Ophthalmology quantum imaging method, system and device and storage medium
By combining quantum imaging technology with image processing algorithms, high-resolution and high signal-to-noise ratio ophthalmic imaging has been achieved, overcoming the resolution and depth limitations of traditional microscopic imaging, providing three-dimensional reconstruction capabilities, and making it suitable for the early diagnosis of ophthalmic diseases.
Patent Information
- Application Number
- CN202511545054.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-28
- Publication Date
- 2026-01-27
AI Technical Summary
Traditional confocal microscopy imaging techniques in ophthalmology are limited in resolution, have low signal-to-noise ratio, and insufficient penetration depth, making it difficult to achieve high-quality deep ocular tissue imaging. Quantum imaging technology also has compatibility issues in ophthalmic applications and lacks three-dimensional reconstruction analysis.
By combining quantum imaging technology with image processing and reconstruction algorithms, quantum information is acquired through scanning, and high-resolution images are constructed, filtered, and reconstructed to generate high signal-to-noise ratio 3D renderings.
It achieves high-resolution, high signal-to-noise ratio imaging of ocular tissues, improves image quality, supports 3D reconstruction, and provides a reliable tool for the early diagnosis of ophthalmic diseases.
Smart Images
Figure CN121400766A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of image technology, and in particular to a method, system, apparatus, and storage medium for ophthalmic quantum imaging. Background Technology
[0002] Confocal microscopy is a commonly used method for observing the microscopic structure of biological tissues, providing a non-invasive and intuitive view of tissue morphology. However, the imaging performance of traditional confocal microscopes is limited by the optical diffraction limit, making it difficult to overcome the physical constraints of numerical aperture. Furthermore, regarding imaging depth, the significant absorption, scattering, and refraction of photons by biological tissues (especially deep tissues) leads to signal attenuation, increased background noise, and decreased image contrast, resulting in unsatisfactory imaging quality for deep tissues. Quantum imaging, as an emerging technology, can utilize the non-classical properties of quantum light fields to achieve super-resolution imaging and high noise suppression under low-light conditions, possessing the potential to break through the limitations of classical imaging.
[0003] However, the practical application of quantum imaging technology in medical imaging is still in its early stages of exploration, and many key issues remain to be addressed in image acquisition, processing, analysis, and reconstruction methods. For example, the near-infrared fluorescence quantum imaging method published in Nature Nanotechnology in 2022, "In vivo non-invasive confocal fluorescence imaging beyond 1,700 nm using superconducting nanowire single-photon detectors," still faces compatibility issues in the ophthalmology field and has not yet addressed the analysis related to the three-dimensional reconstruction of biological tissue structures. Summary of the Invention
[0004] To address the problems existing in the prior art, the present invention aims to provide a method, system, device, and storage medium for ophthalmic quantum imaging. Based on quantum imaging technology and image processing and reconstruction algorithms, it achieves high-resolution, high signal-to-noise ratio imaging of ocular tissues and is applied to ocular quantum imaging, providing a more reliable imaging tool for the early diagnosis and physiological research of ophthalmic diseases.
[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: A method for ophthalmic quantum imaging includes the following steps: Step 1: Scan the eye tissue to obtain raw data files containing quantum information in a specified storage format; Step 2: Parse and read the original data file and construct images to obtain high-resolution multi-layer two-dimensional cross-sectional images of ocular tissue; Step 3: Filter the two-dimensional cross-sectional image of the eye tissue; Step 4: Based on the multi-layer two-dimensional tomographic images processed in Step 3, a three-dimensional reconstruction algorithm is used to restore the three-dimensional structure of the eye tissue, generating a three-dimensional rendering that can be used for lesion display or subsequent analysis.
[0006] The step one involves scanning eye tissue based on a quantum imaging system containing quantum information. The quantum imaging system is an existing system that includes an optical path system for generating and controlling a quantum light source and a quantum detection system for receiving and converting the quantum light into an observable signal.
[0007] Step two involves parsing and reading the original data file. A Python program based on the tifffile library functions reads continuous value data from the original data file in step one and exports the data needed to construct the image.
[0008] The image construction described in step two involves bit depth mapping and RGB three-channel data construction based on the read data. Bit depth mapping maps continuous values to the range covered by the given positioning depth. RGB three-channel data construction is based on channel color rendering using different color schemes to obtain a color-mapped image with obvious color contrast.
[0009] Step three describes the filtering process, which uses Gaussian smoothing filtering to suppress noise in the image and reduce detail at a specified scale. The Gaussian filter function is as follows: (1) in, The value is usually 1.
