Lung tissue blood oxygen imaging method, device, system, medium and terminal

Abstract

The application provides a lung tissue blood oxygen imaging method, device, system, medium and terminal. The method comprises the following steps: synchronously collecting and preprocessing a white light image, a first near-infrared wavelength image and a second near-infrared wavelength image; extracting multiple scattering components from the first near-infrared wavelength image and the second near-infrared wavelength image respectively based on a single-frame statistical gating technology, so as to obtain a first near-infrared multiple scattering image and a second near-infrared multiple scattering image respectively; extracting scattering information of the white light image in lung tissue according to the white light image, so as to perform depth mismatch correction on the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image; calculating blood oxygen saturation data of lung tissue according to the multiple scattering components after the depth mismatch correction, and finally generating a pseudo-color image in real time. The application can accurately realize precise extraction of blood oxygen saturation information of lung deep tissue, and significantly improve the accuracy of intersegment boundary identification in lung segment resection.

Description

Technical Field

[0001] This application relates to the field of medical imaging technology, and in particular to a method, device, system, medium and terminal for lung tissue blood oxygenation imaging. Background Technology

[0002] In lung cancer surgery, accurately identifying the boundary between the diseased lung segment and the healthy lung segment is crucial for surgical success. Traditionally, surgeons rely on white light illumination under thoracoscopy to determine the segmental boundaries by observing the morphology and color of the lung tissue. This method requires oxygenating the lung tissue during surgery and temporarily blocking blood flow to the target lung segment to induce a color change. However, this traditional method has the following drawbacks:

[0003] (1) Limited recognition accuracy: Color changes mainly reflect the blood oxygen saturation of the surface of lung tissue, and cannot accurately obtain blood oxygen information of deep tissues, which may lead to inaccurate boundary recognition.

[0004] (2) Prolonged operation time: In order to observe obvious color changes, it is usually necessary to wait for more than 20 minutes, which prolongs the operation time and increases the patient's anesthesia risk and surgical burden.

[0005] Therefore, it is necessary to provide a method, device, system, medium, and terminal for lung tissue blood oxygenation imaging to solve the aforementioned problems in the prior art. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a method, device, system, medium and terminal for lung tissue blood oxygenation imaging, so as to solve the technical problem that the prior art cannot accurately obtain blood oxygen saturation information of lung tissue.

[0007] To achieve the above and other related objectives, a first aspect of this application provides a method for lung tissue oxygenation imaging, comprising:

[0008] Simultaneously acquire white light images of lung tissue irradiated with white light, first near-infrared wavelength images of lung tissue irradiated with first wavelength near-infrared light, and second near-infrared wavelength images of lung tissue irradiated with second wavelength near-infrared light, and preprocess the acquired white light images, first near-infrared wavelength images, and second near-infrared wavelength images respectively;

[0009] Based on single-frame statistical gating technology, multiple scattering components are extracted from the preprocessed first near-infrared wavelength image and the second near-infrared wavelength image to obtain the first near-infrared multiple scattering image and the second near-infrared multiple scattering image, respectively.

[0010] The scattering information in the lung tissue is extracted based on the preprocessed white light image, and compared with the multiple scattering information of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image to perform depth mismatch correction on the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image.

[0011] Based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction, the blood oxygen saturation data of the lung tissue were calculated.

[0012] Based on the calculated blood oxygen saturation data of lung tissue, a pseudo-color image is generated in real time.

[0013] In some embodiments of the first aspect of this application, the step of extracting scattering information of the preprocessed white light image in lung tissue and comparing it with the multiple scattering information of a first near-infrared multiple scattering image and a second near-infrared multiple scattering image to perform depth mismatch correction on the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image includes: calculating the scattering index parameters of the preprocessed white light image, the first near-infrared multiple scattering image, and the second near-infrared multiple scattering image respectively to obtain a first scattering index parameter, a second scattering index parameter, and a third scattering index parameter; calculating a local depth mismatch factor based on the first scattering index parameter, the second scattering index parameter, and the third scattering index parameter; calculating a correction factor based on the calculated local depth mismatch factor; and correcting the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image based on the calculated correction factor to obtain the corrected multiple scattering image intensity.

[0014] In some embodiments of the first aspect of this application, the process of calculating the blood oxygen saturation data of lung tissue based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction includes: calculating optical density values ​​based on the intensity of the corrected multiple scattering images to obtain optical density values ​​at a first wavelength and a second wavelength, respectively; establishing a system of linear equations based on the optical density values ​​at the first and second wavelengths, the extinction coefficient of oxyhemoglobin, and the extinction coefficient of deoxyhemoglobin; solving the established system of linear equations to obtain the concentration of oxyhemoglobin and the concentration of deoxyhemoglobin at each pixel; and calculating the blood oxygen saturation data of lung tissue based on the concentrations of oxyhemoglobin and deoxyhemoglobin.

[0015] In some embodiments of the first aspect of this application, a first scattering index parameter of the preprocessed white light image is calculated using a normalized local image intensity method.

[0016] In some embodiments of the first aspect of this application, a second scattering index parameter of the first near-infrared multiple scattering image and a third scattering index parameter of the second near-infrared multiple scattering image are calculated using a local contrast normalization function.

[0017] In some embodiments of the first aspect of this application, the preprocessing method includes one or more of: preliminary calibration, noise reduction processing, and contrast enhancement.

