Photoacoustic imaging apparatus and detection method for kidney tumor margin detection

Abstract

The application provides a photoacoustic imaging device and a detection method for kidney tumor margin detection. The photoacoustic imaging device for kidney tumor margin detection comprises an energy laser, an ultrasonic transducer comprising a plurality of probes arranged in a ring array, an optical excitation ultrasonic module and an optical detection ultrasonic module arranged in the probes, the optical excitation ultrasonic module is used for receiving pulsed laser from the energy laser and converting the pulsed laser into pulsed ultrasonic waves, the pulsed ultrasonic waves are emitted through an ultrasonic wave emission port, the optical detection ultrasonic module adopts a pi phase shift Bragg grating for converting ultrasonic signals entering through an ultrasonic wave receiving port into laser signals, the Q value of the pi phase shift Bragg grating is not more than 10 5 , and the pi phase shift Bragg grating can receive ultrasonic signals with a center frequency of 50 MHz and a bandwidth of 120%; and a control system is used for receiving the laser signals. The technical scheme of the application solves the problems of long detection time and low detection precision of the photoacoustic imaging device in the prior art.

Description

Technical Field

[0001] This invention relates to the field of intraoperative diagnostic technology in medical imaging, and more specifically, to a photoacoustic imaging device and detection method for detecting renal tumor resection margins. Background Technology

[0002] Kidney cancer is one of the most common malignant tumors worldwide, currently ranking tenth in incidence among cancers globally. Surgical resection is the core treatment for radical renal cancer, and the core principle of the surgery is to completely remove the tumor lesion while preserving as much healthy kidney tissue as possible. Therefore, kidney-preserving surgery has become the mainstream surgical procedure for renal cancer treatment. Accurate detection and assessment of the tumor margin during surgery is crucial for controlling surgical quality and determining the patient's postoperative recovery. Currently, clinical detection of surgical margins for kidney tumors in China mainly relies on intraoperative frozen section pathology. However, this traditional method has many inherent technical limitations and cannot meet the high-precision, high-efficiency needs of clinical surgery. It also suffers from a long detection cycle, leading to higher surgical risks. Specifically, existing traditional detection methods mostly adopt non-array single-point ultrasound detection mode, which has a small detection coverage and extremely low scanning efficiency. Therefore, the entire process of sample processing, slide observation and result interpretation in traditional intraoperative frozen pathology testing usually takes more than 30 minutes, which greatly prolongs the overall operation time. Prolonged operation not only increases the risk of anesthesia exposure for patients, but also increases the probability of surgical wound infection and intraoperative complications, which greatly affects the safety of the operation.

[0003] However, traditional piezoelectric ultrasonic array detection technology has inherent device defects. The ultrasonic main frequency and bandwidth parameters of each piezoelectric transducer are significantly different, resulting in non-uniformity of ultrasonic response amplitude and imaging resolution of each detection unit in the array. This causes system imaging distortion and resolution reduction, making it difficult to meet the high-precision detection requirements of small tumor margins. Summary of the Invention

[0004] The main objective of this invention is to provide a photoacoustic imaging device and detection method for detecting renal tumor margins, in order to solve the problems of long detection time and low detection accuracy of existing photoacoustic imaging devices.

[0005] To achieve the above objectives, the present invention provides a photoacoustic imaging device for detecting renal tumor resection margins, comprising: an energy laser for emitting pulsed laser light; and an ultrasonic transducer including multiple probes arranged in a ring array, each probe having an ultrasonic emission port and an ultrasonic receiving port. Each probe contains an optically excited ultrasonic module and an optically detected ultrasonic module. The optically excited ultrasonic module receives the pulsed laser light from the energy laser and converts it into pulsed ultrasonic waves, which are emitted through the ultrasonic emission port. The optically detected ultrasonic module employs a π-phase-shift Bragg grating to convert the ultrasonic signal entering through the ultrasonic receiving port into a laser signal. The Q value of the π-phase-shift Bragg grating does not exceed 10. 5 It can receive ultrasonic signals with a center frequency of 50MHz and a bandwidth of 120%; the control system is used to receive laser signals for analysis and imaging.

[0006] Furthermore, the photoacoustic imaging device for detecting renal tumor margins also includes a detection laser, and the optical detection ultrasound module includes a signal sensing fiber. The signal sensing fiber passes through multiple probes sequentially along the axial direction. Multiple π-phase-shifted Bragg gratings are etched axially inside the signal sensing fiber so that each probe has a π-phase-shifted Bragg grating. One end of the signal sensing fiber is connected to the detection laser, and the other end of the signal sensing fiber is connected to the control system.

[0007] Furthermore, the detection laser is a tunable narrowband laser, which is used to lock the wavelength of the pulsed laser within the -3dB linear slope region of the reflection spectrum of the π phase-shifted Bragg grating.

[0008] Furthermore, the optically excited ultrasound module includes a composite film made of multi-walled carbon nanotubes and polymethyl methacrylate to convert picosecond pulsed lasers into ultra-high frequency and wide-bandwidth pulsed ultrasound.

[0009] Furthermore, the thickness of the composite film does not exceed 25 μm.

[0010] Furthermore, the proportion of multi-walled carbon nanotubes in the total mass of the composite film is 1%-10%.

[0011] Furthermore, the probe is equipped with an energy transmission fiber, with the emitting end of the energy transmission fiber facing the ultrasonic emission port. The optical excitation ultrasonic module is located between the ultrasonic emission port and the emitting end of the energy transmission fiber. The energy transmission fibers of multiple probes are connected to the energy laser through an optical fiber bundle.

