Dynamic Optical Tissue Perfusion Analysis for Real-Time Viability
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Solution Overview
Problem
Existing optical imaging techniques for assessing tissue perfusion and viability face challenges such as sensitivity to motion, limitations in spatial or temporal resolution, computational complexity, and the difficulty in integrating multiple physiological measurements into a cohesive framework, which can impact the accuracy and scalability of real-time assessments.
Innovation Solution
The DyPERF framework integrates dynamic physiological parameters like pulsatile blood flow and vascular hemodynamics into the quantification process, using a modified speckle contrast parameter (KDyPERF) to analyze raw imaging data, preserving full temporal and spatial characteristics, and incorporating spatial and temporal domain analyses to provide a comprehensive assessment of tissue perfusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced physiological models and multiple wavelengths are used to improve tissue viability assessment accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex tissue viability assessment into multiple independent measurement channels, each using specific wavelengths (e.g., 785nm for perfusion, 630nm for oxygenation) and analysis methods (LSCI, HSI, NIRF). This allows parallel processing of different physiological parameters without requiring a single complex analytical model, thereby improving measurement precision while managing device complexity through modular architecture.
Solution Approach 2:
The patent implements a multi-functional imaging system that can perform multiple physiological assessments (perfusion, oxygenation, metabolic activity) using a single integrated platform that combines multiple light sources, detectors, and analysis algorithms. This universal system approach consolidates what would otherwise require separate devices, improving comprehensive tissue viability assessment while controlling overall system complexity through shared hardware and software resources.
2Productivity
If real-time tissue perfusion assessment is implemented, then productivity is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary processing of optical signals by capturing raw reflectance data and immediately applying wavelength-specific filtering and normalization algorithms before full analysis. This preliminary action prepares the data in advance for subsequent real-time perfusion calculations, reducing the computational burden during critical real-time assessment phases and enabling faster processing without sacrificing accuracy.
Solution Approach 2:
The patent implements dynamic adjustment of processing parameters based on tissue characteristics and imaging conditions. The system adaptively selects analysis methods (LSCI for high-speed perfusion, HSI for detailed spectral analysis, NIRF for metabolic assessment) and adjusts computational intensity based on the specific clinical scenario, enabling real-time assessment while optimizing computational resource usage dynamically rather than using fixed high-complexity processing for all cases.
3Reliability
If motion sensitivity is reduced to improve measurement stability, then reliability is improved, but temporal resolution deteriorates
Solution Approach 1:
The system uses periodic illumination patterns and synchronized detection cycles to differentiate between physiological motion (cardiac pulsation, respiratory cycles) and pathological motion artifacts. By sampling at specific periodic intervals aligned with the cardiac cycle, the system can filter out noise while preserving genuine perfusion dynamics, thereby improving measurement stability without sacrificing temporal resolution of clinically relevant physiological changes.
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 DyPERF framework enhances the accuracy and clinical relevance of tissue perfusion assessments by providing detailed temporal and spatial granularity, enabling real-time, physiologically informed evaluations of tissue viability, suitable for various clinical and diagnostic applications.
Implementation Method 1
illuminate the tissue with a light source... capture absorption, quenching, scattering, and reflection characteristics
Implementation Method 2
illuminate the tissue with a light source... capture absorption, quenching, scattering, and reflection characteristics
Implementation Method 3
illuminate the tissue with a light source... capture absorption, quenching, scattering, and reflection characteristics
Implementation Method 4
obtain a raw data set from the imaging system... speckle contrast parameter (KDyPERF) to analyze raw imaging data
Implementation Method 5
illuminate the tissue with a light source... reflected light is captured for subsequent analysis
Data Source
AI summary
Methods, systems, and computer-readable media for assessing tissue perfusion and viability are disclosed. The methods can analyze optical imaging data, including reflectance captured using various light sources and imaging modalities. Tissue perfusion and viability can be evaluated by incorporating spatial, temporal, and physiological parameters. These approaches can enable assessments across a variety of clinical and research contexts, supporting diagnostics, therapy, and monitoring.