Super-Resolution Holotomography via Cross-Modal Deep Learning
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Solution Overview
Problem
Current holotomography technology is limited by the Abbe diffraction limit, restricting its resolution to 200 to 400 nm, which is insufficient for accurately imaging and analyzing nanoscale intracellular organelles and microorganisms like bacteria and viruses.
Innovation Solution
A cross-modal inference-based deep learning engine is developed to generate super-resolution three-dimensional tomographic images by combining holographic tomography with super-resolution fluorescence microscopy and cryo-electron microscopy, utilizing digital holograms and convolution-based algorithms to overcome the resolution limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional holotomographic imaging is used, then the imaging process is simple and real-time, but the resolution is limited to 200-400 nm due to the Abbe diffraction limit
Solution Approach 1:
The patent uses digital holograms as an intermediary representation that contains latent high-resolution information. The holographic data serves as a bridge between the low-resolution direct imaging and the high-resolution reconstruction, enabling super-resolution through computational processing without requiring complex optical hardware modifications
Solution Approach 2:
The patent replaces the need for complex super-resolution optical hardware with computational algorithms. Instead of using sophisticated optical systems to achieve high resolution, the invention uses digital processing and machine learning to reconstruct high-resolution images from holographic data, substituting mechanical/optical complexity with computational simplicity
2Measurement precision
If super-resolution fluorescence microscopy is used, then nano-resolution images can be achieved, but preprocessing such as staining is required
Solution Approach 1:
The holotomographic imaging system inherently captures three-dimensional refractive index distribution without requiring external staining or labeling agents. The biological samples maintain their natural state while the imaging system itself provides the necessary contrast through optical path difference measurements, making the system self-sufficient for high-resolution imaging
Solution Approach 2:
The patent changes the imaging parameter from fluorescence emission detection to refractive index distribution measurement. By measuring optical path differences caused by variations in refractive index rather than detecting fluorescent signals, the system achieves high resolution without requiring samples to be stained or labeled with fluorophores
3Measurement precision
If the Abbe diffraction limit is accepted, then the imaging system remains simple, but nanoscale intracellular organelles and microorganisms cannot be accurately imaged
Solution Approach 1:
The patent performs preliminary action by capturing comprehensive holographic data that contains latent high-resolution information before any reconstruction process. The digital holograms recorded during the imaging process inherently encode nanoscale structural information that can be extracted through computational reconstruction, preserving information that would otherwise be lost due to diffraction limitations
Solution Approach 2:
The patent transitions from direct spatial imaging to the frequency domain through Fourier diffraction algorithms. By transforming the holographic data into the spatial frequency domain and applying appropriate reconstruction algorithms, the system recovers high-resolution structural information in the spatial domain that exceeds the conventional diffraction limit
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 acquisition of nano-resolution images, achieving horizontal resolutions of tens of nanometers or less, allowing for detailed imaging of biological cells and microorganisms without the need for preprocessing, thereby overcoming the Abbe diffraction limit.
Implementation Method 1
Holotomography (HT) technology is a technology of acquiring a diffraction field scattered from a sample through an interferometer by irradiating a laser beam in a 360 degrees direction
Implementation Method 2
acquiring a diffraction field scattered from a sample through an interferometer
Implementation Method 3
a stimulated emission depletion (STED) super-resolution fluorescence microscope configured to capture a fluorescence image having nano-resolution
Implementation Method 4
stimulated emission depletion (STED) super-resolution fluorescence microscope
Data Source
AI summary
A method of operating an electronic apparatus configured to generate holotomographic image data according to one embodiment of the present disclosure includes: acquiring fusion data from a fusion imaging apparatus in which a holographic tomography apparatus and a super-resolution fluorescence microscope are combined, in which the fusion data includes input data corresponding to holotomographic image data and output data corresponding to image data of the super-resolution fluorescence microscope; generating a cross-modal inference-based deep learning engine configured to generate a super-resolution tomographic image based on the input data and the output data; and acquiring a molecule-specific tomographic image using the deep learning engine, wherein the molecule-specific tomographic image has nano-resolution.