Near-infrared lymph node detection system

Abstract

The utility model discloses a near-infrared lymph node detection system. The detection system comprises an infrared imaging system, the infrared imaging system comprises an excitation light source, a beam expanding element, a light path forwarding device, a light filtering device and a short-wave infrared sensor which are arranged along a light path, and an image processing and displaying device which is in communication connection with the short-wave infrared sensor; wherein the excitation light source is a continuous spectrum light source or a wavelength continuously adjustable light source. According to the utility model, based on a near infrared spectrum imaging technology known as a biological transparent window, image processing is carried out in combination with a model algorithm, and an innovative solution is provided for lymph node detection; according to the system, the low tissue scattering characteristic of a near-infrared window and the absorption peak difference characteristic of main biological components in a near-infrared spectrum are utilized, and high-contrast imaging of lymph nodes and surrounding adipose tissues is achieved without a tracer agent. The detection system provided by the utility model can provide real-time guidance and tracking for related operations.

Description

TECHNICAL FIELD

[0001] The utility model relates to a near-infrared lymph node detection system and belongs to the medical detection technical field. BACKGROUND

[0002] In the field of tumor treatment, especially for common malignant tumors such as breast cancer, lymph node metastasis as the main route of tumor cell spread has a decisive influence on pathological staging, treatment plan selection and prognosis evaluation. The judgment standard of whether the lymph node is metastatic usually depends on the pathological examination after surgical resection, but this process faces great challenges. The operator needs to quickly and accurately locate the lymph node in the adipose tissue around the tumor and evaluate its morphology to ensure that suspicious lymph nodes can be removed as much as possible to prevent the spread of tumor cells. However, the current intraoperative detection of lymph nodes is mainly based on experience and visual observation, which is time-consuming and not accurate, so there is an urgent need for a technology that can easily, quickly and accurately detect lymph nodes.

[0003] There are some lymph node detection technologies on the market, such as radioactive tracers and ultrasound guidance, but these methods have many limitations in practical application. Radioactive tracers may expose unnecessary radiation risk and are costly, while ultrasound guidance technology is radiation-free but requires high skills of the operator. These technologies are difficult to achieve easy, fast and accurate intraoperative lymph node detection, resulting in low efficiency of lymph node positioning and resection during surgery.

[0004] The near-infrared lymph node detection system, based on the near-infrared spectral imaging technology known as the "biological transparency window", provides an innovative solution for lymph node detection combined with advanced model algorithms. The system takes advantage of the low tissue scattering characteristics of the near-infrared window and the absorption peak difference characteristics of the main biological components in the near-infrared spectrum to achieve high-contrast imaging of lymph nodes and surrounding adipose tissue. This unique imaging method allows for quick and accurate intraoperative lymph node localization, improving the accuracy and safety of surgery. At the same time, real-time image feedback can improve surgical efficiency and shorten surgery time. In addition, the imaging system algorithm is optimized to make lymph node detection clearer and more sensitive. The development of this technology effectively addresses the shortcomings of existing intraoperative lymph node detection techniques, providing a safer and more effective treatment option and offering a new perspective for the study and treatment of lymphatic system diseases. SUMMARY

[0005] The utility model aims at the shortage of prior art, the utility model provides a kind of near-infrared lymph node detection system and image acquisition method.

[0006] To achieve the above objectives, this utility model provides a near-infrared lymph node detection system, including an infrared imaging system. The infrared imaging system includes an excitation light source, a beam expander, an optical path repeater, a filter, and a short-wave infrared sensor arranged along the optical path, as well as an image processing and display device communicatively connected to the short-wave infrared sensor. The excitation light source is a continuous spectrum light source or a continuously adjustable wavelength light source.

[0007] Preferably, the shortwave infrared sensor includes a lens and a detector, and the image processing and display device includes a signal receiving module and an image processing / display module. The signal receiving module is used to receive infrared data detected by the detector, and the infrared data is processed and displayed by the image processing / display module.

[0008] Preferably, the image processing / display module performs image processing and display based on the OpenCV and NumPy libraries.

[0009] Preferably, the excitation light source is an incandescent tungsten filament lamp, a continuous laser, or a modulated laser.

