UV-Excited Sectioning Tomography With Deep-Learning Super-Resolution
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Solution Overview
Problem
Existing 3D fluorescence microscopy techniques for large biological samples are laborious, time-consuming, and often require tissue processing that can induce side effects and degrade imaging quality, while label-free imaging systems face challenges with lower imaging specificity and longer experimental times.
Innovation Solution
Implementing neural networks, such as cGAN and ESRGAN, to transform low-resolution TRUST images into high-resolution images and integrate virtual optical sectioning methods, reducing the need for multiple illumination shots and optimizing tissue preparation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 3D fluorescence microscopy is used to acquire high-resolution images of large biological samples, then imaging resolution is improved, but image acquisition time becomes extremely long (e.g., 2 weeks for whole mouse brain)
Solution Approach 1:
The patent divides the large biological sample into multiple small tissue blocks through mechanical sectioning. Each block is imaged separately using UV excitation, and the images are then computationally reconstructed into a complete 3D volume. This segmentation allows rapid acquisition of each block while maintaining overall high resolution through the combination of multiple sections.
Solution Approach 2:
The patent replaces conventional optical sectioning methods with a computational reconstruction approach. Instead of using complex optical systems to achieve sectioning, the system uses UV excitation to illuminate the entire block and employs algorithms to reconstruct the 3D structure from the fluorescence signals, significantly reducing acquisition time while maintaining resolution.
2Measurement precision
If tissue preparation protocols (clearing, staining) are applied to large samples, then imaging quality is improved, but preparation time becomes extremely long and sample morphology may be distorted
Solution Approach 1:
The patent performs mechanical sectioning of the sample into small blocks before imaging. This preliminary action reduces the sample size to a scale where rapid UV excitation imaging can be performed without requiring lengthy clearing or staining protocols, thus maintaining imaging quality while dramatically reducing preparation time.
3Measurement precision
If optical clearing methods are used on large samples, then imaging penetration is improved, but sample morphology is distorted and reagent toxicity increases
Solution Approach 1:
By dividing the large sample into small tissue blocks, the patent eliminates the need for optical clearing methods. The small block size allows UV light to penetrate effectively without requiring toxic clearing reagents, thus maintaining imaging penetration while avoiding morphological distortion and reagent toxicity.
4Productivity
If label-free imaging systems are used, then staining time is reduced, but imaging specificity and contrast decrease
Solution Approach 1:
The patent utilizes the natural fluorescence properties of different tissue components, which emit at different wavelengths when excited by UV light. This allows the system to achieve specific imaging of different tissue types and structures based on their intrinsic fluorescence characteristics, maintaining imaging specificity without requiring external stains while preserving rapid imaging speed.
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 significantly reduces image acquisition time while maintaining high imaging resolution and content, improving scanning speed and axial resolution, and enhancing imaging specificity without the need for extensive tissue processing.
Implementation Method 1
irradiated with UV light to yield LR fluorescence and autofluorescence images
Data Source
AI summary
Translational rapid ultraviolet-excited sectioning tomography (TRUST) applies ultraviolet (UV) excitation to a sample and images fluorescence and autofluorescence emission for tomographically imaging the sample. Deep-learning neural networks are used to achieve higher imaging speed and imaging resolution. In one use, fluorescence images acquired with relatively low imaging resolution can be transformed into high-resolution images through the first conditional generative adversarial network (cGAN), a super-resolution neural network (e.g., ESRGAN), which is also helpful for reducing the image scanning time. In another use, the second cGAN, such as Pix2Pix, is used to realize virtual optical sectioning to enhance the axial resolution of the imaging system. Compared to the conventional pattern illumination methods (e.g., HiLo microscopy), which need at least two shots for each field of view, the imaging speed is also times higher because only one shot under the uniform-illumination condition of UV irradiation is required.