Region-Optimized Virtual MRI Coils for ROI Signal Isolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional MRI systems often excite magnetization from regions not of immediate interest, leading to artifacts and inefficient data acquisition, as they do not discriminate between interesting and uninteresting spatial regions, complicating the design of efficient imaging methods.
Innovation Solution
The implementation of region-optimized virtual coils (ROVir) that suppress unwanted signals from uninteresting regions by applying beamforming techniques directly to raw multi-channel k-space data, optimizing signal-to-interference ratio through generalized eigenvalue decomposition, without modifying hardware or pulse sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI systems excite magnetization from all spatial regions, then complete coverage is achieved, but signal interference from unwanted regions increases and acquisition efficiency decreases
Solution Approach 1:
The patent applies local quality by creating virtual coils with spatially varying sensitivity profiles through linear combinations of physical coil signals. Each virtual coil is optimized to have high sensitivity in specific regions of interest and low sensitivity in unwanted regions, allowing selective suppression of interference from specific spatial locations while maintaining signal from desired areas.
Solution Approach 2:
The patent changes the parameter of coil sensitivity distribution by computing optimal linear combination weights using generalized eigenvalue decomposition. This mathematical transformation converts physical coil signals into virtual coil signals with optimized spatial sensitivity patterns, dynamically adjusting the effective sensitivity map to maximize signal-to-interference ratio for the region of interest.
2Measurement precision
If region-optimized virtual coils are implemented, then signal-to-interference ratio is optimized, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing the optimal linear combination weights through generalized eigenvalue decomposition during a calibration phase or initial setup. These pre-computed weights are then stored and applied during actual imaging, avoiding the need for real-time optimization calculations and enabling fast image reconstruction with optimized signal-to-interference ratios.
3Measurement precision
If beamforming techniques are applied to raw k-space data, then interference suppression is improved, but data processing time increases
Solution Approach 1:
The patent substitutes mechanical hardware modifications with computational beamforming techniques applied in the signal processing domain. Instead of physically modifying coils or pulse sequences, the method uses mathematical operations (linear combinations and generalized eigenvalue decomposition) on raw k-space data to achieve interference suppression, maintaining hardware simplicity while improving signal quality through software-based optimization.
Data Source
AI summary
Systems and methods of image reconstruction are provided. A system may have a memory and a processor to receive data corresponding to magnetic resonance imaging coils and data corresponding to a region of interest within a field of view of the magnetic resonance imaging machine. By determining different weights to associate with virtualized magnetic resonance imaging coils, images may be reconstructed to favor signals associated with a region of interest and to disfavor interference associated with areas outside the region of interest.