[0018] To achieve the above and other related objectives, a second aspect of this application provides a lung tissue oxygenation imaging device, comprising:

[0019] The image acquisition module is used to simultaneously acquire a white light image of lung tissue irradiated with white light, a first near-infrared wavelength image of lung tissue irradiated with a first wavelength near-infrared light, and a second near-infrared wavelength image of lung tissue irradiated with a second wavelength near-infrared light, and to preprocess the acquired white light image, the first near-infrared wavelength image, and the second near-infrared wavelength image respectively.

[0020] The single-frame statistical gating module is used to extract multiple scattering components from the preprocessed first near-infrared wavelength image and the second near-infrared wavelength image based on the single-frame statistical gating technology, so as to obtain the first near-infrared multiple scattering image and the second near-infrared multiple scattering image respectively.

[0021] The depth mismatch correction module is used to extract the scattering information of the preprocessed white light image in the lung tissue and compare it with the multiple scattering information of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image to perform depth mismatch correction on the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image.

[0022] The blood oxygen saturation calculation module is used to calculate the blood oxygen saturation data of lung tissue based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction.

[0023] The pseudo-color image generation module is used to generate pseudo-color images in real time based on the calculated blood oxygen saturation data of lung tissue.

[0024] To achieve the above and other related objectives, a third aspect of this application provides a lung tissue oxygenation imaging system, the system comprising:

[0025] Thoracoscopic endoscope, handpiece, multi-wavelength light source device, lung tissue blood oxygenation imaging device, and dual-screen display device;

[0026] The multi-wavelength light source device is used to simultaneously emit white light, first-wavelength near-infrared light, and second-wavelength near-infrared light, which enter the thoracoscope and irradiate the lung tissue.

[0027] The tail end of the thoracoscope is optically connected to the head end of the handle, which is used to focus the lung tissue scene onto the photosensitive surface of the image sensor inside the handle. The image sensor generates a white light image, a first near-infrared wavelength image, and a second near-infrared wavelength image.

[0028] The lung tissue oxygenation imaging device is communicatively connected to the tail end of the handle, and is used to synchronously acquire white light images of lung tissue irradiated with white light, first near-infrared wavelength images of lung tissue irradiated with first wavelength near-infrared light, and second near-infrared wavelength images of lung tissue irradiated with second wavelength near-infrared light. The acquired white light images, first near-infrared wavelength images, and second near-infrared wavelength images are preprocessed respectively. Multiple scattering components are extracted from the preprocessed first and second near-infrared wavelength images based on single-frame statistical gating technology to obtain first and second near-infrared multiple scattering images respectively. Scattering information in the lung tissue is extracted from the preprocessed white light images and compared with the multiple scattering information of the first and second near-infrared multiple scattering images to perform depth mismatch correction on the multiple scattering components of the first and second near-infrared multiple scattering images. Based on the depth mismatch corrected multiple scattering components of the first and second near-infrared multiple scattering images, the oxygen saturation data of the lung tissue is calculated. Based on the calculated oxygen saturation data of the lung tissue, a pseudo-color image is generated in real time.

[0029] The dual-screen display device is communicatively connected to the lung tissue blood oxygenation imaging device, and is used to display the white light image and the generated pseudo-color image respectively.

[0030] To achieve the above and other related objectives, a fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method.

[0031] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the method.

[0032] As described above, the lung tissue blood oxygenation imaging method, apparatus, system, medium, and terminal of this application have the following beneficial effects:

[0033] By simultaneously acquiring and preprocessing white light images, first near-infrared wavelength images of lung tissue irradiated with first-wavelength near-infrared light, and second near-infrared wavelength images of lung tissue irradiated with second-wavelength near-infrared light, single-scattering and multiple-scattering components of the preprocessed first and second near-infrared wavelength images are separated based on single-frame statistical gating technology to obtain first and second near-infrared multiple-scattering images. The scattering information in the lung tissue is extracted from the preprocessed white light images and compared with the first and second near-infrared multiple-scattering images. The multiple scattering information of the near-infrared multiple scattering images is compared to correct the depth mismatch of the multiple scattering components of the first and second near-infrared multiple scattering images. Based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction, the blood oxygen saturation data of the lung tissue is calculated. Based on the calculated blood oxygen saturation data of the lung tissue, a pseudo-color image is generated in real time. This can simultaneously eliminate pleural interference and motion artifact interference, accurately extract blood oxygen saturation information of deep lung tissue, and significantly improve the accuracy of inter-segment boundary identification during lung segmentectomy. Attached Figure Description

[0034] Figure 1 The diagram shown is a flowchart of a lung tissue blood oxygenation imaging method according to an embodiment of this application.

[0035] Figure 2 The diagram shown illustrates the working principle of a lung tissue oxygenation imaging method in one embodiment of this application.

[0036] Figure 3 The diagram shown is a schematic representation of a lung tissue oxygenation imaging device according to an embodiment of this application.

[0037] Figure 4 The diagram shown is a schematic diagram of a lung tissue oxygenation imaging system according to an embodiment of this application.

[0038] Figure 5 The diagram shown is a structural schematic of an electronic terminal according to an embodiment of this application. Detailed Implementation

[0039] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0040] In the embodiments of this application, terms such as "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, "first XX" and "second XX" are merely used to distinguish different XXs and do not limit their order. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0041] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0042] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0043] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0044] <1> Single-frame statistical gating is a technique that uses Random Matrix Theory (RMT) to automatically separate single-scattering components from multiple-scattering components within a single frame of an image.