[0012] Furthermore, the photoacoustic imaging device for detecting renal tumor margins also includes a drive component connected to the control system. The output end of the drive component is connected to the ultrasonic transducer drive, and the drive component is used to drive the ultrasonic transducer to perform linear displacement relative to the tissue to be tested.

[0013] Furthermore, the control system includes an automated imaging module, which includes: a signal acquisition unit, comprising a wavelength division multiplexer and a photodetector, wherein the output end of the signal sensing fiber is connected to the wavelength division multiplexer, the wavelength division multiplexer is connected to the photodetector, and the photodetector is used to convert optical signals into electrical signals; and a data processing host, wherein the output end of the photodetector is connected to the data processing host, and is used to receive the processed electrical signals and perform image reconstruction.

[0014] According to another aspect of the present invention, the present invention provides a method for detecting renal tumor resection margins, which uses the above-described photoacoustic imaging device for detecting renal tumor resection margins.

[0015] Furthermore, the renal tumor resection margin detection method includes: a sample marking step: marking the detection area at the junction of the tumor and normal tissue in the resected renal tumor specimen; a parameter configuration step: setting the laser pulse width of the energy laser, the ultrasound detection frequency of the ultrasound machine, and the scanning step size; a multimodal scanning step: performing a three-dimensional scan of the marked area through a ring-shaped array of multiple probes and a driving component, simultaneously acquiring ultrasound and photoacoustic images; an AI discrimination step: performing FFT processing on the radio frequency signal and calling the AI ​​model to discriminate the resection margin status; and a result output step: outputting a detection report and comparing it with the pathological gold standard for verification.

[0016] By employing an ultrasonic transducer constructed from multiple probes arranged in a ring array, multi-point synchronous parallel detection can be achieved, significantly reducing intraoperative detection time and solving the problem of time-consuming traditional pathological examination. This effectively shortens the operation time and avoids the increased risk of anesthesia exposure, wound infection, and intraoperative complications caused by the excessively long operation time of traditional frozen section pathological examination. Furthermore, this invention abandons the traditional piezoelectric ultrasonic array with inherent device defects, using a π-phase-shift Bragg grating as the optical detection ultrasonic module. This module can receive ultrasonic signals with a center frequency of 50MHz and a bandwidth of 120%. Based on a uniform fiber substrate, the detection sensitivity and imaging resolution of all sub-gratings within the array are highly consistent, eliminating the parameter differences of traditional array devices. This hardware-level guarantee ensures the imaging uniformity and high-precision imaging performance of the entire imaging system, thus solving the problems of system imaging distortion and resolution degradation, meeting the high-precision detection requirements of small tumor margins. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0018] Figure 1 A schematic diagram of an embodiment of the photoacoustic imaging device for renal tumor margin detection of the present invention is shown;

[0019] Figure 2 It shows Figure 1 A magnified view of a photoacoustic imaging device used for renal tumor margin detection;

[0020] Figure 3 A schematic diagram of an embodiment of the renal tumor resection margin detection method of the present invention is shown.

[0021] The above figures include the following reference numerals:

[0022] 20. Energy laser; 10. Ultrasonic transducer; 11. Probe; 12. Optical excitation ultrasonic module; 13. Optical detection ultrasonic module; 15. Energy transmission fiber; 16. Fiber bundle; 21. Wavelength division multiplexer; 22. Photodetector; 23. Data processing host; 100. Tissue to be tested. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a photoacoustic imaging device for detecting renal tumor margins, comprising: an energy laser 20 for emitting pulsed laser light; and an ultrasonic transducer 10 including multiple probes 11 arranged in a ring array. Each probe 11 has an ultrasonic emission port and an ultrasonic receiving port. Each probe 11 contains an optically excited ultrasonic module 12 and an optically detected ultrasonic module 13. The optically excited ultrasonic module 12 receives the pulsed laser light from the energy laser 20 and converts it into pulsed ultrasonic light, which is emitted through the ultrasonic emission port. The optically detected ultrasonic module 13 employs a π-phase-shift Bragg grating to convert the ultrasonic signal entering through the ultrasonic receiving port into a laser signal. The Q value of the π-phase-shift Bragg grating does not exceed 10. 5 It can receive ultrasonic signals with a center frequency of 50MHz and a bandwidth of 120%; the control system is used to receive laser signals for analysis and imaging.

[0025] In the above technical solution, by using an ultrasonic transducer constructed with multiple probes 11 arranged in a ring array, multi-point synchronous parallel detection can be achieved, significantly reducing the intraoperative detection time and solving the problem of long-term traditional pathological detection. This effectively shortens the operation time and avoids the increased risk of anesthesia exposure, wound infection, and intraoperative complications caused by the excessively long operation time of traditional frozen pathological detection. At the same time, this invention abandons the traditional piezoelectric ultrasonic array with inherent device defects and uses a π phase-shift Bragg grating as the optical detection ultrasonic module. It can receive ultrasonic signals with a center frequency of 50MHz and a bandwidth of 120%. This type of device is based on a uniform optical fiber substrate, and the detection sensitivity and imaging resolution of all sub-gratings in the array are highly consistent, eliminating the parameter difference problem of traditional array devices. From the hardware level, it ensures the imaging uniformity and high-precision imaging performance of the entire imaging system. In this way, the problems of system imaging distortion and resolution reduction can be solved to meet the high-precision detection requirements of small tumor margins.

