Cell Hologram Augmentation for Accurate Flow Cytometry Identification
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Solution Overview
Problem
Existing methods for identifying cells using three-dimensional image capturing are cumbersome and time-consuming, and imaging flow cytometers struggle to capture images of flowing cells while changing focal positions, limiting the preparation of many images needed for effective cell identification.
Innovation Solution
A method that generates a learning model by increasing the number of holograms through varying focal positions and adding or removing aberrations, allowing for appropriate cell identification even when many images are not available in advance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional image capturing methods are used to correctly identify cells, then measurement precision is improved, but device complexity increases and time consumption increases
Solution Approach 1:
The patent uses digital holography to create a digital copy of the cell's three-dimensional information. Instead of using complex optical systems to physically capture three-dimensional images, the invention captures a two-dimensional hologram that can be computationally reconstructed into three-dimensional images, thereby reducing device complexity while maintaining measurement precision.
Solution Approach 2:
The patent replaces complex mechanical/optical three-dimensional imaging systems with a computational approach. By using digital hologram reconstruction algorithms, the system substitutes physical optical complexity with computational processing, achieving three-dimensional cell identification without cumbersome optical equipment.
2Measurement precision
If three-dimensional image capturing is used to identify cells, then measurement precision is improved, but loss of time increases due to image reconstruction
Solution Approach 1:
The patent performs preliminary action by capturing the digital hologram that contains all necessary three-dimensional information in a single shot. The hologram is recorded with pre-encoded phase and amplitude information that can be rapidly reconstructed, avoiding the need for time-consuming sequential focusing or multiple image captures.
Solution Approach 2:
The patent replaces time-consuming mechanical focusing adjustments with computational hologram reconstruction. Instead of physically moving the focal plane through multiple positions, the system uses digital signal processing to reconstruct three-dimensional images from a single holographic record, dramatically reducing time loss.
3Measurement precision
If imaging flow cytometer changes focal position to capture three-dimensional images, then measurement precision is improved, but productivity decreases due to inability to continuously capture flowing cells
Solution Approach 1:
The patent creates a digital copy of three-dimensional cell information through holography. By recording the interference pattern of reference light and object light, the system captures complete three-dimensional data in a single two-dimensional hologram, eliminating the need to stop cell flow for focal adjustments and maintaining high productivity.
Solution Approach 2:
The patent performs preliminary action by encoding all three-dimensional information into a single holographic record during cell flow. The digital hologram contains pre-stored phase and amplitude information that enables subsequent rapid reconstruction of three-dimensional images without interrupting the continuous cell stream, thus maintaining high productivity.
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 cell identification by generating a more appropriate learning model using machine learning with augmented holograms, improving identification accuracy and efficiency.
Implementation Method 1
an acquisition step for model generation of acquiring a hologram for model generation in which an image of a cell is captured
Data Source
Figure 1
Figure 2A~2D
Figure 3
AI summary
The method includes an acquisition step for model generation of acquiring a hologram for model generation in which an image of a cell is captured and which is used to generate a learning model, a generation step for model generation of generating a new hologram for model generation having a different focal position from the acquired hologram for model generation and increasing the number of holograms for model generation, and a learning model generation step of generating a learning model by performing machine learning using the increased holograms for model generation.