[0045] <2> Differential Pathlength Factor (DPF): Defined as the ratio of the average effective optical path length (<ΔL>) of light in a scattering medium (such as brain tissue, muscle, skin, etc.) to the geometric distance (d) of that medium.

[0046] <3> JET is a rainbow color lookup table whose color sequence transitions from cool blue through cyan and yellow to warm red.

[0047] <4> PARULA: A perceptually uniform continuous color lookup table created by mathematician Peter Kovesi, which later became the new default color table in MATLAB.

[0048] <5> HOT: A color lookup table that simulates the color change of an object as it is heated. It starts with black, passes through red and orange, and eventually turns bright yellow or even white, like a metal block being gradually heated.

[0049] In existing technologies, the following techniques have been developed primarily for identifying the boundary between diseased and healthy lung segments during lung cancer surgery:

[0050] (1) Indocyanine Green (ICG) fluorescence imaging: By intravenously injecting ICG contrast agent during surgery, after blocking the arterial and venous blood flow in the target lung segment, ICG is distributed only in the surrounding healthy lung segments, thereby achieving rapid visualization of the intersegmental boundaries with the aid of fluorescence thoracoscopy. However, although this method can shorten the intraoperative waiting time, the operation steps are complex, requiring the surgical team to be proficient in the timing of contrast agent injection and thoracoscopy operation skills. Furthermore, the duration of ICG fluorescence visualization is short, and if the intraoperative operation is not timely, the visualization effect can easily be weakened or even disappear. More seriously, the ICG contrast agent may diffuse or leak into the target lung segment, causing erroneous visualization of the originally clear intersegmental boundaries, affecting the accuracy of the surgery, and even increasing the risk of postoperative complications.

[0051] (2) Near-infrared spectroscopy (NIRS): This technique utilizes the differences in the scattering and absorption characteristics of near-infrared light at different wavelengths to quantitatively analyze tissue oxygen saturation information and identify intersegmental lung boundaries. However, the lung tissue surface is covered by a thick pleural tissue, which significantly shields against the absorption and scattering of near-infrared light, resulting in a significant reduction in the sensitivity of NIRS technology in lung tissue. This pleural interference makes it difficult for NIRS to accurately acquire absorption or oxygen saturation information of deep lung tissue, leading to blurred tissue oxygen distribution information and making it difficult to achieve clear and accurate intersegmental boundary imaging.

[0052] However, in the practical application of thoracoscopic lung segmentectomy, the aforementioned techniques still cannot simultaneously meet the requirements of simple operation and long-term stable imaging, nor can they completely overcome the influence of pleural tissue interference on deep lung tissue imaging. Therefore, this application provides a lung tissue blood oxygenation imaging method, device, system, medium, and terminal that can simultaneously eliminate pleural interference and motion artifact interference, and can accurately extract deep lung tissue blood oxygen saturation information, significantly improving the accuracy and clinical applicability of intersegmental boundary identification during lung segmentectomy.

[0053] To facilitate understanding of the embodiments of this application, in conjunction with Figure 1 and Figure 2 Detailed explanation. Figure 1 A flowchart illustrating a lung tissue blood oxygenation imaging method according to an embodiment of the present invention is shown. Figure 2 A schematic diagram illustrating the working principle of the lung tissue oxygenation imaging method in this embodiment of the invention is shown. The lung tissue oxygenation imaging method in this embodiment mainly includes the following steps:

[0054] Step S11: Simultaneously acquire white light images of lung tissue irradiated with white light, first near-infrared wavelength images of lung tissue irradiated with first wavelength near-infrared light, and second near-infrared wavelength images of lung tissue irradiated with second wavelength near-infrared light, and preprocess the acquired white light images, first near-infrared wavelength images, and second near-infrared wavelength images respectively.

[0055] In some embodiments of this application, the preprocessing methods include one or more of preliminary calibration, noise reduction, and contrast enhancement. It should be understood that preliminary calibration refers to a series of basic adjustments and corrections performed on the imaging system or the image data itself before formal image analysis or processing to eliminate systematic errors or biases. It aims to ensure that the image data is accurate and consistent in geometric and radiometric characteristics, laying a reliable foundation for subsequent quantitative analysis. Noise reduction refers to a technical process that uses algorithms to reduce or eliminate unnecessary random interference information (i.e., "noise") in digital signals (such as images and audio). Contrast enhancement is a technique that increases the difference in brightness (i.e., contrast) between different regions of an image by adjusting the brightness distribution of image pixels. By preprocessing the white light image, the first near-infrared wavelength image, and the second near-infrared wavelength image respectively, the accuracy of subsequent scattering separation and blood oxygen saturation calculation is ensured.

[0056] In step S11, a white light image, a first near-infrared wavelength image, and a second near-infrared wavelength image are acquired simultaneously, and I is used to... WL (x) represents the white light image, and is used as... This represents an image of lung tissue irradiated with near-infrared light of the first wavelength λ1, using... This image represents lung tissue irradiated with near-infrared light at the second wavelength λ2.

[0057] Step S12: Based on single-frame statistical gating technology, extract multiple scattering components from the preprocessed first near-infrared wavelength image and the second near-infrared wavelength image to obtain the first near-infrared multiple scattering image and the second near-infrared multiple scattering image respectively.

