MRI K-Space Segment Merging for Artifact-Resistant Reconstruction
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Solution Overview
Problem
Existing magnetic resonance imaging (MRI) techniques using readout-segmented echo-planar imaging (RS-EPI) face challenges in accurately reconstructing images due to artifacts from susceptibility and B0 inhomogeneity, motion, and phase variations, which affect the quality and diagnostic accuracy of clinical MR images.
Innovation Solution
An iterative MRI reconstruction method that includes pre-processing each segment of K-space data using echo planar imaging (EPI) Nyquist ghost correction and parallel acquisition, followed by phase correction and synthesis, and iteratively merging segments using entropy minimization and k-space center correction to generate a complete K-space data set for improved image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel imaging techniques are used to reduce scan time, then productivity is improved, but measurement precision deteriorates due to undersampling of k-space data
Solution Approach 1:
The system uses auto-calibration signals to automatically determine the sensitivity profiles of receiver coils, providing feedback that enables accurate image reconstruction from undersampled data without manual calibration
Solution Approach 2:
The method performs self-calibration by incorporating calibration signals within the imaging sequence itself, eliminating the need for separate calibration procedures and enabling the system to automatically compensate for undersampling effects
2Loss of time
If k-space is undersampled to accelerate imaging, then scan time is reduced, but image quality deteriorates due to aliasing artifacts
Solution Approach 1:
Sensitivity profiles derived from auto-calibration signals serve as intermediary information that enables separation of aliased signals during image reconstruction, allowing high-quality images from undersampled data
Solution Approach 2:
The system performs preliminary auto-calibration to establish sensitivity profiles before reconstructing the final image, preparing necessary information in advance to handle undersampling effects
Data Source
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AI summary
Provided is an apparatus of reconstructing a magnetic resonance (MR) image, the apparatus including: a memory storing instructions; and at least one processor configured to execute the instructions to: obtain a plurality of segments of K-space data corresponding to a plurality of pulses which are applied to an object based on a pulse sequence; determine, based on radio frequency (RF) coils of the apparatus, a correction coefficient for merging the plurality of segments of K-space data; and generate a magnetic resonance (MR) image of the object by merging the plurality of segments of K-space data based on the determined correction coefficient.