Photoacoustic temperature imaging method based on coherence factor weighted fusion

Abstract

The invention discloses a photoacoustic temperature imaging method based on coherent factor weighted fusion. The method comprises the following steps: step 1, hardware composition and configuration of a multi-bandwidth photoacoustic imaging system based on photoacoustic and ultrasonic dual modes; step 2, ultrasonic modal signal preprocessing; and step 3, calculating a coherence factor and weighting a reconstructed image. According to the invention, high-precision non-intrusive temperature measurement capability is shown, and a more advanced non-intrusive temperature monitoring means is provided for photothermal therapy of tumors. According to the method, functional modules of imaging, segmentation, temperature measurement, treatment and the like are integrated, the reconstructed image is weighted through the coherence factor, high artifacts and multiple side lobes in the FBP reconstructed image are removed, the resolution of temperature imaging is remarkably improved, and the method has remarkable advantages and huge application potential in the application of photo-thermal treatment. And a multi-modal image fusion method is assisted, so that the multi-modal information of the target region can be obtained in the treatment process, more intuitive visual tumor state feedback is provided for workers, and the treatment effect is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photoacoustic / ultrasonic imaging, and relates to a photoacoustic temperature imaging method, and in particular to a photoacoustic temperature imaging method based on coherence factor weighted fusion. Background Art

[0002] As an emerging treatment method with the advantages of non-invasiveness and high selectivity, photothermal therapy has received widespread attention in the field of tumor treatment. However, due to the lack of effective non-contact temperature measurement technology, it becomes very difficult to accurately measure the temperature and distribution of the target area in real time, which may cause damage to the target area and its surrounding healthy tissues during treatment. Fortunately, the rapid development of photoacoustic image reconstruction technology, especially in the field of image reconstruction and signal processing, provides a new solution to this problem. Summary of the invention

[0003] In order to overcome the problem of lack of high-resolution non-invasive temperature imaging in existing photothermal therapy, the present invention provides a photoacoustic temperature imaging method based on coherence factor weighted fusion. This method significantly improves the temperature imaging resolution during photothermal therapy by introducing coherence factor weighting in the image reconstruction process, improves the temperature measurement accuracy to a certain extent, and reduces the complexity of the system hardware, which is suitable for clinical use in tumor photothermal therapy.

[0004] The objective of the present invention is achieved through the following technical solutions:

[0005] A photoacoustic temperature imaging method based on coherence factor weighted fusion comprises the following steps:

[0006] Step 1: Hardware composition and configuration of multi-bandwidth photoacoustic imaging system based on photoacoustic and ultrasonic dual modalities:

[0007] The multi-bandwidth photoacoustic imaging system based on photoacoustic and ultrasonic dual modes is composed of a pulse laser, a ring array ultrasonic transducer, a multi-channel data acquisition card, a central computer and a main control board, wherein: the pulse laser is used to emit pulsed light with a nanosecond pulse width to excite the light absorber in the target area and generate a photoacoustic signal; the ring array ultrasonic transducer is used to obtain ultrasonic and photoacoustic dual-mode signals (expressed as sound pressure signals); the multi-channel data acquisition card is used to perform digital-to-analog conversion on the ultrasonic and photoacoustic dual-mode signals received by the ring array transducer, and transmit the processed data to the central computer for further analysis and processing; the main control board is used to match the working sequence of each system component after receiving the command from the central computer to ensure the normal operation of the system;

[0008] Step 2: Ultrasonic modal signal preprocessing:

[0009] Step 2.1: The pulse laser emits short pulse light, which is irradiated on the tissue in the target imaging area to stimulate the photoacoustic signal. The intensity of the generated photoacoustic signal is related to the temperature of the target area, which is specifically represented by the following formula:

[0010] P(x,y;t)=A1+A2T(x,y;t)

[0011] Wherein, P(x, y; t) represents the sound pressure value at the position (x, y) at time t, T(x, y; t) refers to the instantaneous temperature at the position (x, y) at time t, (x, y) is the Cartesian coordinate at the imaging position, the origin of the coordinate is located at the center of the ring array, and A1 and A2 are linear coefficients;

[0012] Step 2.2, after receiving the imaging instruction from the central computer, the main control board triggers the pulse laser to start sending light pulses, and simultaneously starts the ring array transducer to start receiving signals. A single ring array transducer receives the photoacoustic signal and ultrasound signal of the tumor target area respectively. These signals are collected by a multi-channel data acquisition card and stored in the central computer;