[0026] In some embodiments, the probe includes a probe housing, the number of probes 11 is 64, and the overall diameter of the array imaging probe 11 is no more than 16cm, which is suitable for the limited operating space in the operating room.

[0027] In some embodiments, the bandwidth of the ultrasonic transducer 10 is not less than 120%, and the acoustic sensitivity is 10 times higher than that of conventional devices.

[0028] like Figure 1 and Figure 2 As shown in the embodiment of the present invention, the photoacoustic imaging device for detecting renal tumor margins further includes a detection laser. The optical detection ultrasound module 13 includes a signal sensing fiber. The signal sensing fiber passes through multiple probes 11 sequentially along the axial direction. Multiple π phase-shift Bragg gratings are etched axially inside the signal sensing fiber so that each probe 11 has a π phase-shift Bragg grating. One end of the signal sensing fiber is connected to the detection laser, and the other end of the signal sensing fiber is connected to the control system.

[0029] In the above technical solution, by setting up a detection laser and a signal sensing fiber that passes through multiple probes sequentially along the axial direction and is engraved with multiple π phase-shift Bragg gratings, the reflected laser signals generated by each probe can be coupled to the same signal sensing fiber for transmission through gratings at different positions. This avoids the problem of inconsistent detection sensitivity and imaging resolution of all sub-gratings in the array caused by laying independent signal fibers for each probe. It also eliminates the parameter differences of traditional array devices, ensuring the imaging uniformity and high-precision imaging performance of the entire imaging system from the hardware level. In this way, the problems of system imaging distortion and resolution reduction can be solved to meet the high-precision detection requirements of small tumor margins.

[0030] Specifically, the energy laser 20 emits pulsed laser light, which is transmitted via the energy transmission fiber 15 to the optical excitation ultrasound module 12 of each probe 11 in the ring array. The optical excitation ultrasound module 12 converts the laser energy into pulsed ultrasound waves and emits them into the kidney tissue through the ultrasound emission port. Ultrasound echoes generated inside the kidney tissue enter the probe 11 through the ultrasound receiving port and act on the optical detection ultrasound module 13. Through the elastic-optical effect and geometric deformation effect, the wavelength of the Bragg grating drifts (the amount of drift is proportional to the ultrasound sound pressure). At this time, the ultrasound signal is demodulated by a demodulation scheme. For example, a tunable narrowband laser locks the detection laser wavelength in the -3dB linear slope region of the π phase-shifted Bragg grating reflection spectrum, so that the small wavelength drift can be efficiently converted into a significant change in the intensity or phase of the reflected light (i.e., the reflected laser power spectrum changes with the wavelength drift). Thus, the ultrasound signal is converted into an optical signal with high sensitivity. The control system receives the optical signal carrying the ultrasound signal and converts it into an electrical signal. After signal processing, reconstruction, and artificial intelligence analysis, a photoacoustic image reflecting the tissue microstructure and chemical composition is generated, realizing the accurate detection of the renal tumor resection margin.

[0031] In terms of high-frequency detection adaptation, existing fiber Bragg grating ultrasound detection technology suffers from an excessively low upper limit of detection frequency. The effective length of a conventional fiber Bragg grating directly determines the upper limit of the ultrasound response frequency. Due to structural design limitations, its maximum responsive ultrasound frequency is only 0.95MHz, making it difficult to meet the clinical medical needs for 10MHz-30MHz high-frequency ultrasound detection and hindering the precise identification of minute tumor boundaries. Therefore, this invention employs an ultra-short effective length π-phase-shift Bragg grating as the core device for ultrasound detection. This grating array can stably respond to high-frequency, high-bandwidth medical ultrasound signals. Based on this device, the grating array can accurately capture subtle acoustic differences in kidney tissue, efficiently identify the boundary features between tumors and normal tissues, significantly improve the resolution and accuracy of kidney tumor resection margin detection, and perfectly meet the high-precision clinical detection needs during surgery.

[0032] like Figure 1 and Figure 2 As shown, in an embodiment of the present invention, the detection laser is a tunable narrowband laser, which is used to lock the wavelength of the pulsed laser in the -3dB linear slope region of the reflection spectrum of the π phase-shifted Bragg grating.

[0033] In the above technical solution, the ultrasonic signal is demodulated through a demodulation scheme, such as: a tunable narrowband laser is used to lock the wavelength of the pulsed laser in the -3dB linear slope region of the reflection spectrum of the π phase-shift Bragg grating. By utilizing the wavelength-reflectivity (or phase) conversion characteristics of the Bragg grating in this linear region, the small Bragg wavelength drift caused by ultrasound is efficiently converted into a large change in light intensity or phase, thereby improving the detection sensitivity of the ultrasonic signal. This can avoid the sensitivity saturation or nonlinear distortion problems that may occur when detecting in the center or edge of the linear region, so as to achieve high-sensitivity detection of ultrasonic signals.

[0034] In some embodiments, the wavelength locking accuracy of the tunable narrowband laser is not less than 0.1 nm.

[0035] like Figure 1 and Figure 2 As shown, in an embodiment of the present invention, the optically excited ultrasound module 12 includes a composite film made of multi-walled carbon nanotubes (MWCNTs) and polymethyl methacrylate (PMMA) to convert picosecond pulsed lasers into ultra-high frequency and wide bandwidth pulsed ultrasound.

