3D Sample Imaging with Refractive Index-Based Fluorescence Restoration
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Solution Overview
Problem
Existing image acquisition systems face challenges in accurately restoring three-dimensional sample images due to unknown refractive index distributions and complex sample structures, leading to image degradation such as deformation, reduction in sharpness, and brightness, especially when using optical systems with unknown refractive index values.
Innovation Solution
A sample image acquisition device and generation device that utilizes dual optical systems with a moving mechanism to capture fluorescent and bright field images, calculates refractive index distributions, and employs a processor to generate restored images by dividing images into areas, setting virtual light beams, and calculating point spread functions based on optimized refractive index distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional optical systems are used to capture images of three-dimensional samples, then image acquisition is simplified, but image quality deteriorates due to unknown refractive index distributions causing deformation, reduction in sharpness, and brightness loss
Solution Approach 1:
The patent introduces a computational intermediary (image restoration unit) that processes the captured optical images to correct degradation. Instead of modifying the optical system itself, the patent uses computational methods including point spread function calculation and deconvolution algorithms to restore image quality, effectively mediating between the captured degraded image and the desired high-quality image.
Solution Approach 2:
The patent changes parameters by calculating the point spread function based on the objective lens specifications and imaging conditions, then using these parameters to restore images. The system adjusts imaging parameters such as numerical aperture, wavelength, and refractive index values to optimize the restoration process and improve image quality.
2Measurement precision
If dual optical systems are introduced to improve image restoration accuracy, then image quality improves, but device complexity increases
Solution Approach 1:
The first optical system serves multiple functions: it captures both the optical image used for restoration and the bright field image used for refractive index distribution calculation. This multi-functionality reduces the need for separate specialized systems, thereby limiting the increase in device complexity while maintaining high measurement precision.
Solution Approach 2:
The system uses the bright field image captured by the first optical system to calculate the refractive index distribution, which then serves to restore the fluorescent image from the same system. This self-service approach eliminates the need for external reference measurements or additional sample preparations, reducing overall system complexity.
3Measurement precision
If images are divided into multiple areas for processing, then restoration accuracy improves, but processing time increases
Solution Approach 1:
The patent divides the image into multiple areas and calculates point spread functions for each area independently. This segmentation allows for more accurate restoration by accounting for local variations in optical conditions, while the modular approach enables parallel processing to mitigate the time penalty.
Solution Approach 2:
By calculating area-specific point spread functions rather than using a single global function, the patent applies local quality principles. Each area receives restoration treatment tailored to its specific optical characteristics, improving overall accuracy while the localized processing reduces computational burden compared to processing the entire image uniformly.
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 high-accuracy restoration of three-dimensional sample images by accurately calculating refractive index distributions and point spread functions, even in complex structures with unknown refractive index values, resulting in improved image quality and clarity.
Implementation Method 1
a first optical system configured to form a first optical image of a sample... a fluorescent image group including a plurality of fluorescent images are generated based on the first optical image
Implementation Method 2
a second optical system configured to form a second optical image of the sample... a bright field image group including a plurality of bright field images are generated based on the second optical image
Implementation Method 3
calculates a point spread function corresponding to the area, using the final refractive index distribution included in a range in which the virtual light beam travels
Data Source
AI summary
A sample image acquisition device includes a first optical system configured to form a first optical image of a sample, a second optical system configured to form a second optical image of the sample, a moving mechanism, and an image generation unit. A fluorescent image group is generated based on the first optical image, and a bright field image group is generated based on the second optical image. The image generation unit includes a processor. The processor calculates an estimation image of an estimation sample using a refractive index distribution of the estimation sample, calculates, as a final refractive index distribution, the refractive index distribution optimized using a bright field image and the estimation image, calculates a point spread function using the final refractive index distribution, and generates an image corresponding to a fluorescent image, using the point spread function and the fluorescent image of an area.