[0010] The three-dimensional reconstruction algorithm described in step four includes: 4.1 Volume Data Construction The preprocessed two-dimensional image sequence is stacked in spatial order, and dense isometric interpolation is performed on the layer-by-layer quantum imaging using an isometric interpolation method to form data for constructing a three-dimensional volume. ,in z Dimensions represent the number of layers in an image sequence; 4.2 Data Normalization The constructed 3D volume data value range is mapped to a unified standard interval [0, 1]. (2) 4.3 Maximum Intensity Projection The highest intensity voxel value is found along each view path in the normalized 3D volume data and used as the response value of the corresponding pixel. Specifically, for each pixel coordinate in the normalized 3D volume data... Calculate the 3D volume data along the projection ray perpendicular to the view plane. The maximum value of all voxel values along the corresponding path is used to obtain the two-dimensional intensity matrix. Its mathematical expression is: (3) in, Represents the path along the main viewpoint.
[0011] 4.4 Visual Mapping A mapping relationship is established between the color and transparency visual channels and data values, and the two-dimensional intensity matrix is mapped using a predefined mapping function. These values are mapped to RGB three-channel color values and transparency values respectively to enhance the color contrast of the image, present an intuitive and easy-to-understand three-dimensional view, and realize the three-dimensional reconstruction of quantum signals; Specifically, The RGB three-channel color mapping function is predefined Color mapping table specifies: (4) in, For the length of the color map table, This is a round-down operation; Transparency mapping function: (5) in, Transparency Map Length, generally a linear mapping; The final RGB channel composition function for the visualized 3D image is: (6) in, The background color is used to obtain a 3D rendering for lesion display or subsequent analysis.
[0012] The present invention also includes: An ophthalmic quantum imaging system is provided to perform scanning of ocular tissues and acquire ocular data containing quantum information in the ophthalmic quantum imaging method.
[0013] An ophthalmic quantum imaging device includes a memory and a processor. The memory stores a computer program that implements the above-described ophthalmic quantum imaging method, and the processor is used to execute the steps of the ophthalmic quantum imaging method.
[0014] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the ophthalmic quantum imaging method.
[0015] Compared with the prior art, the present invention has at least the following beneficial effects: 1. Since step one uses quantum imaging to acquire raw data containing quantum response characteristics, it can provide higher resolution and higher signal-to-noise ratio data for subsequent image analysis compared to traditional optical imaging methods.
[0016] 2. The quantum imaging system used in step one has good compatibility and scalability, can be integrated with existing ophthalmic imaging equipment, and supports real-time imaging and multimodal fusion analysis.
[0017] 3. Since step four combines quantum imaging technology with three-dimensional reconstruction technology, it is possible to achieve three-dimensional modeling of the eye structure at the quantum effect level, filling the gap in the application of quantum imaging in the field of ophthalmic three-dimensional reconstruction.
[0018] This invention provides an ocular quantum imaging method and system based on quantum detection technology and confocal imaging principle. It addresses the problems of limited resolution, low signal-to-noise ratio and insufficient penetration depth in imaging transparent tissues of the eye, and significantly improves image quality by combining image processing and analysis methods.
[0019] In summary, this invention combines quantum imaging technology with image processing and reconstruction algorithms. Compared with traditional confocal microscopy and classical imaging methods, it not only achieves a significant improvement in lateral and axial resolution, but also maintains good image contrast and diagnostic reliability under low light conditions. It provides a powerful technical tool for the early detection and dynamic monitoring of ophthalmic diseases and has important clinical and application value. Attached Figure Description
[0020] Figure 1 The implementation steps of this invention are described below.
[0021] Figure 2 This is the cross-sectional image before filtering in step three of this embodiment of the invention.
[0022] Figure 3 The image shown is a scanned cross-sectional image at a certain depth after filtering in step three of this embodiment of the invention (after visualization mapping processing).
[0023] Figure 4 This is the result of three-dimensional reconstruction in step four of the embodiment of the present invention. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0025] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0026] Depend on Figure 1 As shown, a method for ophthalmic quantum imaging specifically includes the following steps: Step 1: Scan the eye tissue using a quantum imaging system to obtain raw data files containing quantum information in a specified storage format; Step 2: Use the completed program to parse and read the original data file and construct images to obtain a set of high-resolution two-dimensional cross-sectional images of ocular tissue; Step 3: Filter the two-dimensional cross-sectional images of the eye tissue to effectively suppress noise, enhance feature signals, and improve the signal-to-noise ratio and clarity of the images; Step 4: Based on the multi-layer two-dimensional tomographic images processed in Step 3, a three-dimensional reconstruction algorithm is used to restore the three-dimensional structure of the eye tissue, generating a three-dimensional rendering that can be used for lesion display or subsequent analysis.
