Fluorescence Decomposition for Real-Time Blood Compartment Identification
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Solution Overview
Problem
Current methods for evaluating blood flow using fluorescence imaging in neurosurgery require extensive video analysis and are unreliable, especially when dealing with superimposed tissue layers, and can only be started after the entire dynamic phenomenon is complete, leading to delayed analysis and potential misidentification of tissue types.
Innovation Solution
A method and image processor that decompose the fluorescence intensity time sequence into component signals representing typical tissue responses, allowing for real-time identification of blood compartments by generating pseudocolor images based on the combination of these signals, which can be computed sequentially and terminated when convergence is reached, reducing computational burden and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If curve-parameter extraction method is used to identify blood compartments, then identification reliability is improved, but analysis time is increased and real-time identification is not achieved
Solution Approach 1:
The patent pre-calculates and stores component signals representing typical fluorescence intensity developments for different blood compartments (arteries, capillaries, veins) before actual analysis. These component signals are computed offline and saved in a database, allowing rapid comparison with measured signals during surgery without performing full decomposition in real-time
Solution Approach 2:
The patent creates simplified copies of the complex fluorescence decomposition problem by using pre-computed component signals that represent typical tissue responses. Instead of performing full mathematical decomposition on measured signals, the system compares measured signals against these pre-computed templates to identify blood compartments rapidly
2Productivity
If decomposition into component signals is performed for real-time identification, then analysis speed is improved, but computational complexity increases
Solution Approach 1:
The patent performs the computationally intensive decomposition of fluorescence intensity curves into component signals in advance, before the surgical procedure. These decompositions are stored as reference data, eliminating the need to perform complex calculations during real-time surgery
Solution Approach 2:
The patent performs only partial decomposition by comparing measured signals against a limited set of pre-computed component signals representing typical blood compartment responses. This partial approach (comparing against templates rather than full decomposition) reduces computational complexity while maintaining sufficient accuracy for clinical decision-making
3Measurement precision
If entire time series is collected before analysis, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent prepares component signals and analysis frameworks in advance, allowing immediate processing of fluorescence measurements as they are acquired during surgery. The measurement system is pre-configured with reference data, enabling continuous real-time analysis without waiting for complete data collection
Solution Approach 2:
The patent enables continuous analysis of fluorescence signals throughout the entire surgical procedure. By using pre-computed component signals for comparison, the system can continuously identify blood compartments in real-time as fluorescence data streams in, rather than pausing to collect all data before analysis
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 reliable and rapid identification of different blood compartments in real-time, reducing the need for extensive data collection and improving the accuracy of tissue type identification, even in cases of superimposed layers, by decomposing fluorescence intensity time sequences into component signals that reflect the characteristics of various tissues.
Implementation Method 1
If an observation area of the object is illuminated using fluorescence excitation wavelengths which trigger the fluorescence of the fluorophore in the fluorescence emission wavelengths
Data Source
AI summary
The invention relates to a method, an image processor (26) and a medical observation device (1), such as a microscope or endoscope, for observing an object (4) containing a bolus of at least one fluorophore (12). The object (4) is preferably live tissue comprising several types (16, 18, 20) of tissue. According to the method, a set (34) of component signals (36) is provided. Each component signal (36) represents a fluorescence intensity development of the fluorophore (12) over time in a different type of tissue. A time series (8) of input frames (10) is accessed, one input frame (10) after the other. The input frames (10) represent electronically coded still images of the object (4) at subsequent time. Each input frame (10) contains at least one observation area (22) comprising at least one pixel (23). In the observation area (22) of the current input frame (10) of the time series (8), a fluorescent light intensity (I) is determined over at least one fluorescence emission wavelength (15) of the fluorophore (12). This fluorescent light intensity (I1) is joined with the fluorescence light intensities (In) of the observation area (22) of preceding input frames (10) of the time series (8) to generate a time sequence (40) of fluorescent light intensities (I1, In) of the observation area (22). This time sequence (40) is decomposed on in a preferably linear combination (72) of at least some of the component signals (36) of the set (34). A new set (34) of component signals (36) is provided which includes only those component signals (36) which are present in the combination (72). An output frame (46) is generated, in which the observation area (22) is assigned a color from a color space depending on the combination (72) of component signals (36).