[0013] Step 2.3, pre-processing the signals collected by the multi-channel data acquisition card in the central computer to remove noise from the received signals and enhance the effective parts of the signals;

[0014] Step 2.4, the preprocessed signal uses a reconstruction algorithm to generate preprocessed images of multiple ultrasound modes and obtain the target area coordinates;

[0015] Step 3: Calculation of coherence factor and weighting of reconstructed image:

[0016] Step 3.1: Allocation of basic parameters and division of target area grid:

[0017] Allocation of basic parameters: specify the initial sound velocity, delay time of the array ultrasonic transducer, radius of the array, grid size of the target area grid and sampling frequency of the multi-channel data acquisition card in the program;

[0018] Target area grid division: Use the target area coordinates obtained in step 2.4 to divide the grid for photoacoustic image reconstruction. Take the center of the ring array as the coordinate origin, and the corresponding coordinate is (0,0). Assign relative coordinates in standard units to each grid point. By specifying the radius of the ring array ultrasonic transducer, use the parametric equation of the circle Calculate the relative coordinates of each circular array ultrasonic transducer and the origin;

[0019] Step 3.2: Photoacoustic temperature image reconstruction based on coherence factor weighted fusion:

[0020] Step 3.2.1: Reconstruct the sound pressure signal using the sound pressure signal received by the ring array transducer. The reconstruction formula is as follows:

[0021]

[0022] Where P0(x, y; t) represents the sound pressure information reconstructed at the position (x, y) at time t, Δs i (t+δt i ) indicates that the i-th transducer receives the signal at time t+δt i The differential signal at r S is the sound source position vector, expressed in grid coordinates (x, y), r d is the position vector of the circular array ultrasonic transducer, which is calculated in step 3.1, cosα refers to the cosine value of the angle between the line connecting the ith circular array transducer and the center of the circle and the line connecting the target grid point (x, y) and the transducer, and N is the total number of transducers; is the result of normalization of the coherent weighting factor CF(x,y;t), that is:

[0023]

[0024] Wherein, max(CF(x,y;t)) represents the maximum value of CF(x,y;t), and the coherence weighting factor CF(x,y;t) is calculated by the following formula:

[0025]

[0026] Among them, CF(x,y;t) is the value of the coherent weighting factor at time t, s i (t+Δt i ) is the signal received by the array ultrasonic transducer at t+Δt i The value obtained at the moment;

[0027] Step 3.2.2: Substitute the linear relationship between sound pressure and temperature in step 2.1 into the sound pressure signal reconstruction formula in step 3.2.1 to obtain the photoacoustic temperature image reconstruction formula:

[0028]

[0029] Step 3.3, fuse the coherence factor weighted temperature reconstructed image obtained in step 3.2.2 with the preprocessed image of the ultrasound mode obtained in step 2.4 to obtain fused image information.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] 1. Traditional reconstructed images have strong artifacts, many side lobes and low resolution. The present invention introduces a coherent weighting factor to provide weighted calculation for the reconstructed image, which effectively suppresses the high artifacts and multiple side lobes existing in the traditional image reconstruction algorithm.

[0032] 2. The present invention proposes a new method for photoacoustic temperature image reconstruction, which significantly improves the resolution of non-invasive temperature imaging based on the photoacoustic effect and improves the temperature measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Schematic diagram of the temperature image reconstruction structure based on the coherence factor;

[0034] Figure 2 This is a schematic diagram of the system working sequence;

[0035] Figure 3 This is a comparison chart between CFWTM temperature imaging and FBP temperature imaging. DETAILED DESCRIPTION

[0036] The technical solution of the present invention is further described below in conjunction with the accompanying drawings, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0037] The present invention provides a photoacoustic temperature imaging method based on coherence factor weighted fusion, such as Figure 1 As shown, the method comprises the following steps:

[0038] Step 1: Hardware composition and configuration of multi-bandwidth photoacoustic imaging system based on photoacoustic and ultrasonic dual modalities:

[0039] The present invention provides a multi-bandwidth photoacoustic imaging system based on photoacoustic and ultrasonic dual modes, which is intended to provide the required basic information for subsequent temperature reconstruction and multi-modal image fusion. The multi-bandwidth photoacoustic imaging system based on photoacoustic and ultrasonic dual modes is composed of the following main components: a pulse laser, a ring array ultrasonic transducer, a multi-channel data acquisition card, a central computer and a main control board, wherein: the pulse laser is used to emit pulsed light with a nanosecond pulse width to excite the light absorber in the target area and generate a photoacoustic signal; the ring array ultrasonic transducer is used to obtain ultrasonic and photoacoustic dual-modal signals; the multi-channel data acquisition card is used to digitally process the ultrasonic and photoacoustic dual-modal signals received by the ring array transducer, and transmit the processed data to the central computer for further analysis and processing; the main control board is used to match the working sequence of each system component after receiving the command of the central computer to ensure the normal operation of the system. During the operation of the system, by issuing instructions in the central computer to control the operation of the pulse laser, the main control board automatically matches the working sequence of the ring array ultrasonic transducer and the multi-channel data acquisition device under different working modes to complete temperature imaging and photothermal therapy. Ultrasonic signals and photoacoustic signals are essentially sound pressure signals. The ring array transducer can obtain information from both ultrasound and photoacoustic modes. Through the preset timing design, this system can achieve real-time ultrasound / photoacoustic dual-modality image reconstruction of the target area, thereby providing rich information on tumor structure and function.

[0040] Step 2: Ultrasonic modal signal preprocessing:

[0041] During the experimental operation, the pulsed laser emits short pulses of light, which are irradiated on the tissue in the target imaging area to stimulate photoacoustic signals. The intensity of the generated photoacoustic signal is related to the temperature of the target area, which can be specifically characterized by the following formula:

[0042] P(x,y;t)=A1+A2T(x,y;t)

[0043] Among them, P(x,y; t) represents the sound pressure value at the position (x,y) at time t, T(x,y; t) refers to the instantaneous temperature at the position (x,y) at time t, (x,y) is the Cartesian coordinate at the imaging position, the origin of the coordinate is located at the center of the ring array, A1 and A2 are linear coefficients, which can be calibrated by experiments. Therefore, by calculating P(t) of the target area, the temperature of the target position can be obtained.

[0044] like Figure 2As shown, after the main control board receives the imaging instruction from the central computer, the trigger pulse laser starts to send light pulses, and the ring array transducer starts to receive signals. Through the predetermined timing design, a single ring array transducer receives the photoacoustic signal and ultrasound signal of the tumor target area respectively. These signals are collected by the multi-channel data acquisition card and stored in the central computer. In order to ensure the accuracy and efficiency of subsequent image reconstruction, the signals collected by the multi-channel data acquisition card are preliminarily preprocessed in the central computer to remove the noise in the received signal and enhance the effective part of the signal, so as to ensure the accuracy and efficiency of subsequent dual-modality image reconstruction. The preprocessed signal uses the traditional reconstruction algorithm to generate an ultrasound modality image, select the target area position, and obtain the target area coordinates. The target area is set as a rectangle, and its coordinates are represented by four numbers, which are represented by the horizontal coordinate of the upper left corner of the rectangle, the vertical coordinate of the upper left corner of the rectangle, the length of the rectangle in the x direction, and the length of the rectangle in the y direction.

[0045] Step 3: Calculation of coherence factor and weighting of reconstructed image:

[0046] Step 3.1: Allocation of basic parameters and division of target area grid:

[0047] Allocation of basic parameters: specify the initial sound velocity, delay time of the array ultrasonic transducer, radius of the array, grid size of the target area grid and sampling frequency of the multi-channel data acquisition card in the program.

[0048] Target area grid division: In order to achieve high-definition temperature image reconstruction, the target area coordinates obtained in step 2 are used to divide the grid for photoacoustic image reconstruction. The center of the ring array is used as the coordinate origin, and the corresponding coordinate is (0,0). Each grid point is assigned a relative coordinate in standard units (m). By specifying the radius of the ring array ultrasonic transducer, the parametric equation of the circle is used. The relative coordinates of each circular array ultrasonic transducer and the origin are calculated.

[0049] At this point, the allocation of basic parameters and the division of the target area grid are completed.

[0050] Step 3.2: Photoacoustic temperature image reconstruction based on coherence factor weighted fusion:

[0051] Step 3.2.1: Reconstruct the sound pressure signal using the sound pressure signal received by the ring array transducer. The reconstruction formula is as follows:

[0052]

[0053] Where P0(x, y; t) represents the sound pressure information reconstructed at the position (x, y) at time t, Δs i (t+δt i ) indicates that the i-th transducer receives the signal at time t+δt iThe differential signal at r S is the sound source position vector, expressed in grid coordinates (x, y), r d is the position vector of the circular array ultrasonic transducer, which is calculated in step 3.1, cosα refers to the cosine value of the angle between the line connecting the ith circular array transducer and the center of the circle and the line connecting the target grid point (x, y) and the transducer, and N is the total number of transducers; is the result of normalization of the coherence weighting factor CF(x,y;t), which is calculated by the following formula:

[0054]

[0055] Among them, CF(x,y;t) is the value of the coherent weighting factor at time t, s i (t+Δt i ) is the signal received by the array ultrasonic transducer at t+Δt i The principle of the coherence factor is derived from the coherence between the signals received by different array ultrasonic transducers. The numerator of CF(x,y;t) will contain s after being squared. i and j This reflects the calculation relationship of coherence. If the target point of reconstruction is the real sound source point, then the coherence here is good and CF(x,y;t) is large. If the target point of reconstruction is not the real sound source point, such as the point in the artifact area of ​​the traditional reconstructed image, CF(x,y;t) will be significantly reduced. In the implementation process, because the CF operator value is less than 1, in order to avoid the undesirable small value when calculating the sound pressure value P(t) in the weighted image, CF(x,y;t) needs to be normalized to obtain It is the result of normalizing the coherence weighting factor CF(x,y;t), namely:

[0056]

[0057] In the formula, max(CF(x,y;t)) represents the maximum value of CF(x,y;t).

[0058] Step 3.2.2: Substitute the linear relationship between sound pressure and temperature in step 2.1, P(x, y; t) = A1 + A2T(x, y; t), into the reconstruction formula of the sound pressure signal in step 3.2.1, and obtain the photoacoustic temperature image reconstruction formula:

[0059]

[0060] The above formula is the coherent factor weighing temperature imaging algorithm CFWTM (coherent factor weighing temperature imaging). The temperature reconstructed image obtained by the above calculation effectively reduces the side lobe and noise level, and ultimately improves the SNR and the spatial resolution of the reconstructed image.

[0061] Step 3.3, fuse the coherence factor weighted temperature reconstructed image obtained in step 3.2.2 with the preprocessed image of the ultrasound mode obtained in step 2.4 to obtain fused image information.

[0062] Step 4: System verification and application:

[0063] In order to verify the effectiveness of the present invention, relevant experiments were conducted to verify its reliability and applicability in practical applications. The experimental results show that CFWTM performs well in multi-bandwidth photoacoustic imaging tasks. In in vivo experiments, the system was successfully applied to non-invasive temperature measurement of mouse tumor target areas with local injection of photothermal nanoprobes. After using CFWTM, the system performed well in local temperature measurement resolution, temperature measurement accuracy, temperature correlation and other indicators, verifying its reliability and wide applicability in practical applications.

[0064] Figure 3 The comparison between the finger photoacoustic reconstruction image reconstructed by CFWTM and the photoacoustic temperature image reconstructed by traditional FBP is shown. Figure 3 It can be clearly seen that the photoacoustic temperature imaging based on the CFWTM algorithm can image smaller targets. Compared with the images reconstructed by traditional FBP, the present invention can more effectively remove artifacts and improve imaging resolution.

[0065] The present invention demonstrates high-precision non-invasive temperature measurement capabilities, providing a more advanced non-invasive temperature monitoring method for photothermal therapy of tumors. The present invention integrates imaging, segmentation, temperature measurement, treatment and other functional modules, weights the reconstructed image by coherence factors, removes high artifacts and multiple side lobes in the FBP reconstructed image, and significantly improves the resolution of temperature imaging. It shows significant advantages and huge application potential in the application of photothermal therapy. Assisted by multimodal image fusion methods, it ensures that multimodal information of the target area can be obtained during the treatment process, providing staff with more intuitive visual feedback on the tumor status and improving the treatment effect.