[0058] Because the lung tissue surface is covered by a thick pleural tissue, it has a significant shielding effect on the absorption and scattering of near-infrared light, making it difficult to accurately obtain blood oxygen saturation information of the deep lung tissue and achieve clear imaging of lung segment boundaries. To address this, step S12 of this application employs single-frame statistical gating technology to separate the single scattering signal from the pleural surface from the multiple scattering signal from the deep lung tissue, extracting the multiple scattering components of the first and second near-infrared wavelength images. The first and second near-infrared multiple scattering images were obtained, eliminating interference from pleural tissue. Furthermore, efficient separation of single scattering and multiple scattering signals can be achieved on a single frame image, effectively avoiding the image registration problem caused by motion artifacts.

[0059] The following section will explain single-frame statistical gating technology in detail. Single scattering and multiple scattering have different decorrelation time characteristics: for single scattering components, the decorrelation time τ cS The decorrelation time is longer because they only undergo one scattering, maintaining a persistent optical field correlation; for multiple scattering components, the decorrelation time τ is longer. cM The exposure time is relatively short because photons undergo multiple random scatterings within the medium, leading to rapid decorrelation of the optical field. By selecting an appropriate exposure time T, the condition τ can be satisfied. cM <T<τ cS Then, by using statistical gating, single and multiple scattering components can be effectively separated in a single frame image.

[0060] The statistical properties of speckle are described using the Marchenko-Pastur (MP) and Tracy-Widom (TW) distributions from random matrix theory. The MP distribution describes multiple scattering and represents the eigenvalue distribution of a large-size random matrix; the TW distribution, in order to accurately extract the noise components of multiple scattering, requires correction for the bias of the MP distribution's minimum eigenvalue.

[0061] Step S13: Extract the scattering information of the white light image in the lung tissue based on the preprocessed image, and compare it with the multiple scattering information of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image to perform depth mismatch correction on the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image.

[0062] It should be understood that the penetration depth of near-infrared light of different wavelengths in lung tissue is different, and the penetration depth of the same wavelength is also different in tissues with different characteristics. The tissue characteristics within the imaging area (lung tissue) are different, which will introduce errors. To address this, step S13 of this application performs depth mismatch correction on the first near-infrared multiple scattering image and the second near-infrared multiple scattering image to achieve accurate calculation of blood oxygen saturation in lung tissue.

[0063] In some embodiments of this application, the step of extracting scattering information in lung tissue from the preprocessed white light image and comparing it with the multiple scattering information of the first and second near-infrared multiple scattering images to perform depth mismatch correction on the multiple scattering components of the first and second near-infrared multiple scattering images includes: Step S131: Calculating the scattering index parameters of the preprocessed white light image, the first near-infrared multiple scattering image, and the second near-infrared multiple scattering image respectively to obtain the first, second, and third scattering index parameters; Step S132: Calculating the local depth mismatch factor based on the first, second, and third scattering index parameters; Step S133: Calculating the correction factor based on the calculated local depth mismatch factor; and correcting the multiple scattering components of the first and second near-infrared multiple scattering images based on the calculated correction factor to obtain the corrected multiple scattering image intensity.

[0064] In some embodiments of this application, the first scattering index parameter of the preprocessed white light image is calculated using a normalized local image intensity method.

[0065] In some embodiments of this application, the second scattering index parameter of the first near-infrared multiple scattering image and the third scattering index parameter of the second near-infrared multiple scattering image are calculated using a local contrast normalization function.

[0066] Specifically, in step S131, for the preprocessed white light image, the first scattering index parameter g[I] is calculated using the normalized local image intensity method. WL [x], the specific formula is as follows:

[0067]

[0068] Among them, I WL (x) represents the local intensity value of the white light image at pixel location x; I WL,max I WL,min These represent the local maximum and minimum intensities of a white light image, respectively; ∈ represents a very small positive number (e.g., 10).-8 Prevent division by zero.

[0069] For the first and second near-infrared multiple scattering images, the second scattering index parameter is calculated using the local contrast normalization function. and the third scattering index parameter The specific formula is as follows:

[0070]

[0071] in, They represent wavelengths λ and λ, respectively. i Local mean intensity and standard deviation of multiple scattering images; Indicates wavelength λ i The local intensity value at pixel location x in a multiscattering image; ∈ is a very small positive number (e.g., 10). -8 Prevent division by zero.

[0072] In step S132, based on the first scattering index parameter, the second scattering index parameter, and the third scattering index parameter obtained in step S131, the local depth mismatch factor Δ(x) is calculated, and the specific formula is as follows:

[0073]

[0074] in, Represented as wavelength λ i Below, the effective light attenuation coefficient after scattering separation.

[0075] In step S133, based on the local depth mismatch factor calculated in step S132, the correction factors for the first near-infrared multiple scattering image and the second near-infrared multiple scattering image are calculated respectively. The specific formula is as follows:

[0076]

[0077] The value of β ranges from 0.1 to 0.5.

[0078] The multiple scattering components of the first and second near-infrared multiple scattering images are corrected based on the calculated correction factor to obtain the intensity of the corrected multiple scattering images. The specific formula is as follows:

[0079]

[0080] Traditional dual-wavelength near-infrared saturation measurements often rely solely on the optical properties of each wavelength for qualitative or empirical correction, making it difficult to accurately quantify the differences in depth sampling caused by multiple scattering at different wavelengths. Step S13 of this application estimates the optical properties of different regions of lung tissue by introducing an additional white light image, extracts its scattering information within the tissue, and compares it with a first and a second near-infrared multiple scattering image. This quantifies the depth mismatch between the first and second wavelengths at the same tissue location and uses it as a calibration parameter to correct the multiple scattering components of both the first and second near-infrared multiple scattering images.

