One-to-Many Randomizing Interference Microscope for Label-Free Imaging
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Solution Overview
Problem
Conventional optical microscopes face limitations in capturing information due to the tradeoff between field-of-view and image resolution, with computational microscopy also restricted by the need for human-interpretable images, which limits the amount of information that can be extracted.
Innovation Solution
A computational microscope that deviates from the traditional point-to-point mapping paradigm by employing one-to-many mapping of object points to intensity-pattern points, using machine learning algorithms like neural networks, and techniques such as illumination angle coding, polarization coding, amplitude coding, and phase coding to capture and interpret intensity patterns that contain more information about the sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional optical microscopy uses point-to-point mapping to form direct images, then image interpretability by humans is improved, but the amount of information captured is limited by the space-bandwidth product tradeoff
Solution Approach 1:
The patent inverts the traditional imaging paradigm by replacing point-to-point mapping with one-to-many mapping. Instead of mapping each object point to a single image point, the system maps object points to multiple intensity pattern points, enabling capture of significantly more information about the sample while accepting that the resulting patterns require computational algorithms rather than direct human interpretation.
Solution Approach 2:
The patent changes the fundamental mapping parameter from one-to-one (point-to-point) to one-to-many (object point to multiple intensity pattern points). This parameter change allows the system to capture multiple pieces of information simultaneously from each object point,突破ing the traditional space-bandwidth product limitations and enabling access to higher-order statistical information about the sample.
2Loss of information
If computational microscopy algorithms are used to enhance information capture, then the space-bandwidth product is increased, but the need for human-interpretable images limits further information extraction
Solution Approach 1:
The patent deliberately inverts the traditional imaging paradigm by replacing point-to-point mapping with one-to-many mapping. Instead of mapping each object point to a single image point, the system maps object points to multiple intensity pattern points, enabling capture of significantly more information about the sample while accepting that the resulting patterns require computational algorithms rather than direct human interpretation.
Solution Approach 2:
The patent replaces the mechanical/optical image formation system with a computational system. Rather than relying on traditional optical components to produce human-interpretable images, the system uses computational algorithms to process and interpret intensity patterns, enabling extraction of information that would be inaccessible through conventional optical imaging alone.
3Measurement precision
If numerical aperture is increased to improve spatial resolution, then image resolution is improved, but geometric aberrations increase and field-of-view decreases
Solution Approach 1:
The patent introduces an additional dimension to the imaging problem by mapping object points to multiple intensity pattern points. This one-to-many mapping creates multiple measurement channels that provide complementary information, allowing the system to overcome the tradeoff between field-of-view and resolution by capturing information across multiple dimensions simultaneously.
Solution Approach 2:
The patent changes the fundamental mapping parameter from one-to-one (point-to-point) to one-to-many (object point to multiple intensity pattern points). This parameter change allows the system to capture multiple pieces of information simultaneously from each object point,突破ing the traditional space-bandwidth product limitations and enabling access to higher-order statistical information about the sample.
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 allows for the capture of unprecedented amounts of information from samples, particularly biological samples, without the need for chemical labeling, providing higher contrast and signal-to-noise ratios, and enabling label-free imaging that is less expensive and faster than traditional methods.
Implementation Method 1
the raw measurements obtained are sometimes not interpretable via human examination. Rather, such measurements are converted into a human-interpretable image of the sample by solving a so called 'inverse problem' based on a model of the system's physics
Data Source
AI summary
A computational microscope and a method for its operation are disclosed. In some embodiments, the microscope maps points on a sample to point in an intensity pattern on a one-to-many basis. The microscope utilizes illumination angle coding, polarization coding, amplitude coding, and phase coding to capture more information than prior art computational microscopes. Although the resulting intensity patterns are not human-interpretable images of the sample, they contain more information about the sample, by virtue of the aforementioned coding techniques, than is captured by prior-art microscopes. Machine-learning algorithms, such as neural networks, are used to analyze the intensity patterns and extract useful information, such as cellular events or cell behavior.