[0027] Step two involves parsing and reading the original data file. A Python program based on the tifffile library functions reads continuous value data from the original data file in step one and exports the data needed to construct the image.
[0028] The image construction described in step two involves bit depth mapping and RGB three-channel data construction based on the read data. Bit depth mapping maps continuous values to the range covered by the given positioning depth (usually set to 8 or 16). RGB three-channel data construction is based on channel color rendering using different color schemes to obtain a color-mapped image with obvious color contrast.
[0029] Step 3, the filtering process, addresses the issue that the data from the quantum imaging device contains noise during the detection process. Since these noise values differ significantly from the reference values, a Gaussian smoothing filter is used to suppress noise in the image and reduce detail at a specified scale. The Gaussian filter function is as follows: (1) in, Generally, the value is 1, and the filter size is selected as 5×5.
[0030] The three-dimensional reconstruction algorithm described in step four includes: 4.1 Volume Data Construction The preprocessed two-dimensional image sequence is stacked in spatial order, and dense isometric interpolation is performed on the layer-by-layer quantum imaging using an isometric interpolation method to form data for constructing a three-dimensional volume. ,in z Dimensions represent the number of layers in an image sequence; 4.2 Data Normalization The constructed 3D volume data value range is mapped to a unified standard interval [0, 1]. (2) To eliminate dimensional differences and provide standardized input for subsequent visualization mapping; 4.3 Maximum Intensity Projection By identifying the voxel value with the highest intensity along each view path in the normalized 3D volumetric data as the response value of the corresponding pixel, the expression of feature structures in the volumetric data can be effectively enhanced, while suppressing the interference of isolated noise points. This achieves effective extraction and clear display of the true 3D structure in the original data. Specifically, for each pixel coordinate in the normalized 3D volumetric data... Calculate the 3D volume data along the projection ray perpendicular to the view plane. The maximum value of all voxel values along the corresponding path is used to obtain the two-dimensional intensity matrix. Its mathematical expression is: (3) in, Represents the path along the main viewpoint.
[0031] 4.4 Visual Mapping A mapping relationship is established between the color and transparency visual channels and data values. Through a predefined mapping function, the normalized intensity values are mapped to RGB three-channel color values and transparency values respectively. The single-channel volume data values are converted into an intuitive and easy-to-understand three-dimensional view to enhance the color contrast of the image and present an intuitive and easy-to-understand three-dimensional view, thereby realizing the three-dimensional reconstruction of quantum signals.
[0032] Specifically, The RGB tee color mapping function is predefined Color mapping table specifies: (4) in, For the length of the color map table, This is a floor operation.
[0033] Transparency mapping function: (5) in, Transparency Map Length, generally a linear mapping; The final RGB channel composition function for the visualized 3D image is: (6) in, The background color is usually black. Ultimately, a three-dimensional rendering is obtained for displaying the lesion or for subsequent analysis.
[0034] Experimental results verification The results of a quantum imaging experiment on an isolated mammalian anterior segment were analyzed to validate the findings. The images of this ocular tissue before and after quantum imaging are shown below. Figure 2 , Figure 3 As shown, the number of noise points is reduced after filtering, the image features are more clearly displayed, and the signal-to-noise ratio is improved. To enhance the image display effect, an operation equivalent to the visualization mapping in the 3D reconstruction process was directly applied to the image.
[0035] The three-dimensional structure of the anterior segment of the eye was reconstructed using a 3D reconstruction algorithm. The image of the eye tissue after 3D reconstruction is shown below. Figure 4 As shown, reconstruction can effectively reveal the structural information and hierarchical details of tissues, which is of great significance in ophthalmic histological and pathological analysis.
[0036] The present invention also includes: An ophthalmic quantum imaging system is provided to scan ocular tissues and acquire ocular data containing quantum information, as described in the ophthalmic quantum imaging method.
[0037] An ophthalmic quantum imaging device includes a memory and a processor. The memory stores a computer program that implements the above-described ophthalmic quantum imaging method, and the processor is used to execute the steps of the ophthalmic quantum imaging method.
