Multi-stage MRI Reconstruction Reducing Interpolation Artifacts
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Solution Overview
Problem
Current MRI technologies face challenges in reducing scan time while maintaining image quality, particularly at high acceleration factors, as interpolation errors increase with more undersampled k-space data, leading to visible artifacts.
Innovation Solution
A multi-step magnetic resonance reconstruction process that involves acquiring an incomplete k-space data set, applying parallel imaging techniques like GRAPPA to fill in missing lines, and using a Fourier transform to generate a complete k-space data set, which can be further processed to produce high-quality images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the acceleration factor is increased to reduce scan time, then scan time is reduced, but interpolation error increases and image quality deteriorates
Solution Approach 1:
The patent divides the k-space reconstruction process into multiple sequential stages. First, a subset of k-space lines is acquired and processed through parallel imaging reconstruction to generate intermediate images. Then, additional k-space lines are acquired and processed to refine the reconstruction. This segmentation allows the system to achieve high acceleration factors while maintaining image quality by progressively filling in missing data rather than attempting to interpolate all missing lines simultaneously from a single undersampled dataset.
2Productivity
If fewer k-space lines are acquired to reduce scan time, then scan time is reduced, but data completeness decreases and artifacts increase
Solution Approach 1:
The patent performs preliminary parallel imaging reconstruction on a subset of acquired k-space lines to generate intermediate images before acquiring additional lines. This preliminary action allows the system to produce usable images from partial data while continuing to acquire more lines to reduce artifacts and improve quality, rather than waiting to acquire all lines before any reconstruction.
3Reliability
If GRAPPA is applied to highly undersampled data, then missing lines are interpolated, but interpolation error increases and visible artifacts appear
Solution Approach 1:
The patent implements a dynamic, multi-stage reconstruction process where the amount of acquired data and the reconstruction approach adapt as more lines become available. The system transitions from initial parallel imaging reconstruction on limited data to refined reconstruction as additional lines are acquired, allowing the reconstruction quality to improve dynamically rather than being fixed by the initial undersampling level.
Data Source
AI summary
A computer-implemented method for reconstruction of a magnetic resonance image includes acquiring a first incomplete k-space data set comprising a plurality of first k-space lines spaced according to an acceleration factor and one or more calibration lines. A parallel imaging reconstruction technique is applied to the first incomplete k-space data to determine a plurality of second k-space lines not included in the first incomplete k-space data set, thereby yielding a second incomplete k-space data set. Then, the parallel imaging reconstruction technique is applied to the second incomplete k-space data to determine a plurality of third k-space lines not included in the second incomplete k-space data, thereby yielding a complete k-space data set.