Flow Cytometer Classification Region Optimization
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Solution Overview
Problem
Flow cytometers face challenges in accurately classifying particle populations due to larger than necessary classification regions, leading to inconsistent results between systems, which complicates system-to-system matching and increases manufacturing costs and complexity.
Innovation Solution
A method to determine optimized classification regions by analyzing sample values, calculating properties such as mean, median, and standard deviation, and adjusting region sizes to contain a predetermined percentage of values, allowing for more precise classification without altering physical system components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If larger classification regions are used to account for accumulated tolerance, then system reliability is improved, but measurement precision deteriorates
Solution Approach 1:
The patent changes the parameters of classification regions by calculating optimized boundaries based on statistical properties (mean, standard deviation) of particle populations. This allows precise determination of region boundaries that account for system tolerance while maintaining high classification precision through data-driven parameter optimization.
2Reliability
If larger classification regions are used to account for accumulated tolerance, then system reliability is improved, but device complexity increases
Solution Approach 1:
The system performs self-calibration by automatically analyzing particle population data and calculating optimized classification region boundaries without requiring manual intervention. The system uses its own measurement data to determine statistical properties and establish region parameters, enabling self-service optimization that reduces operational complexity.
Solution Approach 2:
The patent implements preliminary calibration by determining optimized classification regions before actual sample analysis. This preliminary action establishes accurate boundaries in advance, eliminating the need for complex real-time adjustments during operation and reducing overall system complexity.
3Manufacturing precision
If rigorous assembly efforts are used to compensate for tight tolerances, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
Instead of relying on tight manufacturing tolerances, the patent changes the approach by using statistical parameter optimization to define classification regions. This software-based parameter adjustment compensates for manufacturing variations without requiring complex assembly procedures or ultra-precise component fabrication.
4Reliability
If larger classification regions are used, then system reliability is improved, but productivity deteriorates
Solution Approach 1:
The system performs classification region optimization as a preliminary calibration step that occurs once or periodically, rather than continuously during sample analysis. This preliminary action establishes reliable boundaries in advance, allowing high-speed data acquisition and classification to proceed efficiently without real-time computational overhead.
Data Source
AI summary
Methods for altering one or more parameters of a measurement system are provided. One method includes analyzing a sample using the system to generate values from classification channels of the system for a population of particles in the sample. The method also includes identifying a region in a classification space in which the values for the populations are located. In addition, the method includes determining an optimized classification region for the population using one or more properties of the region. The optimized classification region contains a predetermined percentage of the values for the population. The optimized classification region is used for classification of particles in additional samples.