3D Microscopy via Rotational Imaging and Computational Reconstruction
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Solution Overview
Problem
Current methods for generating three-dimensional models of microscopic objects using confocal microscopy are expensive and time-consuming, with trade-offs between exposure time and signal-to-noise ratio, and require numerous images to achieve desired resolution.
Innovation Solution
A system that uses a substrate with electrodes to rotate the object within a fluid, capturing images at various orientations, and employs a comparison engine to determine spatial relationships and a modeling engine to adjust the model based on image contours, allowing for quick generation of high-resolution three-dimensional models with good signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If confocal microscopy is used to generate three-dimensional models, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the complex confocal microscopy system into simpler components: a standard microscope, rotation stage, and software-based 3D reconstruction. Instead of using complex confocal optics, the system captures multiple 2D images at different rotation angles and reconstructs the 3D model computationally, dividing the problem into image acquisition and image processing segments.
Solution Approach 2:
The patent replaces the complex optical mechanical system of confocal microscopy with a simpler mechanical rotation stage combined with software processing. The 3D reconstruction is achieved through computational methods rather than complex optical sectioning, substituting mechanical rotation and digital processing for complex optical mechanisms.
2Measurement precision
If confocal microscopy with pinhole aperture is used, then measurement precision is improved, but loss of substance (light) increases
Solution Approach 1:
The patent extracts and removes the pinhole aperture component from the optical path entirely. Instead of using confocal optics with pinholes that block light, the system uses a simple microscope objective that allows maximum light transmission, achieving 3D imaging through rotational capture and computational reconstruction rather than optical sectioning.
3Measurement precision
If numerous images are captured to achieve desired resolution, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent introduces dynamic rotation of the sample during image capture. By rotating the sample continuously and capturing images at different angular positions, the system acquires multiple views in a single rotational cycle rather than requiring multiple separate imaging sessions, reducing total acquisition time while maintaining resolution through angular diversity.
Solution Approach 2:
The system uses periodic rotation of the sample stage to systematically capture images at regular angular intervals. This periodic action allows efficient sampling of the object from multiple angles in a structured sequence, enabling complete 3D reconstruction with fewer total images compared to non-periodic capture methods.
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 enables the rapid and cost-effective generation of high-resolution three-dimensional models with improved signal-to-noise ratio, capable of handling multiple objects from a single set of images, outperforming traditional confocal microscopy systems.
Implementation Method 1
A system that uses a substrate with electrodes to rotate the object within a fluid
Data Source
AI summary
An example method includes capturing a plurality of images of a rotating object using an imaging device optically coupled to a microscope. The method removing a first portion of a model of the rotating object based on a first contour of the rotating object in a first image of the plurality of images. The method includes orienting the model based on an amount of rotation of the rotating object between capture of the first image and capture of a second image of the plurality of images. The method also includes removing a second portion of the model of the rotating object based on a second contour of the rotating object in the second image.