Claims

1. A photoacoustic temperature imaging method based on coherence factor weighted fusion, characterized in that The method comprises the following steps: Step 1: Hardware composition and configuration of multi-bandwidth photoacoustic imaging system based on photoacoustic and ultrasonic dual modalities: The multi-bandwidth photoacoustic imaging system based on photoacoustic and ultrasonic dual modes is composed of a pulse laser, a ring array ultrasonic transducer, a multi-channel data acquisition card, a central computer and a main control board, wherein: the pulse laser is used to emit pulsed light with a pulse width of nanoseconds to excite the light absorber in the target area and generate a photoacoustic signal; the ring array ultrasonic transducer is used to obtain ultrasonic and photoacoustic dual-mode signals; the multi-channel data acquisition card is used to perform digital-to-analog conversion on the ultrasonic and photoacoustic dual-mode signals received by the ring array transducer, and transmit the processed data to the central computer for further analysis and processing; the main control board is used to match the working sequence of each system component after receiving the command from the central computer to ensure the normal operation of the system; Step 2: Ultrasonic modal signal preprocessing: Step 2.1: The pulse laser emits short pulse light, which is irradiated on the tissue in the target imaging area to stimulate the photoacoustic signal. The intensity of the generated photoacoustic signal is related to the temperature of the target area, which is specifically represented by the following formula: P(x,y;t)=A1+A2T(x,y;t) Wherein, P(x, y; t) represents the sound pressure value at the position (x, y) at time t, T(x, y; t) refers to the instantaneous temperature at the position (x, y) at time t, (x, y) is the Cartesian coordinate at the imaging position, the origin of the coordinate is located at the center of the ring array, and A1 and A2 are linear coefficients; Step 2.2, after receiving the imaging instruction from the central computer, the main control board triggers the pulse laser to start sending light pulses, and simultaneously starts the ring array transducer to start receiving signals. A single ring array transducer receives the photoacoustic signal and ultrasound signal of the tumor target area respectively. These signals are collected by a multi-channel data acquisition card and stored in the central computer; Step 2.3, pre-processing the signals collected by the multi-channel data acquisition card in the central computer to remove noise from the received signals and enhance the effective parts of the signals; Step 2.4, the preprocessed signal uses a reconstruction algorithm to generate preprocessed images of multiple ultrasound modes and obtain the target area coordinates; Step 3: Calculation of coherence factor and weighting of reconstructed image: Step 3.1: Allocation of basic parameters and division of target area grid: Allocation of basic parameters: specify the initial sound velocity, delay time of the array ultrasonic transducer, radius of the array, grid size of the target area grid and sampling frequency of the multi-channel data acquisition card in the program; Target area grid division: Use the target area coordinates obtained in step 2.4 to divide the grid for photoacoustic image reconstruction. Take the center of the ring array as the coordinate origin, and the corresponding coordinate is (0,0). Assign relative coordinates in standard units to each grid point. By specifying the radius of the ring array ultrasonic transducer, use the parametric equation of the circle Calculate the relative coordinates of each circular array ultrasonic transducer and the origin; Step 3.2: Photoacoustic temperature image reconstruction based on coherence factor weighted fusion: Step 3.2.1: Reconstruct the sound pressure signal using the sound pressure signal received by the ring array transducer. The reconstruction formula is as follows: Where P0(x, y; t) represents the sound pressure information reconstructed at the position (x, y) at time t, Δs i (t+δt i ) indicates that the i-th transducer receives the signal at time t+δt i The differential signal at , cosα refers to the cosine value of the angle between the line connecting the i-th ring array transducer and the center of the circle and the line connecting the target grid point (x, y) and the transducer, and N is the total number of transducers; is the normalized result of the coherence weighting factor CF(x,y;t); Step 3.2.2: Substitute the linear relationship between sound pressure and temperature in step 2.1 into the reconstruction formula of the sound pressure signal in step 3.2.1 to obtain the photoacoustic temperature image reconstruction formula: Step 3.3, fuse the coherence factor weighted temperature reconstructed image obtained in step 3.2.2 with the preprocessed image of the ultrasound mode obtained in step 2.4 to obtain fused image information.

2. The photoacoustic temperature imaging method based on coherence factor weighted fusion according to claim 1 is characterized in that The target area is set as a rectangle, and its coordinates are represented by four numbers, which are respectively represented as the horizontal coordinate of the upper left corner of the rectangle, the vertical coordinate of the upper left corner of the rectangle, the length of the rectangle in the x direction, and the length of the rectangle in the y direction.

3. The photoacoustic temperature imaging method based on coherence factor weighted fusion according to claim 1 is characterized in that Said r S is the sound source position vector, expressed in grid coordinates (x, y), r d is the position vector of the circular array ultrasonic transducer.

4. The photoacoustic temperature imaging method based on coherence factor weighted fusion according to claim 1 is characterized in that Said Calculated by the following formula: In which, max(CF(x,y;t)) represents the maximum value of CF(x,y;t).

5. The photoacoustic temperature imaging method based on coherence factor weighted fusion according to claim 1 or 4, characterized in that The coherence weighting factor CF(x, y; t) is calculated by the following formula: Among them, CF(x,y;t) is the value of the coherent weighting factor at time t, s i (t+Δt i ) is the signal received by the array ultrasonic transducer at t+Δt i The value obtained at the moment.

Images