Monte Carlo Lookup Tables for Tissue Optical Property Extraction
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Solution Overview
Problem
Current methods for extracting absorption and scattering coefficients from diffuse reflectance spectra in optical spectroscopy are limited by constraints on probe geometries, require extensive phantom studies, and are computationally intensive, making them difficult to implement on a wide variety of existing data sets.
Innovation Solution
A Monte Carlo modeling method that iteratively calculates scattering and absorption characteristics of tissue from diffuse reflectance measurements, using a scaling approach to increase efficiency and adapt to a wide range of optical properties, including high absorption, allowing for accurate extraction of tissue optical properties without restrictive assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo modeling is used to extract absorption and scattering coefficients from diffuse reflectance spectra, then measurement precision is improved, but device complexity increases due to computational intensity
Solution Approach 1:
The patent pre-calculates and stores lookup tables of diffuse reflectance spectra for various combinations of absorption and scattering coefficients before actual measurement. During extraction, the measured spectrum is compared against these pre-computed tables to rapidly identify matching optical properties, eliminating the need for real-time Monte Carlo simulations and reducing computational complexity from hours to seconds.
Solution Approach 2:
The patent creates simplified copy representations of the complex Monte Carlo light transport model by pre-generating synthetic spectra libraries that capture the essential relationships between optical properties and measured reflectance. These copied spectral patterns serve as surrogate models that can be quickly queried without re-running the full computational Monte Carlo simulation.
2Adaptability or versatility
If conventional extraction methods with constraints are used, then device complexity is reduced, but adaptability decreases due to restrictions on probe geometries and optical property ranges
Solution Approach 1:
The patent develops a universal extraction methodology that works across multiple probe geometries (single-fiber, dual-fiber, multi-fiber configurations) and a wide range of optical properties by using the pre-computed lookup tables that span diverse geometric and optical parameter spaces. A single extraction algorithm can handle different probe types without requiring geometry-specific calibration or constraint adjustments.
Solution Approach 2:
The patent extends the applicability of the extraction method by systematically varying parameters in the pre-computed lookup tables to cover wide ranges of absorption coefficients, scattering coefficients, and probe geometries. This allows the same extraction framework to adapt to different tissue types and measurement configurations without modifying the core algorithm.
3Measurement precision
If iterative Monte Carlo modeling is performed for each measurement, then measurement precision is improved, but productivity decreases due to time-consuming calculations
Solution Approach 1:
The patent performs the computationally intensive Monte Carlo simulations in advance to build comprehensive lookup tables of diffuse reflectance spectra corresponding to various optical property combinations. This preliminary computation shifts the computational burden from real-time measurement to offline preparation, enabling rapid extraction during actual clinical use by simply matching measured spectra against the pre-computed tables.
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 method enables accurate and efficient extraction of tissue optical properties, improving diagnostic accuracy and reducing the need for repetitive biopsies by providing real-time, non-destructive identification of tissue types during breast biopsy procedures, with high sensitivity and specificity for distinguishing malignant and non-malignant tissues.
Implementation Method 1
A forward model of light transport based on the diffusion approximation... Monte Carlo modeling is a numerical technique that is valid for a wide range of absorption and scattering coefficients
Implementation Method 2
the combined influence of absorption and scattering events upon diffusely reflected light in tissue make it difficult to interpret the physiological and structural content
Data Source
AI summary
An iterative process calculates the absorption and scattering coefficients of tissue from a set of diffuse reflectance measurements made with an optical spectrometer operating in the UV-VIS spectral range. The relationship between measured diffuse reflectance and the absorption and scattering coefficients is modeled using a Monte Carlo simulation.