[0036] In the above technical solution, multi-walled carbon nanotubes have the characteristic of efficiently absorbing laser energy, while polymethyl methacrylate, with its high transparency and high hardness, converts the absorbed laser energy into ultra-high frequency, wide bandwidth pulsed ultrasound (pulse width in the picosecond range), thus providing support for high-resolution imaging.

[0037] In the embodiments of the present invention, the thickness of the composite film does not exceed 25 μm.

[0038] In the above technical solution, by limiting the thickness of the composite film, the penetration depth and thermal diffusion distance of the laser inside the film can be effectively reduced, so as to excite the generation of high-frequency ultrasound with narrower pulse width and richer frequency components. This can improve the bandwidth and center frequency of the ultrasound signal, so as to avoid signal broadening and high-frequency component attenuation caused by thermal diffusion due to excessive film thickness, thereby ensuring stable laser energy absorption and ultrasound conversion efficiency.

[0039] In some embodiments, the composite film is prepared by solution casting.

[0040] like Figure 1 and Figure 2 As shown in the embodiments of the present invention, the proportion of multi-walled carbon nanotubes in the total mass of the composite film is 1%-10%.

[0041] By using the above ratio, while ensuring that the composite film has sufficient light absorption capacity to efficiently generate broadband ultrasound signals, it effectively avoids the problems of decreased film transmittance, deteriorated mechanical properties, or local thermal damage caused by excessive absorption of laser energy due to excessive carbon nanotube content. This can improve the resolution and contrast of photoacoustic imaging, thereby ensuring the accuracy and reliability of renal tumor resection margin detection.

[0042] like Figure 1 and Figure 2 As shown, in an embodiment of the present invention, an energy transmission fiber 15 is provided inside the probe 11, and the emitting end of the energy transmission fiber 15 is arranged facing the ultrasonic emission port. The optical excitation ultrasonic module 12 is arranged between the ultrasonic emission port and the emitting end of the energy transmission fiber 15. The energy transmission fibers 15 of multiple probes 11 are connected to the energy laser 20 through an optical fiber bundle 16.

[0043] In the above technical solution, the energy laser 20 emits pulsed laser light, which enters the corresponding energy transmission fiber 15 inside each probe 11 through the fiber bundle 16 and is transmitted along the fiber axis to its emission end. Since the emission end of the energy transmission fiber 15 is set towards the ultrasonic emission port, the laser light directly irradiates and deposits on the composite film located at the ultrasonic emission port after being emitted from the emission end. The composite film efficiently absorbs the laser energy and rapidly generates a thermoelastic expansion effect, thereby exciting ultra-high frequency, large bandwidth pulsed ultrasonic waves at the film interface. The ultrasonic waves are focused by the acoustic lens and emitted into the kidney tissue to be tested, completing the photoacoustic excitation process.

[0044] In some embodiments, the ultrasonic transducer further includes an acoustic lens located at the ultrasonic emission port to focus the ultrasonic waves.

[0045] like Figure 1 and Figure 2 As shown in the embodiment of the present invention, the photoacoustic imaging device for detecting renal tumor margins further includes a driving component connected to the control system. The output end of the driving component is driven and connected to the ultrasonic transducer 10. The driving component is used to drive the ultrasonic transducer 10 to perform linear displacement relative to the tissue 100 to be tested.

[0046] In the above technical solution, by setting a driving component to drive the ultrasonic transducer 10 of the ring array to perform linear displacement relative to the tissue 100 to be tested, the expansion from two-dimensional planar scanning to three-dimensional stereoscopic scanning can be realized. The ring probe array simultaneously performs multi-point acquisition in the XY plane to form a high-resolution two-dimensional slice image, while the driving component drives the entire probe array to perform linear displacement along the Z-axis, thereby changing the detection depth. By continuously acquiring two-dimensional slice data at different depths and superimposing and reconstructing them, the final result is a full-domain three-dimensional photoacoustic imaging data reflecting the three-dimensional structure, blood vessel distribution and chemical composition spatial distribution inside the kidney tissue.

[0047] In one embodiment, the driving component includes a rotary motor and a lead screw and nut mechanism. Specifically, the rotary motor (preferably a stepper motor or a servo motor) is fixedly mounted on the bracket, and its output shaft is coaxially connected to one end of the lead screw. The lead screw passes through the bracket axially and through a movable slider connected to the ultrasonic transducer 10. The movable slider slides with the bracket via a linear guide rail. The nut is located inside the movable slider and engages with the lead screw thread. When the rotary motor starts and drives the lead screw to rotate, the movable slider cannot rotate with the lead screw because it is restricted by the linear guide rail. This converts the rotational motion of the lead screw into a linear displacement of the movable slider along the axis of the lead screw, thereby driving the ultrasonic transducer 10 fixed on the movable slider to perform linear reciprocating motion relative to the tissue 100 to be tested.

[0048] In one embodiment, the probe housing is made of medical-grade stainless steel and contains an internal energy transmission fiber (core diameter 50μm), a composite film, an acoustic lens (focal length 2mm), and a signal sensing fiber; 64 probes are assembled in a ring array (diameter 10cm) and connected to a drive component (accuracy 0.01mm).

[0049] In one embodiment, the device requires performance testing: using an ultra-wideband hydrophone and sound field scanning system, test the probe bandwidth (ensuring ≥40MHz) and resolution (verify lateral ≤100μm and axial ≤80μm through simulated sample imaging); use vernier calipers to confirm that the overall diameter of the full array probe is ≤16cm.

