Hierarchical Mapping Framework for MRI Coil Compression
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Solution Overview
Problem
Current coil compression techniques for MRI systems face challenges in reducing computational burden with larger coil arrays, particularly in non-Cartesian sampling patterns, leading to loss of signal-to-noise ratio (SNR) at high compression rates.
Innovation Solution
A hierarchical mapping framework (HMF) for coil compression that organizes receive channels based on correlation strength, creating subgroups and virtual channels using hierarchically semiseparable channel mixing, allowing for efficient reconstruction and SNR retention across various sampling patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If global mapping methods such as SVD compression are used for coil compression, then a wide range of k-space sampling patterns can be supported, but signal-to-noise ratio is lost at high compression rates
Solution Approach 1:
The patent segments the receive coil channels into multiple subgroups based on correlation strength. Each subgroup is processed independently through separate compression operations, allowing the system to maintain signal-to-noise ratio by preserving correlated channel information within subgroups while achieving overall compression across the full coil array.
2Productivity
If larger coil arrays are used for parallel imaging, then greater acceleration and signal-to-noise ratio are achieved, but computational cost increases with the square of the number of channels
Solution Approach 1:
The patent divides the large coil array into multiple subgroups of channels. By performing compression operations on smaller subgroups rather than the entire coil array, the computational complexity is reduced from O(N²) to O((N/k)² × k) where N is the total number of channels and k is the number of subgroups, thereby reducing overall computational burden while maintaining acceleration capability.
Solution Approach 2:
The patent introduces a hierarchical dimension to the compression process by organizing channels into subgroups and applying compression at multiple levels. This hierarchical approach transforms the single-stage compression problem into a multi-stage process, reducing the dimensionality of each compression operation and thereby reducing computational complexity.
3Loss of time
If the number of receive channels is reduced to decrease computational burden, then reconstruction time is reduced, but signal-to-noise ratio deteriorates
Solution Approach 1:
The patent performs preliminary organization of receive channels into subgroups based on correlation strength before the compression operation. This preliminary action identifies and preserves the most important correlated channel relationships, ensuring that when channels are reduced for faster reconstruction, the signal-to-noise ratio is maintained by keeping channels that are strongly correlated and thus contain redundant useful information.
Data Source
AI summary
Systems and methods for a hierarchical mapping framework (“HMF”} for coil compression are provided. The HMF-based coil compression can be applied to existing coil compression algorithms to improve their performance. The receive channels associated with a coil array are divided into subgroups based on the strength of their mutual correlation. In each subgroup, one or more virtual channels are produced based on the channels not in the subgroup. The virtual channels are produced using a coil compression algorithm subject to a hierarchically semiseparable channel mixing across the subgroups. Images are reconstructed for the subgroups and then combined to produce the final image of the subject.