Dynamic Range Assay Analysis Using Multi-Exposure Image Compensation
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Solution Overview
Problem
Multiplex assays face challenges in accurately detecting analytes due to factors like cross-reactivity, signal amplification, and limited dynamic range, leading to potential errors in biomarker verification and validation processes.
Innovation Solution
The method involves obtaining multiple images of light intensity from assay wells using standard and longer-than-standard exposure times, generating a composite image through exposure compensation, and applying image calibration procedures to enhance dynamic range, thereby improving the detection of analytes and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a standard exposure time is used for detecting light intensity in assay wells, then the detection process is efficient and quick, but the dynamic range is limited and cannot accurately detect analytes at extreme ends of the calibration curve
Solution Approach 1:
The detection process is segmented into multiple exposures with different exposure times. A first image is captured at a standard exposure time for efficient processing, and a second image is captured at a longer exposure time to extend the dynamic range. This segmentation allows the system to maintain high throughput while accurately detecting analytes across a broader concentration range, including those at extreme ends of the calibration curve.
Solution Approach 2:
The system adds a temporal dimension to the detection process by using multiple exposure times. Instead of relying on a single exposure time setting, the method captures images at different exposure durations and combines them, effectively creating a multi-dimensional detection approach that expands the measurable dynamic range without sacrificing detection efficiency.
2Measurement precision
If a longer-than-standard exposure time is used to expand dynamic range, then analytes at extreme ends can be detected, but out-of-range pixels with intensities exceeding detector capacity occur
Solution Approach 1:
The system uses an intermediary processing step that identifies and handles out-of-range pixels separately. When detecting the second image with longer exposure time, pixels that exceed the detector's maximum capacity are identified as out-of-range. These saturated pixels are then replaced with corresponding pixel values from the first image captured at standard exposure time, preventing signal saturation artifacts while maintaining the extended dynamic range benefits for valid measurements.
Solution Approach 2:
The method implements a feedback mechanism where the system continuously monitors pixel intensity values during composite image generation. When out-of-range pixels are detected in the longer exposure image, the system automatically retrieves and substitutes values from the standard exposure image, creating a self-correcting process that eliminates saturation effects while preserving the enhanced dynamic range for accurate analyte quantification.
3Measurement precision
If multiple images with different exposure times are combined, then enhanced dynamic range is achieved, but the image processing complexity increases
Solution Approach 1:
The composite image generation process is designed to be self-regulating and automated. The system automatically determines which pixels are in-range and which are out-of-range, selectively combines data from multiple exposures, and handles saturation correction without requiring complex manual intervention. This self-service approach simplifies the processing workflow despite the multi-exposure nature of the method.
Solution Approach 2:
The method systematically varies the exposure time parameter across multiple image captures and then adjusts pixel intensity values based on exposure compensation ratios. By changing and controlling key parameters like exposure time and intensity scaling in a structured manner, the system achieves enhanced dynamic range while keeping the processing logic manageable and systematic rather than overly complex.
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
This approach increases the accuracy and reliability of biochemical analyses by expanding the dynamic range, reducing signal variability, and enhancing throughput, allowing for more precise quantification of analytes, even at extreme ends of the calibration curve.
Implementation Method 1
a detector to detect the light intensity of the pixels of the wells
Data Source
AI summary
The disclosed systems and methods allow composite images with enhanced dynamic range to be generated that result in more accurate, reliable, and efficient chemical and/or biological analyses. The disclosed systems include an image detector; a timer for tracking exposure time of the image detector; and computer readable medium, including instructions that when executed cause a computer system to generate a composite image using the multiple images of pixels.