MR Fingerprinting Sequence Optimization With B-Spline Parameterization
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Solution Overview
Problem
The optimization of acquisition parameters for MR Fingerprinting is computationally expensive, limiting its practical utility despite improved estimation performance and signal-to-noise ratio (SNR) efficiency.
Innovation Solution
The method represents acquisition parameter sequences using piecewise polynomial functions, specifically B-spline basis functions, constraining them to low-dimensional subspaces, reducing the search space and improving computational efficiency while maintaining estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimal experimental design optimization is performed for MR Fingerprinting acquisition parameters, then estimation accuracy and SNR efficiency are improved, but computation time increases significantly
Solution Approach 1:
The patent segments the continuous parameter optimization space into discrete grids for flip angles and repetition times. This segmentation transforms the continuous optimization problem into a discrete search problem, reducing computational complexity while maintaining estimation accuracy. The grid-based approach allows pre-computation and storage of optimal parameter sets, significantly reducing real-time computation requirements.
Solution Approach 2:
The patent performs preliminary optimization computations offline to generate lookup tables of optimal acquisition parameters for different tissue types and imaging conditions. These pre-computed optimal parameter sets are stored and retrieved during actual imaging experiments, eliminating the need for real-time optimization calculations while maintaining high estimation accuracy and SNR efficiency.
2Ease of operation
If randomized parameter encoding is used for MR Fingerprinting, then acquisition is simplified, but signal-to-noise ratio efficiency deteriorates
Solution Approach 1:
The patent systematically varies acquisition parameters (flip angles and repetition times) according to pre-determined optimal patterns rather than random sequences. These parameter changes are designed to maximize signal-to-noise ratio efficiency while maintaining the simplicity of the acquisition process. The optimized parameter sequences create distinct magnetization evolutions that improve parameter estimation accuracy.
Data Source
AI summary
A method of performing a diagnostic scan of a subject comprises defining a set of data acquisition parameter sequences, defining a set of upper bounds and a set of lower bounds for each of the set of data acquisition parameter sequences, defining a set of representative tissue parameters for a tissue of the subject, selecting a set of basis functions, calculating values for a set of basis function coefficients to yield a piecewise polynomial representation of each of the set of data acquisition parameter sequences within the sets of upper and lower bounds based on the set of desired tissue parameters, and performing a diagnostic scan of the subject using the calculated data acquisition parameter sequences.