Quasi-diffusion MRI using constrained CTRW model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diffusion imaging techniques, such as Diffusional Kurtosis Imaging (DKI) and diffusion tensor imaging (DTI), face challenges in accurately representing physical diffusion processes, leading to unstable parameterization, inefficient computation, and suboptimal utilization of modern MRI systems.
Innovation Solution
A computer-implemented method using a constrained continuous-time random walk (CTRW) model, where the time and space parameters are related by a correlation function, reducing the degrees of freedom and enabling faster data acquisition with lower noise and higher accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DKI uses a simple parameterisation of the diffusion signal, then the technique can be implemented, but it does not accurately represent actual physical diffusion processes and cannot utilise diffusion sensitisations for which b>3000 s mm -2
Solution Approach 1:
The patent changes the parameterisation approach from simple DKI to a continuous-time random walk (CTRW) model with fractional diffusion, allowing accurate representation of non-Gaussian diffusion while enabling utilisation of high b-value data (b>3000 s mm -2) that DKI cannot handle
2Measurement precision
If geometrically constrained diffusion models are used, then potentially useful clinical information can be provided, but the models are challenging to fit and exhibit acquisition and parameter based bias that limit their application
Solution Approach 1:
The patent replaces the mechanical/geometric constraint-based models (which require complex fitting and have acquisition biases) with a stochastic CTRW model that naturally accounts for diffusion heterogeneity without requiring complex geometric assumptions, thereby reducing fitting complexity and bias
3Measurement precision
If standard diffusion imaging techniques are used, then diffusion parameters can be computed, but the computed images are very susceptible to noise, leading to the requirement for long acquisition times
Solution Approach 1:
The patent changes the diffusion signal parameterisation to use CTRW model parameters (diffusion coefficient D and fractional order α) that are more robust to noise, enabling accurate diffusion parameter computation with shorter acquisition times while reducing susceptibility to noise
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
The method allows for rapid image acquisition with high signal-to-noise and contrast-to-noise ratios, overcoming limitations of existing techniques and providing accurate diffusion parameter maps that can be used for clinical diagnosis and research.
Implementation Method 1
Nuclear magnetic resonance (NMR) is a well-known and powerful tool for analysing the properties and structure of materials
Data Source
Figure 1(a)~2
Figure 3(i)~3(ii)
Figure 4
AI summary
A computer-implemented method of analysing nuclear magnetic resonance, NMR, data of a target object is provided. The method comprises receiving NMR data of the target object, and analysing the received NMR data using a model of the diffusive behaviour of 5 particles within the target object. The model includes a time parameter and a space parameter, the time parameter describing temporal characteristics of the diffusive behaviour of particles in the model and the space parameter describing spatial characteristics of the diffusive behaviour of particles in the model. The model is constrained such that the value of the time parameter and the value of the space parameter 10 are related according to a correlation function. An apparatus for analysing nuclear magnetic resonance, NMR, data of a target object is also provided.