[0038] A computer-readable storage medium stores a computer program that, when executed by a processor, implements the ophthalmic quantum imaging method. The results obtained through the above steps are analyzed to demonstrate the effectiveness of the method proposed in this invention. Finally, it should be noted that the above embodiments are merely specific implementations of this invention, used to illustrate the technical solutions of this invention, and not to limit it. The scope of protection of this invention is not limited thereto. Although this invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in this invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this invention, and should all be covered within the scope of protection of this invention. Therefore, the scope of protection of this invention should be determined by the scope of the claims.
Claims
1. A method for ophthalmic quantum imaging, characterized in that, Includes the following steps: Step 1: Scan the eye tissue to obtain raw data files containing quantum information in a specified storage format; Step 2: Parse and read the original data file and construct images to obtain high-resolution multi-layer two-dimensional cross-sectional images of ocular tissue; Step 3: Filter the two-dimensional cross-sectional image of the eye tissue; Step 4: Based on the multi-layer two-dimensional tomographic images processed in Step 3, a three-dimensional reconstruction algorithm is used to restore the three-dimensional structure of the eye tissue, generating a three-dimensional rendering that can be used for lesion display or subsequent analysis.
2. The ophthalmic quantum imaging method according to claim 1, characterized in that, The step one involves scanning eye tissue based on a quantum imaging system containing quantum information. The quantum imaging system includes an optical path system for generating and controlling a quantum light source and a quantum detection system for receiving and converting the quantum light into an observable signal.
3. The ophthalmic quantum imaging method according to claim 1, characterized in that, Step two involves parsing and reading the original data file. A Python program based on the tifffile library functions reads continuous value data from the original data file in step one and exports the data needed to construct the image.
4. The ophthalmic quantum imaging method according to claim 1, characterized in that, The image construction described in step two involves bit depth mapping and RGB three-channel data construction based on the read data. Bit depth mapping maps continuous values to the range covered by the given positioning depth. RGB three-channel data construction is based on channel color rendering using different color schemes to obtain a color-mapped image with obvious color contrast.
5. The ophthalmic quantum imaging method according to claim 1, characterized in that, Step three describes the filtering process, which uses Gaussian smoothing filtering to suppress noise in the image and reduce detail at a specified scale. The Gaussian filter function is as follows: (1) in, Take 1.
6. The ophthalmic quantum imaging method according to claim 1, characterized in that, The three-dimensional reconstruction algorithm described in step four includes: 4.1 Volume Data Construction The preprocessed two-dimensional image sequence is stacked in spatial order, and dense isometric interpolation is performed on the layer-by-layer quantum imaging using an isometric interpolation method to form data for constructing a three-dimensional volume. ,in z Dimensions represent the number of layers in an image sequence; 4.2 Data Normalization The constructed 3D volume data value range is mapped to a unified standard interval [0, 1]. (2) 4.3 Maximum Intensity Projection The highest intensity voxel value is found along each view path in the normalized 3D volume data and used as the response value of the corresponding pixel. Specifically, for each pixel coordinate in the normalized 3D volume data... Calculate the 3D volume data along the projection ray perpendicular to the view plane. The maximum value of all voxel values along the corresponding path is used to obtain the two-dimensional intensity matrix. Its mathematical expression is: (3) in, Represents the path along the main viewpoint; 4.4 Visual Mapping A mapping relationship is established between the color and transparency visual channels and data values, and the two-dimensional intensity matrix is mapped using a predefined mapping function. These values are mapped to RGB three-channel color values and transparency values respectively to enhance the color contrast of the image, present an intuitive and easy-to-understand three-dimensional view, and realize the three-dimensional reconstruction of quantum signals.
7. The ophthalmic quantum imaging method according to claim 6, characterized in that, The mapping relationship is as follows: The RGB three-channel color mapping function is predefined Color mapping table specifies: (4) in, For the length of the color map table, This is a round-down operation; Transparency mapping function: (5) in, Transparency Map Length, generally a linear mapping; The final RGB channel composition function for the displayed 3D image is: (6) in, The background color is used to obtain a 3D rendering for lesion display or subsequent analysis.
8. An ophthalmic quantum imaging system, characterized in that, The ophthalmic quantum imaging method according to any one of claims 1 to 7 is used to scan ocular tissue and acquire ocular data containing quantum information.
9. An ophthalmic quantum imaging device, comprising a memory and a processor, the memory storing a computer program for implementing any one of the ophthalmic quantum imaging methods of claims 1 to 7, and the processor being configured to execute the steps of the ophthalmic quantum imaging method.
10. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the ophthalmic quantum imaging method according to any one of claims 1 to 7.