ICG Fluorescence Imaging Processing for Dynamic Blood Flow Evaluation
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Solution Overview
Problem
Current ICG fluorescence imaging in surgery lacks reliable standardized measurements for dynamic evaluation of blood flow, relying on subjective visual assessments due to lack of quantitative temporal measurements, which limits the ability to predict tissue perfusion effectively.
Innovation Solution
A method involving the injection of Indocyanine Green (ICG) into a patient, recording and normalizing ICG images, and filtering background noise to generate a dynamic representation of ICG perfusion over time, allowing for objective evaluation of tissue perfusion and prediction of ischemia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ICG fluorescence imaging is used to evaluate tissue perfusion, then visual representation of blood flow is provided, but reliable standardized measurements of signal intensity are lacking
Solution Approach 1:
The patent transforms the qualitative visual assessment into quantitative measurements by analyzing temporal changes in fluorescence signal intensity. The system extracts kinetic parameters (arrival time, peak time, area under curve) from the fluorescence curves, converting static intensity values into dynamic temporal profiles that objectively characterize tissue perfusion status.
Solution Approach 2:
The system provides real-time feedback by continuously monitoring fluorescence signal changes over time and comparing them against established perfusion thresholds. This allows the surgeon to immediately assess tissue viability and adjust surgical decisions based on objective quantitative data rather than subjective visual interpretation.
2Measurement precision
If absolute value of ICG signal intensity is measured, then camera distance and ambient light distractions affect the measurement, but no dynamic evaluation of blood flow is achieved
Solution Approach 1:
The patent shifts from static absolute intensity measurement to dynamic temporal analysis of fluorescence curves. By examining how the signal changes over time (rate of rise, peak timing, decay pattern), the system evaluates blood flow dynamics that are independent of absolute intensity variations caused by camera distance or ambient light conditions.
Solution Approach 2:
The system performs preliminary normalization of the fluorescence signal by establishing a baseline and using relative changes from that baseline. This preliminary processing step compensates for varying initial conditions (camera distance, ambient light) before extracting the kinetic parameters that truly reflect blood flow characteristics.
3Quantity of substance
If snapshot view of static environment is used, then how much dye is present in tissue is captured, but prediction of blood flow dynamics over time is limited
Solution Approach 1:
The patent continuously records fluorescence signal over an extended time period, capturing the complete kinetic profile of dye arrival, distribution, and clearance. This continuous temporal monitoring transforms a static snapshot into a dynamic trajectory, enabling prediction of blood flow patterns and tissue perfusion status over time rather than at a single moment.
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 real-time, objective assessment of tissue perfusion, reducing variability and subjectivity, and providing quantitative data for clinical decision-making during surgical procedures.
Implementation Method 1
Indocyanine Green (ICG) is a chemical that can be used to identify blood flow through fluorescence detection
Data Source
AI summary
Methods for dynamically evaluating blood flow are provided. The method may include injecting Indocyanine Green (ICG) into the bloodstream of a patient, such that the ICG perfuses into a tissue of the patient. ICG images of the tissue may be recorded and stored, such that a user may select a range of stored ICG images. Data indicative of the selected range of stored ICG images is automatically normalized and background-filtering, and a dynamic representation of the ICG perfusion may be generated as a function of time based on the normalized and filtered data. As such, a clinical decision may be made based on the dynamic representation of the ICG perfusion.