[0010] Preferably, it also includes a lifting developing stage for adjusting the height of the infrared imaging system.

[0011] This utility model also provides a method for obtaining detection images based on the above-mentioned near-infrared lymph node detection system, comprising: adjusting the absorption wavelength to 1000-1700nm through a filter device, acquiring an initial image of the lymph node tissue to be tested using an infrared imaging system, wherein the initial image is processed and displayed by an image processing / display module in an image processing and display device to obtain a detection image of the lymph node tissue.

[0012] Compared with the prior art, the beneficial effects of this utility model are as follows:

[0013] The detection system provided by this invention, based on near-infrared spectral imaging technology, known as the "biological transparent window," and combined with model algorithms for image processing, offers an innovative solution for lymph node detection. This system utilizes the low tissue scattering characteristics of the near-infrared window and the difference in absorption peaks of major biological components in the near-infrared spectrum (lymph nodes absorb more near-infrared light than adipose tissue, thus appearing distinctly black in near-infrared imaging), achieving high-contrast imaging of lymph nodes and surrounding adipose tissue without the need for tracers. This unique imaging method allows for rapid and accurate intraoperative lymph node localization, improving surgical precision and safety. Simultaneously, real-time image feedback enhances surgical efficiency and shortens operation time. Furthermore, the optimized imaging system algorithm makes lymph node detection clearer and more sensitive. Therefore, this invention effectively overcomes the shortcomings of existing intraoperative lymph node detection technologies, provides a safer and more effective surgical guidance plan, and offers a new perspective for the research and treatment of lymphatic system diseases. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall structure of the near-infrared lymph node detection system provided in the embodiment.

[0015] Reference numerals: 1. Excitation source; 2. Short-wave infrared sensor; 3. Optical path repeater; 4. Beam expander; 5. Filtering device; 6. Lifting developing stage; 7. Image processing and display device; 8. Adipose tissue; 9. Lens; 10. Detector; 11. Dichroic mirror; 12. Mirror; 13. Filter wheel; 14. Filter. Detailed Implementation

[0016] To make this utility model more apparent and understandable, preferred embodiments are described in detail below with reference to the accompanying drawings.

[0017] Example 1

[0018] This embodiment provides a near-infrared lymph node detection system, such as... Figure 1 As shown, it includes an infrared imaging system, which comprises an excitation light source 1, a beam expander 4, an optical path repeater 3, a filter 5, and a short-wave infrared sensor 2 arranged along the optical path, as well as an image processing and display device 7 communicatively connected to the short-wave infrared sensor 2; wherein:

[0019] The excitation source 1 is a light source capable of emitting a continuous spectrum covering the near-infrared band (1000-1700nm) or a continuously tunable light source. The continuous spectrum light source can be an incandescent tungsten filament lamp, a continuous laser, etc.; the continuously tunable light source can be a modulated laser.

[0020] The optical path forwarding device 3 includes a dichroic mirror 11 and a reflector 12; the dichroic mirror 11 allows light of a specific wavelength to pass through while reflecting light of other wavelengths, and the reflector 12 is used to change the direction of light propagation to ensure that the light can propagate along the expected path.

[0021] The filtering device 4 includes a filter wheel 13 and a filter 14. The filter wheel is a component used to control and adjust the spectral distribution of the light source in spectral imaging. It mainly consists of a drive motor, bearings, a wheel, and optical filters. The drive motor rotates the wheel, changing the position of the filter, thus selecting and filtering the spectrum to achieve infrared spectral imaging. The filter wheel plays an important role in infrared spectral imaging; by selecting and controlling the spectral filtering, interference between different materials can be eliminated, improving the accuracy and precision of the detection results.

[0022] The shortwave infrared sensor 2 includes a lens 9 and a detector 10. The main function of the lens 9 in the shortwave infrared sensor 2 is to converge the shortwave infrared light emitted by the observed object and focus it onto the image sensor, thereby achieving high-resolution imaging.

[0023] The image processing and display device 7 includes a signal receiving module and an image processing / display module. The signal receiving module is used to receive infrared data detected by the detector 10. The infrared data is processed by the image processing module. The image processing / display module performs image processing and display based on the OpenCV library and the NumPy library.