[0050] like Figure 1 and Figure 2 As shown, in an embodiment of the present invention, the control system includes an automated imaging module, which includes: a signal acquisition unit, including a wavelength division multiplexer 21 and a photodetector 22, the output end of the signal sensing optical fiber is connected to the wavelength division multiplexer 21, the wavelength division multiplexer 21 is connected to the photodetector 22, and the photodetector 22 is used to convert optical signals into electrical signals; and a data processing host 23, the output end of the photodetector 22 is connected to the data processing host 23, and is used to receive the processed electrical signals and perform image reconstruction.

[0051] In the above technical solution, after the optical detection ultrasound module 13 in each probe 11 receives the ultrasound signal returned from the tissue, it modulates the ultrasound signal onto the wavelength of the reflected light through the elastic-optical effect and deformation effect of the π phase-shift Bragg grating. These modulated optical signals carrying ultrasound information are transmitted to the wavelength division multiplexer 21 via the signal sensing fiber. The wavelength division multiplexer 21 uses its wavelength division multiplexing function to separate and converge multiple optical signals of different wavelengths from different probe fibers in the ring array, and outputs them uniformly to the photodetector 22. The photodetector 22 converts the received light intensity change signal into a corresponding analog electrical signal, which is then transmitted to the data processing host 23. The data processing host 23 performs preprocessing such as amplification, filtering, and analog-to-digital conversion on the electrical signal, and generates an ultrasound image reflecting the physical morphology of the tissue and a photoacoustic image reflecting the distribution of the tissue's chemical components based on the reconstruction algorithm. In this way, the parallelism of data acquisition and the overall imaging rate can be improved.

[0052] In some embodiments, the data processing host 23 is an ultrasound machine. The drive component, energy laser, and detection laser are all connected to the ultrasound machine control system. The control system is responsible for automating the imaging process and intelligently identifying the cutting edge state. Through program writing, it realizes the synchronous control of the ultrasound machine's hardware signal reception trigger, detection laser laser emission, energy laser pulse emission, and drive component movement. It supports custom settings for scanning step size (adjustable according to resolution requirements) and imaging speed (≥4cm² / min). It can automatically scan tissue samples according to preset parameters, synchronously acquire ultrasound images reflecting the physical morphology of the tissue and photoacoustic images reflecting the chemical composition of the tissue, and complete automated three-dimensional reconstruction to intuitively present the distribution of photoacoustic signals in the tissue.

[0053] In some embodiments, the control system further includes an artificial intelligence analysis module. This module, based on a support vector machine, convolutional neural network, or InceptionResNet model, can automatically distinguish between tumor tissue and normal tissue based on photoacoustic signal intensity and radio frequency signal frequency characteristics. Specifically, the module first constructs a tissue frequency spectrum library: It performs a region-specific fast Fourier transform (FFT) on the acquired two-dimensional radio frequency (RF) signals of the tissue under test, extracting micro-region tissue frequency response characteristics. Combining the acoustic RF spectra within a 0.01 mm³ volume of at least 20 renal tumor resection samples with three-dimensional pathological results, it establishes a correspondence between tumor tissue, adjacent normal tissue, and "photoacoustic signal intensity - RF frequency spectrum characteristics" (i.e., obtained through multiple experiments). Secondly, it trains an artificial intelligence model: using algorithms such as support vector machine (SVM), logistic regression, decision tree, convolutional neural network (CNN), and InceptionResNet, it constructs and optimizes a resection margin discrimination model, defining a positive resection margin as the presence of tumor tissue on the surface of the resected tissue, thus achieving automatic differentiation between tumor and normal tissue.

[0054] It should be noted that the radio frequency signal refers to the current signal carrying the ultrasonic signal output from the output terminal of the photodetector 22.

[0055] In some embodiments, the control system further includes a data processing terminal, which has functions of signal preprocessing (filtering, noise reduction, Fourier transform), multimodal image reconstruction, feature extraction, AI model inference, and diagnostic result storage and output. It can interface with pathological gold standard data to realize real-time verification of test results and iterative optimization of models. The artificial intelligence analysis module is connected to the data processing terminal, which is used for signal preprocessing, image reconstruction and diagnostic result output. The automated imaging module controls the energy laser, detection laser and acquisition card to work according to a predetermined program. After obtaining the radio frequency signal, it is processed by the artificial intelligence analysis module (which performs signal analysis, filtering and other processing) and sent to the data processing terminal to form an image.

[0056] It should be noted that the ultrasound machine, artificial intelligence analysis module, automated imaging module, and data processing terminal can all adopt existing technologies, which will not be elaborated here.

[0057] It should be noted that the steps for setting up the control system are as follows:

[0058] 1. Develop a control program based on LabVIEW to achieve synchronous triggering of the ultrasonic machine, energy laser, and drive components, and support the visualization setting and real-time adjustment of imaging parameters;

[0059] 2. Acoustic RF spectra of 0.01 mm³ volume were collected from 20 renal tumor resection samples (including tumor tissue, adjacent normal tissue, and normal tissue). The tissue type was determined by pathological sections (H&E staining), and a tissue frequency spectrum library containing 10,000+ feature data was constructed.

[0060] 3. Based on the Python / TensorFlow framework, SVM, CNN, and InceptionResNet models were trained respectively. The model parameters were optimized through 5-fold cross-validation and ROC curve analysis to ensure that the sensitivity and specificity of the optimal model were both ≥95%.

[0061] 4. Integrate the imaging module, AI module and data processing terminal to realize the integrated process of "scanning-imaging-analysis-judgment-reporting" and test the system stability (error ≤2% for 10 consecutive scans).

