Microwave Tissue Imaging With Iterative Permittivity Estimation
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Solution Overview
Problem
Microwave imaging in biomedical applications faces challenges due to imprecise reconstruction of tissue permittivity and the ill-posed inverse scattering problem, particularly in non-homogeneous biological tissues, leading to inaccurate anomaly detection.
Innovation Solution
A processor-implemented method using a Vector Network Analyzer (VNA) to measure reflection coefficients, apply a Delay-Multiply-and-Sum (DMAS) technique, and iteratively compute permittivity to accurately detect and characterize anomalies in biological tissues by generating radar return images and refining images to estimate actual dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If microwave imaging method is applied in biomedical domain, then non-invasive imaging capability is achieved, but measurement precision deteriorates due to imprecise reconstruction of relative permittivity
Solution Approach 1:
The patent applies parameter changes by systematically varying the distance between the antenna and the biological tissue sample to obtain multiple reflection coefficient measurements. This distance parameter variation enables the iterative computation algorithm to converge on accurate relative permittivity values by comparing measurements at different distances, thereby resolving the measurement precision problem while maintaining non-invasive imaging capability
Solution Approach 2:
The patent implements feedback through an iterative computation algorithm that uses the measured reflection coefficients and initially estimated relative permittivity values to compute updated permittivity values. This feedback loop continues until convergence criteria are met, progressively improving measurement precision without compromising the non-invasive nature of the imaging method
2Measurement precision
If iterative permittivity computation is performed, then measurement precision is improved, but computing time increases
Solution Approach 1:
The patent applies partial action by implementing convergence criteria that allow the iterative computation to terminate when a predetermined number of iterations is reached or when the change in relative permittivity between iterations falls below a threshold. This prevents excessive computing while still achieving sufficient measurement precision for anomaly detection
Solution Approach 2:
The patent performs preliminary action by using a rough initial estimation of relative permittivity values before entering the iterative computation process. This preliminary estimation is derived from the measured reflection coefficients and physical distance, providing a starting point that reduces the number of iterations needed to achieve convergence, thereby reducing computing time while maintaining precision
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
Enables precise detection, localization, and characterization of anomalies in biological tissues by iteratively determining relative permittivity, providing accurate size estimates of anomalies.
Implementation Method 1
measuring, by using the VNA, a first reflection coefficient from the one or more reflected microwaves
Implementation Method 2
applying a Delay-Multiply-and-Sum (DMAS) technique on the filtered time series data to generate a radar return image
Implementation Method 3
computing a permittivity for a plurality of frequencies based on a thickness of DUT, using the second reflection coefficient and the third reflection coefficient
Data Source
AI summary
The usage of microwave imaging for biomedical (BMWI) applications is still challenging due to the imprecise reconstruction of the relative permittivity of tissues and the ill-posed inverse scattering problem. Anomaly detection in biological tissues demand in-vivo, non-invasive, and non-contact measurements. Considering and proving the anomaly as a point object in the microwave imaging is erroneous and results in false implications about the anomaly's presence, location, and characteristics. Present disclosure provides systems and methods for anomaly detection in biological tissues. An intensity map of target region is generated to detect tumor. Area around the tumor is processed to obtain a refined image. As the tumor is embedded in tissues of higher dielectric constant, image thus formed is larger in size than actual tumor. An iterative numerical computation method is implemented to estimate relative permittivity. Subsequently, the effective size of the anomaly is approximately estimated, which is close to their actual values.