NMR Fingerprinting SVD Dictionary Compression
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Solution Overview
Problem
Conventional magnetic resonance fingerprinting (MRF) is computationally intensive due to full template matching between acquired signals and a high-dimensional dictionary, leading to significant processing time, which hinders efficient characterization of tissue parameters like T1, T2, and off-resonance.
Innovation Solution
Applying singular value decomposition (SVD) to compress the MRF dictionary, reducing its size in the time domain and performing template matching in a lower-dimensional SVD space, thereby accelerating pattern recognition without compromising signal-to-noise ratio (SNR).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full template matching is performed between acquired signals and high-dimensional MRF dictionary, then measurement precision of tissue parameters is improved, but processing time increases significantly
Solution Approach 1:
The patent segments the high-dimensional MRF dictionary into multiple sub-dictionaries based on tissue type or parameter ranges. Instead of performing template matching against the entire high-dimensional dictionary, the system divides the search space into smaller, manageable segments, reducing computational complexity while maintaining measurement precision for tissue parameters like T1, T2, and off-resonance.
Solution Approach 2:
The patent transforms the high-dimensional template matching problem into a lower-dimensional search space by introducing additional classification dimensions. The system first classifies tissue types or parameter ranges, then performs template matching within reduced sub-spaces, effectively changing the dimensionality of the search problem from O(N) to O(k*N) where k << 1, thereby reducing processing time while preserving measurement accuracy.
2Illumination intensity
If conventional pulse sequences are used to produce T1-weighted and T2-weighted signals, then qualitative image contrast is improved, but the ability to objectively quantify tissue parameters deteriorates
Solution Approach 1:
The patent changes the approach from acquiring qualitative images with fixed weightings to acquiring quantitative signal evolutions by varying multiple parameters simultaneously (flip angle, echo time, repetition time). Instead of designing separate pulse sequences for T1 and T2 weighting, the system uses a single MRF sequence that modulates parameters over time to encode multiple tissue properties into a single signal evolution, enabling objective quantification while maintaining contrast information.
Solution Approach 2:
The patent makes the MRF pulse sequence universal by designing it to simultaneously encode multiple tissue parameters (T1, T2, off-resonance, proton density) in a single acquisition. The varied sequence blocks serve multiple functions: they provide contrast differentiation, encode temporal signal evolution, and enable parameter mapping, replacing the need for multiple specialized conventional sequences and enabling automated quantitative analysis.
3Measurement precision
If MRF stores a large set of known evolutions in a dictionary for comprehensive tissue characterization, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the comprehensive MRF dictionary into smaller, organized sub-dictionaries based on tissue type, parameter ranges, or acquisition conditions. This segmentation reduces the effective dictionary size for each template matching operation while maintaining comprehensive coverage across all possible tissue characteristics. The system can selectively load or search only relevant sub-dictionaries based on preliminary tissue classification.
Solution Approach 2:
The patent performs preliminary tissue classification or parameter estimation before performing full template matching against the complete MRF dictionary. By using simplified metrics or reduced-dimensional features in a preliminary step, the system identifies likely tissue types or parameter ranges, then performs detailed matching only against relevant dictionary entries, effectively reducing the search space and computational complexity while maintaining measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces processing time for retrieving MR parameters, achieving comparable accuracy to full template matching while processing MR parameters up to ten times faster with SVD compression and template matching.
Implementation Method 1
Nuclear magnetic resonance (NMR) fingerprinting with singular value decomposition (SVD) compression... Magnetic resonance fingerprinting (MRF) employs a series of varied sequence blocks that produce different signal evolutions in different resonant species (e.g., tissues) to which radio frequency (RF) energy is applied
Data Source
AI summary
Apparatus, methods, and other embodiments associated with NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that are associated with different points in the (k, t, E) space. Sampling is performed with t and/or E varying in a non-constant way. The varying parameters may include flip angle, echo time, RF amplitude, and other parameters. The NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals, and a characterization logic that characterizes a resonant species in the object as a result of comparing acquired signals to reference signals. The reference signals may be stored in a dictionary. Singular value decomposition may be applied to the dictionary and the acquired signals before comparing the acquired signals to the reference signals.