PS-OCT Data Correction via Non-Linear Monte Carlo Simulation
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Solution Overview
Problem
Polarization-sensitive optical coherence tomography (PS-OCT) systems face challenges in achieving accurate quantitative diagnosis due to errors in phase retardation measurements, which are asymmetrical and noisy, limiting their ability to accurately classify disease stages in biological samples with fibrous structures or tooth enamel.
Innovation Solution
A program is developed to correct PS-OCT data using a non-linear conversion function, obtained through Monte Carlo simulation, to convert phase difference distribution data into symmetrical data, thereby estimating the true phase constant and enhancing the system's quantitative analysis capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PS-OCT measures phase retardation data directly, then the measurement process is simple and fast, but the data contains systematic errors and is asymmetrical, reducing measurement precision
Solution Approach 1:
The patent applies parameter changes by transforming the asymmetrical phase retardation distribution into a symmetrical distribution through non-linear conversion. The conversion function adjusts the statistical parameters of the data distribution, specifically transforming the mean and variance of the phase retardation values to eliminate systematic errors and achieve normal distribution, thereby improving measurement precision without adding physical complexity to the OCT system
Solution Approach 2:
The patent replaces mechanical/data collection complexity with computational processing. Instead of modifying the physical PS-OCT measurement system to reduce errors, the invention uses computational algorithms (conversion functions based on Monte Carlo simulations) to correct the measured data. This substitutes physical system complexity with software-based correction, achieving high precision while keeping the hardware simple
2Reliability
If PS-OCT is used for quantitative diagnosis, then the application value increases, but the asymmetrical and noisy data distribution prevents accurate disease stage classification
Solution Approach 1:
The patent implements feedback through an iterative correction process. The conversion function is determined based on the actual measured data distribution characteristics, and the correction is applied feedback-driven to ensure the data converges to a symmetrical normal distribution. This feedback mechanism adjusts the processing parameters based on the actual measurement quality, improving reliability for quantitative diagnosis
Solution Approach 2:
The patent applies preliminary action by pre-determining conversion functions through Monte Carlo simulations before actual measurement. Multiple conversion functions are prepared in advance based on simulated data distributions, and the appropriate function is selected based on the measured data's statistical characteristics. This preliminary preparation enables accurate phase constant estimation without requiring complex real-time computation during diagnosis
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 program enables accurate quantitative diagnosis and disease stage classification by removing systematic errors from PS-OCT data, improving the system's ability to provide clear and precise images of tissue structures.
Implementation Method 1
The object light and reference light thus returning to the beam splitter 46 enter a condensing lens 51 and get focused onto an optical detector 52... interference signals manifest only when the distance from the reference arm is roughly equivalent to the distance from the object arm
Implementation Method 2
PS-OCT continuously modulates the polarized state of the linearly polarized beam simultaneously during scan B, to capture the polarization information of the sample
Data Source
AI summary
Data measured by PS-OCT is corrected in a non-linear manner to enhance the quantitative analysis capability of PS-OCT and permit accurate quantitative diagnosis, including diagnosis of disease stage of lesions, as a useful means for computer diagnosis. Even when retardation per PS-OCT 1 contains error and becomes noise and its distribution is not normal or symmetrical around the true value, measured data is converted using a distribution conversion function obtained by analyzing the characteristics of noise via Monte Carlo simulation to remove the systematic error and estimate the true value otherwise buried in noise and thereby correct the PS-OCT 1 image more clearly.