[0062] like Figure 3 As shown, an embodiment of the present invention also provides a method for detecting renal tumor resection margins, which uses the above-mentioned photoacoustic imaging device for renal tumor resection margin detection.

[0063] like Figure 3As shown, in an embodiment of the present invention, the renal tumor resection margin detection method includes: a sample marking step: marking the detection area at the junction of the tumor and normal tissue in the resected renal tumor specimen; a parameter configuration step: setting the laser pulse width of the energy laser 20, the ultrasound detection frequency of the ultrasound machine, and the scanning step size; a multimodal scanning step: performing a three-dimensional scan of the marked area through a ring array of multiple probes 11 and a driving component, simultaneously acquiring ultrasound images and photoacoustic images; an AI discrimination step: performing FFT processing on the radio frequency signal and calling the AI ​​model to discriminate the resection margin status; and a result output step: outputting a detection report and comparing it with the pathological gold standard for verification. This approach enables full automation and intelligent processing from sample labeling to result output, shortening the detection time (≤15 minutes). It solves the problems of long processing time and low efficiency in traditional intraoperative frozen section pathology testing, effectively avoiding missed detections and misjudgments caused by human sampling bias or subjective judgment. At the same time, the three-dimensional scanning mode overcomes the shortcomings of traditional local sampling and detection, accurately identifying hidden micro-lesions at the resection margin and surrounding area, providing clinicians with intuitive and reliable decision-making basis. This allows for the complete removal of tumors while preserving normal kidney tissue to the maximum extent, reducing postoperative recurrence rates and the risk of secondary surgery.

[0064] It should be noted that the scanning step length refers to the physical distance the probe moves between each scanning plane during automated image scanning.

[0065] It should be noted that:

[0066] 1. In the sample labeling step: Select fresh tissue specimens removed by kidney-preserving surgery or radical nephrectomy, and mark four detection areas of approximately 1cm × 1.5cm at the junction of the tumor and normal tissue that is visible to the naked eye with Indian ink.

[0067] 2. In the parameter configuration step: set the core parameters through the imaging control module. The core parameters include the laser pulse width of the energy laser (sub-nanosecond), the ultrasonic detection center frequency (50MHz), the scanning step size (matching resolution requirements), and the imaging rate (≥4cm² / min).

[0068] 3. In the multimodal scanning step: The ultrasound transducer is activated, and the marked area is scanned in three dimensions through multiple probes and drive components. Two types of key images are acquired simultaneously: one is the ultrasound image, which can reflect the physical morphology and structure of the tissue; the other is the photoacoustic image, which is based on molecular-specific absorption. The hemoglobin signal is captured at a near-infrared wavelength of 1000 nm (reflecting the distribution of blood vessels, and the richness of blood vessels in the tumor area differs from that in normal tissue), and the second overtone absorption signal of the lipid CH chemical bond is captured at a wavelength of 1200 nm or 1700 nm (abnormal accumulation of lipid components in tumor tissue, and the signal intensity is significantly different from that in normal tissue).

[0069] 4. In the AI ​​discrimination step: The data processing terminal performs regional FFT processing on the RF signal to extract frequency response features; it calls the trained AI model and combines the photoacoustic signal intensity and frequency features to automatically determine whether there is tumor tissue in the detection area and outputs positive / negative results for the surgical margin.

[0070] 5. In the results output step: the AI ​​judgment results are compared with the gold standard of pathological histology (H&E stained slide diagnosis) to verify accuracy; a detection report containing imaging images, feature analysis, and cutting margin status is generated within 15 minutes to assist doctors in deciding whether further tissue removal is necessary.

[0071] In some embodiments, during the multimodal scanning step, photoacoustic image acquisition includes lipid CH chemical bond overtone absorption signals at wavelengths of 1200 nm and 1700 nm, and hemoglobin signals at a wavelength of 1000 nm. During the AI ​​discrimination step, the resection margin status includes negative and positive margins. A positive margin indicates the presence of tumor tissue on the surface of the excised tissue, with a diagnostic sensitivity and specificity of not less than 95%. A negative margin indicates the absence of tumor tissue on the surface of the excised tissue.

[0072] In some embodiments, during the parameter configuration step, the imaging parameters satisfy the following: lateral resolution not greater than 100 μm, axial resolution not greater than 80 μm, and imaging depth not less than 4 mm; during the result output step, the detection report includes the imaging image, feature analysis, and edge state judgment results.

[0073] It should be noted that the photoacoustic imaging device used for renal tumor margin detection is suitable for kidney-preserving surgery or radical nephrectomy. It can determine in real time whether there is residual tumor at the margin, reducing the risk of secondary surgery after surgery. Furthermore, the photoacoustic imaging device used for renal tumor margin detection adopts all-optical signal transmission, has strong anti-electromagnetic interference capability, and is suitable for the complex electromagnetic environment in the operating room. The ring array of multiple probes can realize overall tissue scanning, avoiding missed detections in local sampling.

[0074] Embodiments of the present invention also provide an artificial intelligence model training method for renal tumor resection margin detection, comprising: collecting the acoustic radio spectrum within a 0.01 mm³ volume of a resected renal tumor sample, constructing a tissue frequency spectrum library in combination with three-dimensional pathological results; extracting the photoacoustic signal intensity and radio frequency signal frequency characteristics output by photodetector 22, training the model using support vector machine, convolutional neural network or InceptionResNet, and optimizing the threshold through ROC curve to ensure that the model sensitivity and specificity are both not less than 95%.

