Diffusion Kurtosis Imaging Using Unconstrained Tensor Fitting
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Solution Overview
Problem
Existing diffusion kurtosis imaging methods require time-consuming calculations using constrained optimization algorithms, which hinder efficient generation of parameter images.
Innovation Solution
Utilize an unconstrained optimization algorithm to fit scan image signals and determine elements of the diffusion tensor and kurtosis tensor, followed by generating parameter images based on these elements, thereby reducing calculation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If constrained optimization algorithms are used to calculate diffusion tensor and kurtosis tensor elements, then the calculation results are reliable, but the calculation time is excessive
Solution Approach 1:
The patent changes the optimization algorithm from constrained to unconstrained type, fundamentally altering the computational approach. This parameter change in the algorithm category enables faster convergence and reduces calculation time while maintaining acceptable reliability through alternative constraint handling methods
Solution Approach 2:
The patent replaces the traditional constrained optimization mechanical system with an unconstrained optimization system. This substitution involves changing the mathematical framework from one with explicit constraints to one that handles constraints implicitly, thereby reducing computational complexity and time
2Measurement precision
If constrained optimization algorithms are used to generate parameter images, then the imaging accuracy is maintained, but the generation speed is slow
Solution Approach 1:
The patent changes the optimization algorithm parameter from constrained to unconstrained, which fundamentally improves the generation speed. The unconstrained algorithm achieves faster convergence and reduces computational iterations while maintaining imaging accuracy through refined parameter estimation techniques
Solution Approach 2:
The patent performs preliminary actions by pre-processing the diffusion-weighted images and preparing the data structure before running the unconstrained optimization algorithm. This preliminary preparation reduces the computational burden during the main calculation phase, thereby increasing generation speed without sacrificing accuracy
Data Source
AI summary
The disclosure provides a diffusion kurtosis imaging method, which includes acquiring scan image signals of a scanned object; fitting the scan image signals using an unconstrained optimization algorithm to obtain elements of a first diffusion tensor and elements of a first kurtosis tensor; determining at least one type of parameters of diffusion tensor imaging parameters or kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor; and generating a parameter image based on the at least one type of parameters of diffusion tensor imaging parameters or kurtosis tensor imaging parameters.