[0081] Furthermore, existing technologies typically treat multiple scattering as overall scattering without further depth matching or layer correction. However, step S13 of this application, by performing targeted correction on the multiple scattering image, forces different wavelengths to function within approximately the same (or closer) tissue depth range, thereby significantly reducing depth sampling inconsistencies caused by "wavelength differences."

[0082] Step S14: Calculate the blood oxygen saturation data of lung tissue based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction.

[0083] In some embodiments of this application, the process of calculating the blood oxygen saturation data of lung tissue based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction includes: calculating optical density values ​​based on the intensity of the corrected multiple scattering images to obtain optical density values ​​at a first wavelength and a second wavelength, respectively; establishing a system of linear equations based on the optical density values ​​at the first and second wavelengths, the extinction coefficient of oxyhemoglobin, and the extinction coefficient of deoxyhemoglobin; solving the established system of linear equations to obtain the oxyhemoglobin concentration and deoxyhemoglobin concentration at each pixel; and calculating the blood oxygen saturation data of lung tissue based on the oxyhemoglobin concentration and deoxyhemoglobin concentration.

[0084] Specifically, the optical density (OD) values ​​under the first wavelength λ1 near-infrared light and the second wavelength λ2 near-infrared light are calculated based on the corrected multiple scattering image intensity obtained in step S133. The purpose is to quantify the degree of absorption of lung tissue by the first wavelength λ1 near-infrared light and the second wavelength λ2 near-infrared light. The specific formula is as follows:

[0085]

[0086] in, Given wavelength λ iThe incident light intensity. Based on formula (9), the optical density value at the first wavelength λ1 is calculated respectively. Optical density value at the second wavelength λ2

[0087] Based on the calculated optical density value at the first wavelength λ1 Optical density value at the second wavelength λ2 Extinction coefficient of oxyhemoglobin The extinction coefficient ε of deoxyhemoglobin HbR (λ i Establish a 2×2 linear system of equations, with the following specific formula:

[0088]

[0089] in, ε HbR (λ i (where λ is a known wavelength) i The extinction coefficients of oxygenated and deoxygenated hemoglobin at the site; differential path length factor (DPF), recommended value range: DPF in lung tissue is usually between 3.0 and 7.0.

[0090] Solving the established formula (10) yields the oxyhemoglobin concentration at each pixel. and deoxyhemoglobin concentration c HbR (x), and based on the calculated oxyhemoglobin concentration and deoxyhemoglobin concentration c HbR (x) Calculate the oxygen saturation of lung tissue using the following formula:

[0091]

[0092] Traditional methods only introduce the average differential path length factor (DPF) into the overall or hierarchical model, which cannot provide fine correction for local heterogeneity or pixel-level depth differences. Step S14 of this application defines a "scattering index" at the pixel level and constructs a set of correction factors calculated at the pixel level based on the difference or ratio between white light and multiple scattering of two wavelengths of near-infrared light, making the correction more targeted and flexible.

[0093] Step S15: Based on the calculated blood oxygen saturation data of lung tissue, generate a pseudo-color image in real time.

[0094] Specifically, the calculated oxygen saturation data of lung tissue is converted into a real-time pseudo-color image using standard pseudo-color mapping, which is then displayed intuitively on the surgical monitor. This helps the surgeon determine the boundary between the diseased and healthy lung segments. In other words, pseudo-color mapping converts the oxygen saturation data corresponding to each pixel into a color image using a predefined color lookup table, thereby accurately identifying the boundary between the diseased and healthy lung segments and improving the precision of lung cancer resection surgery. For example, the color lookup table can be a JET, PARULA, or HOT color conversion table.

[0095] The lung tissue oxygenation imaging method of this application, by quantifying and correcting depth mismatch, can minimize the systematic errors caused by differences in sampling depth at different wavelengths, thereby improving the stability and consistency of oxygen saturation measurement results. Furthermore, pixel-level correction factors can perform fine-grained corrections based on local scattering characteristics, fully addressing spatial variations in absorption and scattering coefficients within the tissue. This allows for higher-precision oxygen saturation estimation even in the presence of local lesions or tissue heterogeneity, enhancing adaptability to local tissue heterogeneity. Simultaneously, while existing white light images are only used for visualization, the lung tissue oxygenation imaging method of this application uses white light images for calibration and depth reference, effectively "converting" white light scattering characteristics into a basis for correcting near-infrared multiple scattering, significantly improving the overall information utilization efficiency of the system.

[0096] Meanwhile, the lung tissue oxygenation imaging method of this application, after completing depth correction of the two-wavelength multiple scattering images, can still use traditional mature algorithms such as the Beer-Lambert law or ratio method for tissue oxygen saturation analysis, without requiring significant modifications to the original oxygen saturation inversion formula. This reduces the resistance to implementation and demonstrates good engineering feasibility and compatibility. Furthermore, compared to using advanced three-dimensional optical tomography or full-wave radiation transfer models, this application primarily uses relatively intuitive exponential comparisons and correction factors in the image correction stage, resulting in lower computational and hardware costs, making it suitable for various clinical or research applications. It should be understood that the Beer-Lambert law describes the attenuation of light propagating in a homogeneous medium.

[0097] Figure 3 This is a schematic block diagram of the lung tissue oxygenation imaging device provided in the embodiments of this application. Figure 3 As shown, the lung tissue oxygenation imaging device 300 includes:

[0098] The image acquisition module 301 is used to simultaneously acquire a white light image of lung tissue irradiated by white light, a first near-infrared wavelength image of lung tissue irradiated by a first wavelength near-infrared light, and a second near-infrared wavelength image of lung tissue irradiated by a second wavelength near-infrared light, and to preprocess the acquired white light image, the first near-infrared wavelength image, and the second near-infrared wavelength image respectively.