[0075] It should be noted that existing technologies still have the following problems: First, diagnosis is highly subjective and accuracy is limited. Frozen tissue pathology relies on pathologists manually observing tissue sections and subjectively judging the lesion state. The test results are easily affected by human and sample factors such as sampling location deviation, insufficient number of sections, and limited field of view. At the same time, it is limited by the short resolution of traditional pathological imaging, making it difficult to accurately identify small tumor lesions, which easily leads to diagnostic errors and cannot guarantee the accuracy of resection margin detection. Second, the detection coverage is incomplete, which easily leads to missed detections and misjudgments. Traditional pathological testing can only perform random sampling analysis on local sampling areas of the resected tissue, and cannot achieve full-area, full-coverage scanning detection of surgical margins and surrounding kidney tissue. This local sampling mode is very likely to lead to missed detection of occult small tumor residues, causing postoperative tumor recurrence; at the same time, in order to avoid the risk of residues, clinicians often passively expand the resection range, resulting in the unnecessary removal of a large amount of normal kidney tissue, which seriously damages the patient's kidney function. In summary, existing intraoperative renal tumor margin detection technologies suffer from low efficiency, poor accuracy, and incomplete detection, making it difficult to simultaneously meet the surgical requirements of complete tumor removal and maximizing the preservation of normal kidney tissue. Therefore, this application provides a photoacoustic imaging device for renal tumor margin detection. This device employs a multi-probe ring array structure to construct a photoacoustic detection system, enabling rapid acquisition of two-dimensional photoacoustic imaging data of kidney tissue. Simultaneously, it supports large-area, high-precision three-dimensional full-domain scanning, completely overcoming the limitations of traditional single-point, localized detection and achieving comprehensive, efficient, and accurate detection of renal tumor margins. Specifically, compared with existing intraoperative frozen section pathology techniques, the photoacoustic imaging device for renal tumor margin detection in this application has the following significant advantages:

[0076] 1. Significantly shortens detection time: Imaging rate ≥4cm² / min, detection and results output within 15 minutes, which is more than 50% shorter than traditional methods (>30 minutes), reducing surgical anesthesia risks and wound exposure time.

[0077] 2. Ultra-high resolution and specificity: lateral resolution ≤100μm and axial resolution ≤80μm, which can clearly present the differences in tissue microstructure; based on the molecular specific photoacoustic signals of hemoglobin and lipids, combined with AI for accurate discrimination, the sensitivity and specificity are both ≥95%, effectively avoiding missed detection (tumor residue) and misjudgment (over-removal of normal tissue).

[0078] 3. Achieve comprehensive detection: By covering the entire marked area with three-dimensional scanning, the limitations of traditional pathological "local sampling" are overcome, and the risk of missed detection due to sampling deviation is reduced.

[0079] 4. High degree of automation and intelligence: Fully automated scanning, reconstruction, and discrimination reduce human error and ensure diagnostic consistency among different operators and samples;

[0080] 5. Strong clinical adaptability: The probe is small in size (overall diameter ≤16cm) and flexible in operation; it is all-optical transmission and anti-electromagnetic interference, which is compatible with the complex electromagnetic environment of equipment such as electrosurgical units and monitors in the operating room; it does not require complicated pretreatment and can be used directly for fresh specimen testing, meeting the needs of rapid clinical application during operation.

[0081] In summary, the key performance indicators achievable by the device and method of the present invention are shown in Table 1 below:

[0082] Table 1

[0083]

[0084] Specifically, a photoacoustic imaging device used for renal tumor margin detection was employed in a clinical validation trial:

[0085] I. Experimental Sample Design:

[0086] Phase 1 (Boundary Accuracy Verification): 30 fresh specimens from radical nephrectomy were selected, the tumor-normal tissue boundary area was marked, and the consistency between the imaging boundary and the pathological section boundary was compared.

[0087] Phase 2 (Diagnostic Performance Validation): 264 cases of renal tumors with positive resection margins and 36 cases of renal tumors with negative resection margins (all fresh specimens from kidney-preserving surgeries) were selected to validate the sensitivity, specificity, and accuracy of the equipment.

[0088] II. Experimental Procedure and Results:

[0089] All sample testing was completed according to the process of "sample labeling - parameter configuration - scanning imaging - AI discrimination - pathological verification";

[0090] Phase 1 results: The average deviation between the imaging boundary and the pathological boundary was ≤50μm, and the concordance rate was ≥98%;

[0091] Phase 2 results: The average detection time was 12 minutes; the sensitivity was 96.2% (254 / 264), the specificity was 97.2% (35 / 36), and the accuracy was 96.5% (289 / 300), meeting the needs of clinical diagnosis.

[0092] III. Aseptic processing and safety verification:

[0093] The probe housing is sterilized with ethylene oxide and should be covered with a disposable sterile protective film during use to avoid cross-infection.

[0094] The laser power is controlled below 0.1W / cm², and in vitro tissue experiments have verified that there is no risk of thermal damage, meeting medical safety standards.

