Method and device for training particle image restoration model of cryoelectron microscope

By constructing a cryo-electron microscopy particle image restoration model and utilizing deep learning technology and high-quality two-dimensional particle projection image label data, the quality of cryo-electron microscopy single particle images was improved, solving the problem of low image quality and improving the reconstruction accuracy of the three-dimensional structural model.

CN120808059APending Publication Date: 2025-10-17XIAMEN UNIV
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Patent Information

Application Number
CN202410430420.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-04-10
Publication Date
2025-10-17

AI Technical Summary

Technical Problem

The low image quality of single-particle images from cryo-electron microscopy leads to low accuracy in 3D structural models. Existing technologies struggle to effectively improve the quality of single-particle images, especially due to the lack of ideal high-quality images as supervisory labels, resulting in poor image restoration effects.

Method used

By constructing a cryo-electron microscopy particle image restoration model, a three-dimensional structural model is built using multiple cryo-electron microscopy single-particle images. Spatial orientation projection and stitching are performed to generate a high-quality two-dimensional particle projection image, which is then used as label data to train a deep learning model to restore cryo-electron microscopy single-particle images.

Benefits of technology

It improves the clarity and accuracy of cryo-electron microscopy single-particle images, enhances the reconstruction accuracy of three-dimensional structural models, and achieves better image restoration results.

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Abstract

The invention provides a training method and device for a particle image restoration model of a cryoelectron microscope. Relates to an image processing technology. The method comprises the following steps: constructing a three-dimensional structure model of a single particle of a target object based on a plurality of cryoelectron microscope single particle images; projecting the three-dimensional structure model along the spatial orientation of each cryoelectron microscope single particle image relative to the three-dimensional structure model to obtain a two-dimensional particle projection image; splicing each two-dimensional particle projection image according to the position of the single-particle image of the cryoelectron microscope on the cryoelectron microscope image to obtain a corresponding cryoelectron microscope projection image; cutting the cryo-electron microscope projection image according to the position of each cryo-electron microscope single-particle image on the cryo-electron microscope image to obtain a plurality of corresponding cryo-electron microscope single-particle projection images; and training a cryoelectron microscope particle image restoration model by taking the plurality of cryoelectron microscope single-particle images as sample data and taking the plurality of cryoelectron microscope single-particle projection images as label data.
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