Propeller MR Image Reconstruction via Blade Rotation and Interpolation
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Solution Overview
Problem
Current image reconstruction methods for the propeller technique in magnetic resonance tomography (MRT) are complex and computationally intensive, making them impractical for routine use, especially due to the need for large processing requirements and the inability to efficiently handle non-Cartesian k-space sampling.
Innovation Solution
A method that involves rotating and interpolating sub-data sets to align with a Cartesian final grid, followed by fast Fourier transformation, allowing for simpler and quicker image reconstruction by compensating for local sample density and eliminating the need for complex deconvolution steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-Cartesian k-space sampling with propeller technique is used, then movement correction capability is improved, but image reconstruction complexity increases
Solution Approach 1:
The k-space data is divided into multiple blades (segments), each of which is independently rotated and processed. This segmentation allows movement correction to be applied to each blade separately while simplifying the overall reconstruction process by treating each segment independently before combining them.
Solution Approach 2:
Instead of directly transforming non-Cartesian sampled data using complex methods like gridding or direct Fourier transformation, the patent inverts the approach by rotating each blade's data onto a Cartesian grid first, then applying standard FFT. This inverse approach simplifies the reconstruction by converting the problem into a form that can be solved with conventional methods.
2Measurement precision
If direct Fourier transformation method is used, then image reconstruction precision is improved, but processing time increases
Solution Approach 1:
The patent inverts the conventional approach by first rotating the non-Cartesian sampled data onto a Cartesian grid, then applying standard FFT instead of using computationally intensive direct Fourier transformation methods. This maintains precision while dramatically reducing processing time.
Solution Approach 2:
The patent creates a copied and transformed version of the original non-Cartesian data by rotating each blade onto a Cartesian grid. This copied data structure enables the use of efficient FFT algorithms while preserving the essential information needed for accurate image reconstruction.
3Productivity
If gridding method is used, then image reconstruction speed is improved, but image quality deteriorates due to deconvolution requirements
Solution Approach 1:
Instead of using gridding methods that require deconvolution and can degrade image quality, the patent inverts the approach by rotating the data onto a Cartesian grid first. This eliminates the need for deconvolution steps while maintaining both speed and image quality.
Solution Approach 2:
The patent extracts and removes the problematic deconvolution step from the reconstruction process by using blade rotation onto a Cartesian grid. This extraction eliminates the source of image quality deterioration while preserving the computational efficiency needed for practical clinical use.
4Ease of operation
If Cartesian sampling is used, then image reconstruction simplicity is improved, but movement correction capability worsens
Solution Approach 1:
The patent segments the k-space data into multiple blades, allowing movement correction to be applied to each segment independently. This segmentation enables the preservation of Cartesian sampling simplicity while recovering movement correction capability that would be lost with standard Cartesian grid sampling.
Solution Approach 2:
The patent introduces dynamic rotation of static blades to align with the Cartesian grid. This dynamic adjustment allows the system to maintain the simplicity of Cartesian sampling and FFT while incorporating movement correction information that would otherwise be unavailable in standard Cartesian sampling.
Data Source
AI summary
In a magnetic resonance (MR) image reconstruction method and MR apparatus, for raw MR data are acquired with the propeller technique, and k-space sampling ensues in sub-data sets. The sample points of each sub-data set correspond to grid points of a Cartesian initial grid and the Cartesian initial grid of the sub-data sets can be brought into congruence by rotation: A Cartesian final grid is selected, and a calculation-based transfer of the data points of each sub-data set ensues to a respective new grid that exhibits the orientation of the respective sub-data set and the grid constants of the final grid, if the grid constants of the output grid differ from those of the final grid. A calculation-based transfer of the data points of each sub-data set, or the data points of a respective new grid (if obtained) ensures to the final grid by the application of a rotation module followed by transformation of the acquired data into the image domain.