Perfusion Quantification Model for Tumoral Vascularization
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Solution Overview
Problem
Current models for quantifying tissue perfusion, particularly using bolus methods, fail to accurately describe the kinetics of perfusion in tissues, leading to unsatisfactory relevance in quantification and noise removal from test signals.
Innovation Solution
A new model based on the equation I(t) = a0 + a1 - a0*A + a2*p*B + a2*q*t is introduced, where coefficients a0, a1, a2, p, q, and B are estimated using two-dimensional data to minimize error and provide a sharper estimation of perfusion parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing Gamma-law or exponential models are used to model contrast uptake curves, then the modeling process is simple, but the quantification accuracy of perfusion parameters is unsatisfactory
Solution Approach 1:
The patent introduces a new mathematical model with six parameters (a0, a1, a2, p, q, B) that better describes the kinetics of bolus perfusion in tissue. This model captures the complex physiological processes of contrast agent distribution and elimination more accurately than traditional Gamma-law or exponential models, thereby improving quantification accuracy of perfusion parameters despite increased model complexity
Solution Approach 2:
The patent replaces the simple empirical Gamma-law or exponential models with a more sophisticated mathematical model that incorporates multiple physiological processes. This substitution allows for more accurate representation of the contrast uptake kinetics, enabling better extraction of perfusion parameters such as blood flow, blood volume, and mean transit time
2Loss of information
If bolus injection method is used to obtain contrast uptake curves, then specific perfusion parameters such as dwell-in time and wash-out speed can be extracted, but the signal contains more noise from measuring equipment and external conditions
Solution Approach 1:
The patent employs an iterative optimization process that minimizes the sum of least-squares differences between the measured contrast uptake curve and the model curve. This feedback mechanism allows the model parameters to be adjusted systematically, extracting accurate perfusion information even in the presence of noise from measuring equipment and external conditions such as patient breathing
Solution Approach 2:
The patent extracts specific perfusion parameters (blood flow, blood volume, mean transit time) from the contrast uptake curve by fitting the new mathematical model. This extraction process separates the meaningful physiological information from the noise, enabling accurate quantification despite the noisy signal inherent in bolus injection methods
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 model offers a more accurate and efficient quantification of perfusion by reducing noise and improving the estimation of perfusion parameters, outperforming existing Gamma-law and exponential models in describing bolus perfusion kinetics.
Implementation Method 1
acquiring an input digital signal F(t) function of time t in the form of two-dimensional data (t, F(t)), said signal F(t) resulting from the excitation, within a test substrate, of a substance adapted to emit a signal in response to said excitation
Data Source
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AI summary
The invention relates to a method and system for processing a test signal in medical domain comprising following steps: - acquiring (110, 120, 130) an input digital signal F(t) function of time t in the form of two-dimensional data (t, F(t)), said signal F(t) resulting from the excitation, within a test substrate, of a substance adapted to emit a signal in response to said excitation; - modeling (140) said input digital signal F(t) in function of a pre- established model; - possibly, generating an output digital signal I(t) made up from said modeling; wherein said modeling is based on the following model: (formula I) where the coefficients a0, a1, a2, p, q, A et B are estimated on the basis of said two-dimensional data. The invention is directed to the tumoral vascularization or tumoral angiogenesis detection in tumors.