[0099] The single-frame statistical gating module 302 is used to extract multiple scattering components from the preprocessed first near-infrared wavelength image and the second near-infrared wavelength image based on the single-frame statistical gating technology, so as to obtain the first near-infrared multiple scattering image and the second near-infrared multiple scattering image respectively.

[0100] The depth mismatch correction module 303 is used to extract the scattering information of the preprocessed white light image in the lung tissue and compare it with the multiple scattering information of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image to perform depth mismatch correction on the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image.

[0101] The blood oxygen saturation calculation module 304 is used to calculate the blood oxygen saturation data of lung tissue based on the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image after depth mismatch correction.

[0102] The pseudo-color image generation module 305 is used to generate a pseudo-color image in real time based on the calculated blood oxygen saturation data of lung tissue.

[0103] The lung tissue oxygenation imaging device provided in this application embodiment can output the blood oxygenation distribution results of lung tissue in real time, which is convenient for surgical operators to observe and thus accurately determine the boundary between diseased lung segments and healthy lung segments.

[0104] It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0105] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0106] Figure 4 This is a schematic diagram of the lung tissue oxygenation imaging system provided in an embodiment of this application. Figure 4As shown, the lung tissue oxygenation imaging system includes: a thoracoscope, a handpiece, a multi-wavelength light source device, a lung tissue oxygenation imaging device, and a dual-screen display device.

[0107] The multi-wavelength light source device is used to simultaneously emit white light, a first-wavelength near-infrared light, and a second-wavelength near-infrared light, which enter the optical path inside the thoracoscope to irradiate the lung tissue. The tail end of the thoracoscope is optically connected to the head end of the handle, used to focus the lung tissue scene onto the photosensitive surface of the image sensor inside the handle. The image sensor generates a white light image, a first near-infrared wavelength image, and a second near-infrared wavelength image. The lung tissue blood oxygenation imaging device is communicatively connected to the tail end of the handle, used to simultaneously acquire white light images of lung tissue irradiated by white light, first near-infrared wavelength images of lung tissue irradiated by the first-wavelength near-infrared light, and second near-infrared wavelength images of lung tissue irradiated by the second-wavelength near-infrared light, and preprocesses the acquired white light images, first near-infrared wavelength images, and second near-infrared wavelength images respectively. Based on single-frame statistical gating technology, the preprocessed images are then processed... Multiple scattering components are extracted from the first and second near-infrared wavelength images to obtain first and second near-infrared multiple scattering images, respectively. Scattering information in lung tissue is extracted from the preprocessed white light image and compared with the multiple scattering information of the first and second near-infrared multiple scattering images to correct for depth mismatch in the multiple scattering components. Based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction, the blood oxygen saturation data of the lung tissue is calculated. A pseudo-color image is generated in real-time based on the calculated blood oxygen saturation data of the lung tissue. The dual-screen display device is communicatively connected to the lung tissue blood oxygen imaging device to display the white light image and the generated pseudo-color image, respectively.

[0108] Specifically, the thoracoscope can be equipped with either a standard 0-degree direct-viewing endoscope or a 30-degree oblique-viewing endoscope, depending on clinical needs. The front end of the endoscope is equipped with an optical lens group to ensure that white light and near-infrared laser light meet the parfocal requirement during imaging. This means that at the same observation distance, both the white light image and the dual-wavelength near-infrared light image (the first near-infrared wavelength image and the second near-infrared wavelength image) are clearly imaged on the imaging plane at the rear end of the endoscope. The optical interface at the rear end of the endoscope connects to the handle for image transmission and imaging.

[0109] The handpiece, serving as the core of the entire lung tissue oxygenation imaging system, internally contains three independent image sensors, such as a CMOS camera sensor module, to receive white light (RGB) and two different wavelengths (IR1, IR2) near-infrared laser signals. The end of the handpiece is optically connected to the thoracoscope via a standard optical interface. The three CMOS camera sensor modules effectively separate the light signals transmitted from the thoracoscope through an internal high-precision dichroic mirror system, achieving a strict parfocal design to ensure that the acquired white light image and dual-wavelength IR image are spatially aligned, facilitating high-precision image processing in subsequent algorithms. Intuitive control buttons are located on the handpiece shell, allowing for quick switching between ordinary white light mode and tissue oxygenation imaging mode with a single button press, ensuring simple and efficient operation during surgery. In ordinary white light mode, the surgeon continuously observes multiple frames of images during the operation; in tissue oxygenation imaging mode, it is used to assist the surgeon in determining the lung segment to be removed.

[0110] The lung tissue oxygenation imaging device connects to the end of the handle via a high-speed data transmission interface, acquiring real-time RGB white light image data and image data from two IR cameras. When the tissue oxygenation imaging mode is activated, the device simultaneously acquires and processes data from the white light camera and the two near-infrared cameras (IR1 and IR2). In normal white light mode, the device automatically adjusts the exposure and calibrates the white balance based on the real-time data from the white light camera to ensure image display quality.

