-space data, reducing information loss while maintaining reduced acquisition time
Solution Approach 2:
The patent performs preliminary interpolation of missing k-space lines before final image reconstruction. By pre-filling missing data using adjacent acquired lines and weighting algorithms, the system prepares complete k-space datasets in advance, ensuring no information is lost during the subsequent reconstruction process
Data Source
AI summary
Provided is a magnetic resonance imaging (MRI) apparatus. The MRI apparatus includes: a data acquisition unit configured to acquire a first k-space including a first missing line by undersampling an MR signal received from an object at a first time point, acquire a second k-space including a first acquired line corresponding to the first missing line by undersampling an MR signal received from the object at a second time point, and acquire a third k-space including a second acquired line corresponding to the first missing line by undersampling an MR signal received from the object at a third time point; and an image processor configured to interpolate data in the first missing line based on data in the first and second acquired lines.