[0095] As can be seen from the above description, the embodiments of the present invention achieve the following technical effects: By using an ultrasonic transducer constructed with multiple probes arranged in a ring array, multi-point synchronous parallel detection can be achieved, significantly reducing the intraoperative detection time and solving the problem of long traditional pathological detection time. This effectively shortens the operation time and avoids the increased risk of anesthesia exposure, wound infection, and intraoperative complications caused by the excessively long operation time of traditional frozen pathological detection. At the same time, the present invention abandons the traditional piezoelectric ultrasonic array with inherent device defects and uses a π phase-shift Bragg grating as the optical detection ultrasonic module. It can receive ultrasonic signals with a center frequency of 50MHz and a bandwidth of 120%. This type of device is based on a unified optical fiber substrate, and the detection sensitivity and imaging resolution of all sub-gratings in the array are highly consistent, eliminating the parameter difference problem of traditional array devices. From the hardware level, it ensures the imaging uniformity and high-precision imaging performance of the entire imaging system. In this way, the problems of system imaging distortion and resolution reduction can be solved to meet the high-precision detection requirements of small tumor margins.

[0096] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A photoacoustic imaging device for detecting renal tumor resection margins, characterized in that, include: Energy laser (20) for emitting pulsed laser; An ultrasonic transducer (10) includes multiple probes (11) arranged in a ring array. Each probe (11) has an ultrasonic transmitting port and an ultrasonic receiving port. Each probe (11) contains an optically excited ultrasonic module (12) and an optically detected ultrasonic module (13). The optically excited ultrasonic module (12) receives pulsed laser light from the energy laser (20) and converts it into pulsed ultrasonic light. The pulsed ultrasonic light is emitted through the ultrasonic transmitting port. The optically detected ultrasonic module (13) uses a π-phase-shift Bragg grating to convert the ultrasonic signal entering through the ultrasonic receiving port into a laser signal. The Q value of the π-phase-shift Bragg grating does not exceed 10. 5 It can receive ultrasonic signals with a center frequency of 50MHz and a bandwidth of 120%. A control system is configured to receive the laser signal, analyze the laser signal, and image it.

2. The photoacoustic imaging device for detecting renal tumor resection margins according to claim 1, characterized in that, The photoacoustic imaging device for detecting renal tumor margins also includes a detection laser. The optical detection ultrasound module (13) includes a signal sensing fiber. The signal sensing fiber passes through multiple probes (11) sequentially along the axial direction. Multiple π-phase-shift Bragg gratings are etched axially inside the signal sensing fiber so that each probe (11) contains one of the π-phase-shift Bragg gratings. One end of the signal sensing fiber is connected to the detection laser, and the other end of the signal sensing fiber is connected to the control system.

3. The photoacoustic imaging device for detecting renal tumor resection margins according to claim 2, characterized in that, The detection laser is a tunable narrowband laser, which is used to lock the wavelength of the pulsed laser in the -3dB linear slope region of the reflection spectrum of the π phase-shifted Bragg grating.

4. The photoacoustic imaging device for detecting renal tumor resection margins according to claim 1, characterized in that, The optically excited ultrasound module (12) includes a composite film made of multi-walled carbon nanotubes and polymethyl methacrylate to convert picosecond pulsed laser into ultra-high frequency and wide bandwidth pulsed ultrasound.

5. The photoacoustic imaging device for detecting renal tumor resection margins according to claim 4, characterized in that, The thickness of the composite film does not exceed 25 μm.

6. The photoacoustic imaging device for detecting renal tumor resection margins according to claim 4, characterized in that, The multi-walled carbon nanotubes account for 1%-10% of the total mass of the composite film.

7. The photoacoustic imaging device for detecting renal tumor resection margins according to any one of claims 1 to 6, characterized in that, The probe (11) is provided with an energy transmission fiber (15), the emitting end of the energy transmission fiber (15) is set towards the ultrasonic emission port, the optical excitation ultrasonic module (12) is set between the ultrasonic emission port and the emitting end of the energy transmission fiber (15), and the energy transmission fibers (15) of multiple probes (11) are connected to the energy laser (20) through an optical fiber bundle (16).

8. The photoacoustic imaging device for detecting renal tumor resection margins according to any one of claims 1 to 6, characterized in that, The photoacoustic imaging device for detecting renal tumor margins also includes a drive component connected to the control system. The output end of the drive component is driven and connected to the ultrasonic transducer (10). The drive component is used to drive the ultrasonic transducer (10) to perform linear displacement relative to the tissue (100) to be tested.

9. The photoacoustic imaging device for detecting renal tumor resection margins according to claim 2, characterized in that, The control system includes an automated imaging module, which includes: The signal acquisition unit includes a wavelength division multiplexer (21) and a photodetector (22). The output end of the signal sensing fiber is connected to the wavelength division multiplexer (21). The wavelength division multiplexer (21) is connected to the photodetector (22). The photodetector (22) is used to convert optical signals into electrical signals. The data processing host (23) is connected to the output terminal of the photodetector (22) and is used to receive the processed electrical signal and perform image reconstruction.

10. A method for detecting renal tumor resection margins, characterized in that, The renal tumor resection margin is detected using the photoacoustic imaging device for renal tumor margin detection as described in any one of claims 1 to 9.

11. The method for detecting renal tumor resection margins according to claim 10, characterized in that, The method for detecting renal tumor margins includes: Sample labeling steps: Mark the detection area at the junction of tumor and normal tissue in the resected kidney tumor specimen; Parameter configuration steps: Set the laser pulse width of the energy laser (20), the ultrasonic detection frequency of the ultrasonic machine, and the scanning step size; Multimodal scanning steps: The marked area is scanned in three dimensions by a ring array of multiple probes (11) and a driving component, and ultrasound and photoacoustic images are acquired simultaneously; AI discrimination steps: Perform FFT processing on the radio frequency signal and call the AI ​​model to determine the cutting edge state; Results output steps: Output the test report and compare it with the pathological gold standard for verification.

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