Sorted Lookup Table for MR Parameter Determination
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Solution Overview
Problem
Current methods for determining physiological tissue parameters from MR images are time-consuming and often find only local minima, failing to achieve global optimality, and result in clinically unusable images due to under-sampling and noise interpretation.
Innovation Solution
A method involving the creation of a sorted lookup table with pre-processed MR signal values allows for faster and more precise determination of signal parameters by using an optimization function, enabling the finding of global minima and producing clinically usable images with reduced processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fitting methods (least squares, Levenberg-Marquardt, Nelder-Mead) are used to determine physiological parameters from MR images, then parameter values can be obtained, but the process is very time-consuming and may only find local minima instead of global minima
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing signal characteristics for various parameter combinations in a lookup table before actual image analysis. This pre-computed reference data enables rapid comparison and parameter determination during clinical use, eliminating the need for time-consuming iterative fitting while ensuring global optimality through comprehensive pre-sampling of the parameter space
Solution Approach 2:
The patent uses copying by creating simulated signal characteristics that replicate the expected MR signal behavior under different physiological parameter conditions. These synthesized signal copies are stored in the lookup table and matched against actual measured signals, replacing complex numerical optimization with efficient pattern matching while maintaining measurement precision
2Measurement precision
If Magnetic Resonance Fingerprinting (MRF) is used to generate quantitative parameter maps, then physiological parameters can be determined, but several hundred highly under-sampled MR images must be recorded which cannot be clinically evaluated
Solution Approach 1:
The patent applies partial action by acquiring only the necessary number of MR images with specific parameter variations needed for reliable parameter determination, rather than the several hundred images required by MRF. The lookup table approach enables accurate parameter estimation with fewer measurements by leveraging pre-computed signal characteristics
Solution Approach 2:
The patent extracts only the essential signal characteristics needed for parameter determination from the complex MRF approach. By separating the signal modeling component (pre-computed lookup table) from the measurement component (actual MR images), the method retains the quantitative accuracy of MRF while eliminating the need for excessive image acquisitions
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 and improves the accuracy of physiological tissue parameter determination, enabling more complex modeling like pharmacokinetic analysis with fewer artefacts and clinically usable MR images.
Implementation Method 1
The present invention concerns a method for using magnetic resonance (MR) data in order to make quantitative spatially resolved determination of a physiological tissue parameter
Data Source
AI summary
In a magnetic resonance method and apparatus for the quantitative spatially resolved determination of a physiological tissue parameter of an examination subject, a signal model is determined with m different signal parameters that influence an MR signal of the subject. N different MR images of the subject are recorded with m⇐N, and measured data tuples with N measured values are determined from the N MR images. A lookup table is created with multiple table entries, which each assigns an N-dimensional tuple of t synthesized measured values, which were calculated using the signal model, to an m-dimensional tuple of signal parameters. The lookup table is pre-processed into a sorted lookup table, and at least some of the signal parameters are determined by comparing the pixel-by-pixel measured data tuples with N dimensional tuples of the synthesized measured values in the sorted lookup table for at least some of the pixels.