[0111] Screen A of the dual-screen display device is dedicated to displaying real-time white-light video footage acquired during thoracoscopy. This screen is always on display, allowing the surgeon to clearly observe the surface of the lung tissue and the movement of surgical instruments. Screen B, when the tissue oxygenation imaging mode is activated, displays a real-time pseudo-color image of lung tissue oxygen saturation calculated and generated by the lung tissue oxygenation imaging device. This image is strictly synchronized with the white-light video on screen A, providing intuitive deep tissue oxygenation information. This allows the surgeon to accurately identify intersegmental boundaries during lung segmentectomy, significantly improving the accuracy and safety of the surgery.

[0112] The multi-wavelength light source device employs a multi-source coupling (beam combining) scheme, specifically including two near-infrared lasers and a broadband white light source. Using high-performance dichroic mirrors with specific reflection / transmission spectral bands and related optical elements, the two near-infrared lasers are precisely combined into the same main optical path via reflection or transmission paths, and then output uniformly with the broadband white light source in an optical beam combiner. The combined beam enters the illumination path inside the thoracoscope through the same high-efficiency optical fiber or fiber bundle, forming highly uniform and flexibly switchable multi-wavelength illumination in the distal illumination region of the endoscope. The multi-wavelength light source device minimizes optical power loss during the beam combining process, ensuring long-term stability and uniformity of the illumination output power. Simultaneously, this multi-source fusion technology can easily achieve white light and dual-wavelength laser fusion illumination without adding additional endoscopes or complex optical structures to the existing endoscopic system structure, providing a solid and reliable technical foundation for real-time, high-quality deep lung tissue oxygenation imaging.

[0113] The lung tissue oxygenation imaging system provided in this application, through the coordinated structural design of the thoracoscope, handpiece, multi-wavelength light source device, lung tissue oxygenation imaging device, and dual-screen display device, effectively achieves high-precision acquisition, real-time processing, and intuitive presentation of white light and dual-wavelength near-infrared light signals, greatly meeting the clinical needs for real-time and accurate identification of lung tissue segment boundaries during thoracoscopic lung segmentectomy.

[0114] Figure 5 This is a schematic block diagram of the electronic terminal provided in an embodiment of this application. Figure 5 As shown, the electronic terminal 500 includes at least one processor 501, a memory 502, at least one network interface 503, and a user interface 505. The various components in the electronic terminal 500 are coupled together via a bus system 504. It is understood that the bus system 504 is used to implement communication between these components. In addition to a data bus, the bus system 504 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 5 The general will label all buses as bus systems.

[0115] The user interface 505 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0116] It is understood that memory 502 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0117] In this embodiment of the invention, the memory 502 is used to store various types of data to support the operation of the electronic terminal 500. Examples of this data include: any executable program for operation on the electronic terminal 500, such as the operating system 5021 and application programs 5022; the operating system 5021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 5022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The implementation of the XX method provided in this embodiment of the invention may be included in the application program 5022.

[0118] The methods disclosed in the above embodiments of the present invention can be applied to processor 501, or implemented by processor 501. Processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 501 or by instructions in the form of software. The processor 501 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 501 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 501 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0119] In an exemplary embodiment, the electronic terminal 500 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0120] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when executed on a computer, causes the computer to perform... Figures 1 to 2 The method of any of the embodiments shown.

[0121] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0122] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0127] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs, DVDs), or semiconductor media (e.g., solid-state disks, SSDs, etc.).

[0128] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0129] In summary, addressing the technical problem that existing technologies cannot accurately obtain lung tissue oxygen saturation information, leading to inaccurate identification of diseased and healthy lung segments, this invention provides a lung tissue oxygenation imaging method, device, system, medium, and terminal. It simultaneously acquires and preprocesses white light images of lung tissue irradiated with white light, first near-infrared wavelength images of lung tissue irradiated with first-wavelength near-infrared light, and second near-infrared wavelength images of lung tissue irradiated with second-wavelength near-infrared light. Based on single-frame statistical gating technology, it separates the single scattering and multiple scattering components of the preprocessed first and second near-infrared wavelength images to obtain first and second near-infrared multiple scattering images. Based on the preprocessed... The scattering information of the white light image in the lung tissue is extracted and compared with the multiple scattering information of the first and second near-infrared multiple scattering images to correct the depth mismatch of the multiple scattering components. Based on the depth mismatch corrected multiple scattering components of the first and second near-infrared multiple scattering images, the blood oxygen saturation data of the lung tissue is calculated. Based on the calculated blood oxygen saturation data of the lung tissue, a pseudo-color image is generated in real time, which can simultaneously eliminate pleural interference and motion artifact interference, accurately extracting blood oxygen saturation information of deep lung tissue and significantly improving the accuracy of inter-segmental boundary identification during segmentectomy. Therefore, this application effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0130] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for lung tissue blood oxygenation imaging, characterized in that, include: Simultaneously acquire white light images of lung tissue irradiated with white light, first near-infrared wavelength images of lung tissue irradiated with first wavelength near-infrared light, and second near-infrared wavelength images of lung tissue irradiated with second wavelength near-infrared light, and preprocess the acquired white light images, first near-infrared wavelength images, and second near-infrared wavelength images respectively; Based on single-frame statistical gating technology, multiple scattering components are extracted from the preprocessed first near-infrared wavelength image and the second near-infrared wavelength image to obtain the first near-infrared multiple scattering image and the second near-infrared multiple scattering image, respectively. The scattering information in the lung tissue is extracted based on the preprocessed white light image, and compared with the multiple scattering information of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image to perform depth mismatch correction on the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image. Based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction, the blood oxygen saturation data of the lung tissue were calculated. Based on the calculated blood oxygen saturation data of lung tissue, a pseudo-color image is generated in real time.

