Coil Selection for Parallel MRI Using SVD
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Solution Overview
Problem
The use of large coil arrays in magnetic resonance imaging (MRI) systems leads to memory storage issues and increased reconstruction times due to the need to process data from numerous coil elements, despite advancements in parallel imaging techniques.
Innovation Solution
A method for selecting a subset of coil elements based on determining a coil sensitivity matrix and a projection matrix, which projects the sensitivity matrix onto virtual coil elements, allowing for automatic selection of the most significantly contributing physical coil elements for improved MRI scans, using singular value decomposition (SVD) for efficient factorization and noise correlation matrix consideration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of coil elements are used in the coil array, then signal to noise ratio and imaging performance are improved, but memory storage requirements and reconstruction time increase
Solution Approach 1:
The patent extracts and utilizes only the most significant coil elements for imaging a specific region of interest. By calculating contribution metrics for each coil element and selecting only those above a threshold value, the system removes unnecessary coil data from processing, thereby reducing memory storage requirements and reconstruction time while preserving the essential signal quality needed for high SNR imaging.
Solution Approach 2:
The patent applies local quality by optimizing coil element selection specifically for the region of interest rather than uniformly processing all coil elements across the entire field of view. The contribution calculation is performed locally for each ROI, allowing the system to adaptively select coil elements based on their actual contribution to the specific imaging region, thus reducing overall computational burden while maintaining local signal quality.
2Measurement precision
If a large number of coil elements are used in the coil array, then signal to noise ratio and imaging performance are improved, but memory storage requirements increase
Solution Approach 1:
The patent extracts and utilizes only the most significant coil elements for imaging a specific region of interest. By calculating contribution metrics for each coil element and selecting only those above a threshold value, the system removes unnecessary coil data from processing, thereby reducing memory storage requirements and reconstruction time while preserving the essential signal quality needed for high SNR imaging.
3Quantity of substance
If data reduction techniques are applied to reduce memory load, then memory storage requirements are reduced, but image quality and signal to noise ratio may deteriorate
Solution Approach 1:
The patent changes the parameter selection criteria by introducing a contribution-based threshold mechanism. Instead of uniformly reducing data or using fixed coil subsets, the system dynamically determines which coil elements contribute significantly to each region of interest and selects them based on calculated contribution metrics. This parameter-driven approach ensures that data reduction does not compromise image quality, as only coil elements that would meaningfully degrade image quality are excluded.
Solution Approach 2:
The patent implements feedback by calculating the contribution of each coil element to the region of interest and using this information to guide the selection process. The system evaluates coil element contributions, compares them against thresholds, and adjusts the selected coil subset accordingly. This feedback mechanism ensures that the reduced coil set maintains optimal image quality by retaining only those elements that contribute significantly to the imaging task.
4Adaptability or versatility
If manual coil element selection is performed, then flexibility in optimizing for specific regions of interest is improved, but workflow efficiency and productivity decrease
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform coil element selection based on calculated contribution metrics. The system autonomously evaluates each coil element's contribution to the region of interest, applies selection criteria, and determines the optimal coil subset without requiring manual user intervention. This automation maintains the adaptability and flexibility of optimized coil selection while dramatically improving workflow efficiency and productivity.
Data Source
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AI summary
The invention relates to a method of selecting a set of coil elements from a multitude of physical coil elements comprised in a coil array for performing a magnetic resonance imaging scan of a region of interest.