Blood Cell Counting With Image-Assisted Dynamic Range Extension
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Solution Overview
Problem
Existing particle analysis systems struggle with inaccurate counting and classification of blood cells, particularly those outside the nominal detection range, leading to erroneous results due to particle size, aggregation, and concentration issues.
Innovation Solution
The use of a particle counter combined with an image analyzer and PIOAL sheath fluid to extend the detection range for blood cells, allowing accurate counting and classification of particles through parallel flowcell and impedance analysis, correcting errors by differentiating particle classes and applying image-based information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a particle counter with nominal detection range is used, then counting accuracy within the nominal range is maintained, but particles outside the detection range cannot be accurately counted or classified
Solution Approach 1:
The system segments particle analysis into two parallel pathways: impedance-based counting for particles within the nominal detection range, and image-based analysis for particles outside the nominal range. This segmentation allows each pathway to be optimized for its specific function, resolving the contradiction between maintaining counting accuracy and expanding detection range.
Solution Approach 2:
The system implements a multi-functional particle analysis platform that combines impedance counting and image analysis capabilities in a single instrument. The flow cell can handle both counting modes simultaneously, enabling the system to adapt to different particle sizes and concentrations, thus expanding the overall detection range while maintaining accuracy.
2Productivity
If impedance-based particle counting is used, then rapid quantification is achieved, but misclassification of particle types and aggregation errors occur
Solution Approach 1:
The system uses image analysis as an intermediary verification step for particles detected by impedance counting. When impedance counting identifies particles, the system can invoke image-based classification to confirm particle type and detect aggregation, thereby maintaining rapid throughput while improving classification accuracy through the intermediary image verification process.
Solution Approach 2:
The system implements feedback between impedance counting and image analysis results. Image analysis provides feedback on particle classification accuracy, which can be used to adjust impedance counting parameters or trigger re-analysis of ambiguous particles, thereby improving overall classification precision while maintaining rapid quantification capability.
3Measurement precision
If manual review of instrument results is performed, then erroneous counts can be verified and identified, but analysis time and operational complexity increase
Solution Approach 1:
The system performs self-verification through automated image analysis of particles counted by impedance methods. The instrument automatically detects and flags potential errors such as aggregation or misclassification by comparing impedance results with image-based characteristics, eliminating the need for manual review while maintaining high result accuracy through self-service error detection.
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 accurate counting and classification of blood cells across extended detection ranges, improving the precision of blood cell analysis by correcting miscounts and enhancing the detection of cells outside the nominal range.
Implementation Method 1
counts the number of different particles or cells in a blood sample based on impedance
Implementation Method 2
based on impedance or dynamic light scattering as the particles or cells pass through a sensing area
Data Source
AI summary
For analyzing a sample containing particles of at least two categories, such as a sample containing blood cells, a particle counter subject to a detection limit is coupled with an analyzer capable of discerning particle number ratios, such as a visual analyzer, and a processor. A first category of particles can be present beyond detection range limits while a second category of particles is present within respective detection range limits. The concentration of the second category of particles is determined by the particle counter. A ratio of counts of the first category to the second category is determined on the analyzer. The concentration of particles in the first category is calculated on the processor based on the ratio and the count or concentration of particles in the second category.