[0024] The detection system also includes a lifting imaging stage 6, which is used to adjust the height of the infrared imaging system relative to the patient's detection site.

[0025] The working principle and process of the above-mentioned lymph node detection system are as follows:

[0026] The near-infrared light or continuous light emitted by the excitation light source 1 is expanded by the beam expander 4 and then enters the dichroic mirror 11, so that the near-infrared light of a specific wavelength is irradiated onto the tested adipose tissue 8 (which is not part of the system). The infrared light reflected by the reflector 12 of the tested adipose tissue is filtered by the filter device 5 to filter the visible light. After the filter 14 is adjusted to the absorption peak band of the lymph node (1000-1700nm), the received visible light is filtered and cut off by the filter 14 and then focused by the front lens 9. After focusing, it is received by the detector 10. Since the detector only receives near-infrared light of a specific band, it outputs near-infrared light to the infrared detector 10. The detector 10 identifies the received infrared light and generates an electrical signal. The image processing and display device 7 receives the preliminary image and then performs image processing and image display through the image processing / display module.

[0027] The specific operations for image processing and image display are as follows:

[0028] # Read black and white images

[0029] image = cv2.imread('test.tif', cv2.IMREAD_GRAYSCALE)

[0030] # Convert the image to single-precision floating-point numbers for numerical computation.

[0031] image_float = image.astype(np.float32)

[0032] def sharpen_image(image):

[0033] # Define the sharpening kernel

[0034] kernel = np.array([[-1, -1, -1],

[0035] [-1, 9, -1],

[0036] [-1, -1, -1]])

[0037] # Applying convolution operations

[0038] sharpened_image = cv2.filter2D(image, -1, kernel)

[0039] return sharpened_image

[0040] mi = 75

[0041] # Set values ​​less than a to a

[0042] image_float[image_float <mi] = mi

[0043] mx = 200

[0044] image_float[image_float>mx] = mx

[0045] # Calculate the minimum and maximum values

[0046] min_val = np.min(image_float)

[0047] max_val = np.max(image_float)

[0048] # Stretch the grayscale values ​​of the image to between 0 and 255 using a linear transformation.

[0049] image_float = (image_float - min_val) / (max_val - min_val) * 255

[0050] sharpened_image = sharpen_image(image_float)

[0051] # Convert the floating-point image back to an 8-bit unsigned integer

[0052] enhanced_image_8bit = image_float.astype(np.uint8)

[0053] # Display the processed image

[0054] cv2.imshow('Enhanced Image', enhanced_image_8bit)

[0055] cv2.waitKey(0)

[0056] cv2.destroyAllWindows()

[0057] # Save the processed image

[0058] cv2.imwrite('result_shape.tif', enhanced_image_8bit).

[0059] The detection image is obtained through the above steps.

[0060] The above-described embodiments are merely preferred embodiments of this utility model and are not intended to limit this utility model in any form or substance. It should be noted that those skilled in the art can make several improvements and additions without departing from this utility model, and these improvements and additions should also be considered within the scope of protection of this utility model.

Claims

1. A near-infrared lymph node detection system, characterized by, The infrared imaging system comprises an excitation light source, a beam expander, a light path transfer device, a light filter device and a short-wave infrared sensor arranged along an optical path, and an image processing and display device in communication connection with the short-wave infrared sensor; wherein the excitation light source is a continuous spectrum light source or a wavelength continuously adjustable light source.

2. The near-infrared lymph node detection system of claim 1, wherein, The short-wave infrared sensor comprises a lens and a detector, and the image processing and display device comprises a signal receiving module and an image processing / display module; the signal receiving module is used for receiving infrared data detected by the detector, and the infrared data is subjected to image processing and image display by the image processing / display module.

3. The near infrared lymph node detection system of claim 1, wherein, The image processing / display module is based on OpenCV library and NumPy library for image processing and display.

4. The near-infrared lymph node detection system of any one of claims 1-3, wherein, The excitation light source is an incandescent tungsten filament lamp, a continuous laser or a modulatable laser.

5. The near-infrared lymph node detection system of any one of claims 1-3, wherein, Further comprising a lifting developing table for adjusting the height of the infrared imaging system.

Images