MRI Fluid Parameter Calculation Using Diffusion Tensor Imaging
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Solution Overview
Problem
The phase contrast method for MRI has low spatial resolution, leading to errors in calculating fluid parameters like kinetic energy loss, especially for low fluid velocities, due to the need for repeated pulse sequences and averaging of flow velocity values.
Innovation Solution
The use of diffusion tensor imaging (DTI) to calculate fluid parameters by obtaining flow velocity distribution information within voxels, avoiding errors associated with low spatial resolution and allowing accurate calculation of fluid parameters, particularly for low velocity fluids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the phase contrast method is used to obtain flow velocity information, then fluid parameters can be calculated, but spatial resolution is low leading to calculation errors especially for low velocity fluids
Solution Approach 1:
The patent changes the measurement parameters by using diffusion tensor imaging (DTI) instead of phase contrast (PC) method. DTI provides different physical information (diffusion characteristics) that can be used to derive flow velocity distribution with better spatial resolution, thereby resolving the contradiction between measurement precision and spatial resolution
Solution Approach 2:
The patent substitutes the mechanical/physical measurement approach of PC method with a different physical mechanism - diffusion-weighted imaging. By measuring diffusion characteristics and deriving flow information from diffusion tensor data, the system achieves improved spatial resolution while maintaining fluid parameter calculation accuracy
2Measurement precision
If repeated pulse sequences are executed to acquire flow velocity data, then fluid parameters can be obtained, but measurement time increases
Solution Approach 1:
The patent merges multiple measurement objectives into a single DTI acquisition process. By obtaining diffusion tensor data that contains flow velocity distribution information, the system simultaneously achieves flow measurement and diffusion characterization, reducing the need for repeated separate pulse sequences
Solution Approach 2:
The diffusion tensor imaging method serves multiple functions: it provides diffusion characteristics for tissue characterization and simultaneously provides flow velocity distribution information. This multi-functionality reduces measurement time compared to dedicated flow measurement sequences
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 enables accurate calculation of fluid parameters with improved precision by utilizing the flow velocity distribution within voxels, reducing errors and enhancing measurement accuracy compared to the phase contrast method.
Implementation Method 1
diffusion tensor imaging (DTI)... from measurement data obtained by performing DWI using MPG pulses in at least six directions
Implementation Method 2
the phase contrast (PC) method of MRI to analyze kinetic energy loss... a gradient pulse for dephasing NMR signals is combined with a gradient pulse for rephasing, thereby differentiating the phase between a signal from a stationary portion and a signal from a portion with a flow
Data Source
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AI summary
Fluid parameters such as WSS and EL are accurately calculated, using flow velocity information obtained by diffusion tensor imaging. Dispersion of a flow velocity distribution of fluid is calculated, using a diffusion tensor image obtained with respect to an examination target containing fluid, and an estimation model is set for a distribution shape of intra-voxel flow velocity. Using the estimation model and the dispersion of the flow velocity distribution, a differential value of the flow velocity is calculated. Then, a fluid parameter representing a flow characteristic of the fluid is calculated.