2. The lung tissue oxygenation imaging method according to claim 1, characterized in that, The process of extracting scattering information in lung tissue from the preprocessed white light image and comparing it with the multiple scattering information of the first and second near-infrared multiple scattering images to perform depth mismatch correction on the multiple scattering components of the first and second near-infrared multiple scattering images includes: The scattering index parameters of the preprocessed white light image, the first near-infrared multiple scattering image, and the second near-infrared multiple scattering image are calculated respectively to obtain the first scattering index parameter, the second scattering index parameter, and the third scattering index parameter. The local depth mismatch factor is calculated based on the first scattering index parameter, the second scattering index parameter, and the third scattering index parameter. Based on the calculated local depth mismatch factor, a correction factor is calculated; and based on the calculated correction factor, the multiple scattering components of the first and second near-infrared multiple scattering images are corrected to obtain the intensity of the corrected multiple scattering images.

3. The lung tissue blood oxygenation imaging method according to claim 2, characterized in that, The process of calculating the blood oxygen saturation data of lung tissue based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction includes: The optical density value is calculated based on the intensity of the corrected multiple scattering image to obtain the optical density values ​​at the first wavelength and the second wavelength, respectively. A system of linear equations was established based on the optical density values ​​at the first and second wavelengths, the extinction coefficient of oxyhemoglobin, and the extinction coefficient of deoxyhemoglobin. The established system of linear equations is solved to obtain the concentrations of oxyhemoglobin and deoxyhemoglobin at each pixel; and the oxygen saturation data of the lung tissue is calculated based on the concentrations of oxyhemoglobin and deoxyhemoglobin.

4. The lung tissue oxygenation imaging method according to claim 2, characterized in that, The first scattering index parameter of the preprocessed white light image is calculated using the normalized local intensity method.

5. The lung tissue blood oxygenation imaging method according to claim 2, characterized in that, The second scattering index parameter of the first near-infrared multiple scattering image and the third scattering index parameter of the second near-infrared multiple scattering image are calculated using the local contrast normalization function.

6. The lung tissue blood oxygenation imaging method according to claim 1, characterized in that, The preprocessing methods include one or more of the following: preliminary calibration, noise reduction, and contrast enhancement.

7. A lung tissue blood oxygenation imaging device, characterized in that, include: The image acquisition module is used to simultaneously acquire a white light image of lung tissue irradiated with white light, a first near-infrared wavelength image of lung tissue irradiated with a first wavelength near-infrared light, and a second near-infrared wavelength image of lung tissue irradiated with a second wavelength near-infrared light, and to preprocess the acquired white light image, the first near-infrared wavelength image, and the second near-infrared wavelength image respectively. The single-frame statistical gating module is used to extract multiple scattering components from the preprocessed first near-infrared wavelength image and the second near-infrared wavelength image based on the single-frame statistical gating technology, so as to obtain the first near-infrared multiple scattering image and the second near-infrared multiple scattering image respectively. The depth mismatch correction module is used to extract the scattering information of the preprocessed white light image in the lung tissue and compare it with the multiple scattering information of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image to perform depth mismatch correction on the multiple scattering components of the first near-infrared multiple scattering image and the second near-infrared multiple scattering image. The blood oxygen saturation calculation module is used to calculate the blood oxygen saturation data of lung tissue based on the multiple scattering components of the first and second near-infrared multiple scattering images after depth mismatch correction. The pseudo-color image generation module is used to generate pseudo-color images in real time based on the calculated blood oxygen saturation data of lung tissue.

8. A lung tissue blood oxygenation imaging system, characterized in that, The system includes: a thoracoscope body, a handle, a multi-wavelength light source device, a lung tissue blood oxygenation imaging device, and a dual-screen display device; The multi-wavelength light source device is used to simultaneously emit white light, first-wavelength near-infrared light, and second-wavelength near-infrared light, and the light path entering the thoracoscope body irradiates the lung tissue. The tail end of the thoracoscope is optically connected to the head end of the handle, which is used to focus the lung tissue scene onto the photosensitive surface of the image sensor inside the handle. The image sensor generates a white light image, a first near-infrared wavelength image, and a second near-infrared wavelength image. The lung tissue oxygenation imaging device is communicatively connected to the tail end of the handle, and is used to synchronously acquire white light images of lung tissue irradiated with white light, first near-infrared wavelength images of lung tissue irradiated with first wavelength near-infrared light, and second near-infrared wavelength images of lung tissue irradiated with second wavelength near-infrared light. The acquired white light images, first near-infrared wavelength images, and second near-infrared wavelength images are preprocessed respectively. Multiple scattering components are extracted from the preprocessed first and second near-infrared wavelength images based on single-frame statistical gating technology to obtain first and second near-infrared multiple scattering images respectively. Scattering information in the lung tissue is extracted from the preprocessed white light images and compared with the multiple scattering information of the first and second near-infrared multiple scattering images to perform depth mismatch correction on the multiple scattering components of the first and second near-infrared multiple scattering images. Based on the depth mismatch corrected multiple scattering components of the first and second near-infrared multiple scattering images, the oxygen saturation data of the lung tissue is calculated. Based on the calculated oxygen saturation data of the lung tissue, a pseudo-color image is generated in real time. The dual-screen display device is communicatively connected to the lung tissue blood oxygenation imaging device, and is used to display the white light image and the generated pseudo-color image respectively.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

10. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method as described in any one of claims 1 to 6.

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