Microscope Dynamic Range Maximization via Sparse Preview Scanning
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Solution Overview
Problem
Current microscopy techniques face challenges in efficiently imaging large specimens, particularly in fluorescence imaging, due to issues with exposure settings, file size management, and processing time, which result in suboptimal image quality and prolonged analysis times.
Innovation Solution
The development of a method that utilizes a sparse pixel preview scan to estimate gain settings and dynamically contract image data, allowing for accurate exposure and efficient processing of large image files, thereby optimizing dynamic range and reducing file sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional full-resolution scanning is performed on large specimens, then image quality is maintained, but processing time and file size increase significantly
Solution Approach 1:
The patent divides the large specimen into multiple smaller regions or tiles that can be scanned and processed independently. This segmentation allows the system to manage large specimens more efficiently by breaking down the overall scanning task into smaller, more manageable units that can be processed in parallel or sequentially without overwhelming the system's processing capabilities.
Solution Approach 2:
The patent performs preliminary scanning at reduced resolution to generate preview images and estimate parameters such as dynamic range, background levels, and feature distribution. These preliminary actions are performed before the final high-resolution scanning, allowing the system to optimize acquisition parameters and prepare processing pipelines in advance, thereby reducing overall processing time while maintaining image quality.
2Measurement precision
If traditional full-resolution scanning is performed on large specimens, then image quality is maintained, but file size becomes unmanageably large
Solution Approach 1:
The patent applies different processing qualities to different regions of the specimen based on their importance and characteristics. High-resolution scanning and processing are applied only to regions containing features of interest, while other regions are processed at lower resolutions. This local quality approach maintains image quality where needed while significantly reducing overall file size.
Solution Approach 2:
Preliminary low-resolution scanning is performed to identify regions of interest and estimate imaging parameters before committing to full-resolution acquisition. This allows the system to plan the final scanning strategy to capture only the necessary data at high resolution, avoiding unnecessary storage of large amounts of low-information data.
3Measurement precision
If gain settings are optimized for each region of large specimens, then dynamic range is maximized, but setup time increases
Solution Approach 1:
The patent performs preliminary scanning to estimate dynamic range, background levels, and feature intensities across different regions of the specimen. These preliminary measurements are used to pre-calculate optimal gain settings for each region before the final high-resolution scanning. This preliminary optimization of gain settings maximizes dynamic range during the main acquisition while minimizing the time required for manual adjustment.
Solution Approach 2:
The system automatically calculates and applies optimal gain settings based on preliminary scanning data without requiring manual intervention. The software performs self-adjustment of acquisition parameters by analyzing the preview data and configuring the scanning system accordingly, thereby maximizing dynamic range while keeping setup time minimal.
4Loss of information
If complete scanning of large specimens is performed, then comprehensive data is obtained, but processing and analysis time increases
Solution Approach 1:
The patent divides the large specimen into multiple regions and processes them independently. By segmenting the data, the system can apply region-specific processing algorithms and prioritize analysis of regions containing features of interest. This segmentation maintains comprehensive data coverage while reducing overall processing and analysis time through parallel processing and selective focus.
Solution Approach 2:
The patent applies enhanced processing and analysis only to regions containing features of interest, while using more efficient processing for other regions. This local quality approach ensures that comprehensive data is obtained and preserved, but processing resources are concentrated where they provide the most value, thereby reducing overall analysis time.
Data Source
AI summary
A method of operating an instrument that is a macroscope, microscope, or slide scanner is provided where the instrument has a larger dynamic range for measurement than a dynamic range required in the final image of a specimen. In the method, data is measured from a specimen using the instrument, and the dynamic range of the measured data is contracted in the final image file during scanning.