Accelerated MRF Reconstruction via Compressed Dictionary Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Magnetic resonance fingerprinting (MRF) techniques face challenges in achieving high-quality image reconstruction and reducing reconstruction time due to computationally expensive iterative processes, particularly in dictionary search and signal comparison steps, which are inefficient with undersampled data.
Innovation Solution
The method employs accelerated iterative reconstruction for MRF (AIR-MRF) by using a compressed dictionary and singular value decomposition (SVD) to reduce computation time, along with a k-d tree search algorithm to efficiently locate approximate magnetic resonance fingerprints, thereby accelerating the image generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative dictionary search and signal comparison steps are performed to achieve accurate magnetic resonance parameter retrieval, then measurement precision is improved, but loss of time increases due to computationally expensive repetitive operations
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing simulated magnetic resonance signals for various parameter combinations in a dictionary before the actual imaging process. This pre-computed dictionary enables faster matching during reconstruction without sacrificing accuracy, as the computationally intensive simulations are performed in advance rather than during the iterative matching process.
Solution Approach 2:
The patent uses copying by creating a dictionary of pre-simulated magnetic resonance fingerprints that replicate the characteristics of actual tissue responses. Instead of performing repeated simulations during iterative reconstruction, the system copies these pre-computed fingerprints into a lookup dictionary, allowing rapid comparison and matching against acquired signals while maintaining parameter retrieval accuracy.
2Measurement precision
If a comprehensive dictionary of simulated responses is used to ensure accurate matching, then measurement precision is improved, but device complexity increases due to large data storage and processing requirements
Solution Approach 1:
The patent applies taking out by extracting only the most relevant features and parameters from the full simulated signal responses for storage in the dictionary. Rather than storing complete time-series data for every possible parameter combination, the system extracts key characteristic values that maintain matching accuracy while significantly reducing the dimensionality and storage requirements of the dictionary.
Solution Approach 2:
The patent applies parameter changes by transforming the stored dictionary data from raw time-domain signals into frequency-domain representations or other compressed forms. This transformation changes the parameter representation to enable more efficient storage and faster comparison operations, reducing computational complexity while preserving the ability to accurately retrieve magnetic resonance parameters.
3Loss of time
If undersampled k-space signals are used to reduce acquisition time, then loss of time is reduced, but measurement precision deteriorates due to insufficient data for accurate dictionary matching
Solution Approach 1:
The patent applies feedback by implementing an iterative reconstruction process where initial parameter estimates are obtained from undersampled data, then used to guide subsequent refinement steps. The system continuously compares reconstructed images with the acquired undersampled signals and adjusts parameters iteratively, using feedback from each iteration to improve accuracy despite the limited initial data availability.
Solution Approach 2:
The patent applies preliminary action by performing preliminary reconstruction steps using the undersampled data to obtain initial parameter estimates before proceeding to more accurate refinement. This preliminary processing extracts usable information from the limited data, establishing a starting point that guides subsequent iterative improvement without requiring complete data acquisition first.
Data Source
AI summary
Disclosed herein is a method obtaining a magnetic resonance image of an object, comprising obtaining a first time evolution signal from a magnetic resonance signal from the object; performing a search of a compressed dictionary of magnetic resonance fingerprints to select a magnetic resonance fingerprint representative of the first time evolution signal, wherein the selected magnetic resonance fingerprint is an exact or approximate nearest neighbor match to the first time evolution signal; obtaining a magnetic resonance parameter associated with the selected fingerprint; generating the magnetic resonance image of the object from the obtained magnetic resonance parameter; and performing a second search of the compressed dictionary using the magnetic resonance image.