Helical line multi-point expansion sampling photoacoustic volumetric imaging method and system

Abstract

The application discloses a helical line multi-point amplification sampling photoacoustic volume imaging method and system, and belongs to the technical field of photoacoustic volume imaging. In step S1, signal amplification training data set is acquired; the imaging area of a biological tissue or a phantom sample is adjusted to be located in a set sound field range of a three-dimensional bowl-shaped ultrasonic array, three-dimensional photoacoustic volume imaging is performed on the biological tissue or the phantom sample to obtain non-rotation scanning sparse data, a pre-amplification photoacoustic chord diagram and a post-amplification photoacoustic chord diagram; in step S2, an interpolation network is constructed and trained; a U-Net interpolation network is constructed, the pre-amplification photoacoustic chord diagram is taken as input, the post-amplification photoacoustic chord diagram is taken as a real label for training, error analysis training is performed, and an optimal interpolation model with minimum error is saved. The application replaces traditional multi-rotation angle physical rotation scanning by using deep learning virtual amplification sampling technology, significantly shortens imaging time, realizes high-speed three-dimensional imaging under single-pulse excitation, and is suitable for real-time dynamic monitoring scenes.

Description

Technical Field

[0001] This invention relates to the field of photoacoustic volumetric imaging technology, specifically to a spiral multi-point amplification sampling photoacoustic volumetric imaging method and system. Background Technology

[0002] Photoacoustic tomography (PACT) is a hybrid imaging technique that combines the high contrast of optical imaging with the deep penetration of ultrasound imaging, and it has broad application prospects in the biomedical field. This technology detects the ultrasound signals generated by biological tissue under pulsed laser irradiation, enabling high-resolution imaging of tissue structure and function. Due to its comprehensive advantages in imaging resolution, penetration depth, optical contrast, and non-destructive in vivo detection, PACT has experienced rapid development in recent years.

[0003] Spherical arrays are one of the effective methods for achieving single-excitation three-dimensional photoacoustic volumetric imaging. Traditional PACT systems often use large hemispherical ultrasonic transducer arrays (such as 1024-element channels) to obtain high-quality images. However, as the number of element channels increases, the system not only sees a significant increase in manufacturing costs, but also a sharp increase in the difficulty of synchronous acquisition and image reconstruction of signals from multiple element channels, leading to a decrease in temporal resolution. Therefore, how to maintain sufficient sampling density while reducing the actual number of transducers to suppress artifacts in a limited field of view, and at the same time improve image quality and temporal resolution, has become a key problem that urgently needs to be solved in current photoacoustic imaging technology.

[0004] Based on this, the present invention designs a spiral multi-point amplification sampling photoacoustic volumetric imaging method and system to solve the above problems. Summary of the Invention

[0005] To address the aforementioned shortcomings of existing technologies, this invention provides a spiral multi-point amplification sampling photoacoustic volumetric imaging method and system.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A spiral multi-point amplification sampling photoacoustic volumetric imaging method includes the following steps:

[0008] Step S1, obtain signal amplification training dataset: adjust the imaging area of ​​biological tissue or phantom sample to be within the set sound field range of the three-dimensional bowl-shaped ultrasonic array, and perform three-dimensional photoacoustic volume imaging on biological tissue or phantom sample to obtain non-rotation scanning sparse data, photoacoustic string map before amplification and photoacoustic string map after amplification.

[0009] Step S2, construct and train the interpolation network: construct a U-Net interpolation network, take the photoacoustic string map before amplification as input, and the photoacoustic string map after amplification as the real label for training, perform error analysis training, and save the optimal interpolation model with the smallest error;

[0010] Step S3, Fast sparse sampling without rotation: The sparse data from the non-rotational scan is recombined to obtain the photoacoustic chord map of the non-rotational scan sparse sampling.

[0011] Step S4: The optimal interpolation model performs virtual rotation interpolation: The photoacoustic string map with sparse sampling without rotation scanning is input into the optimal interpolation model, and the photoacoustic string map after virtual rotation amplification is obtained through virtual rotation interpolation of the optimal interpolation model; the array element channel data in the photoacoustic string map after virtual rotation amplification is extracted according to the array element channel number defined by the pre-generated interpolation sequence, and photoacoustic data of virtual rotation angle is generated.

[0012] Step S5, 3D reconstruction: The photoacoustic chord diagram of sparse sampling without rotation and the photoacoustic data corresponding to multiple virtual rotation angles are recombined into a photoacoustic data matrix of coordinate data according to their three-dimensional spatial coordinates. A high-resolution three-dimensional photoacoustic image equivalent to the real dense sampling is obtained through the three-dimensional reconstruction algorithm.

[0013] Furthermore, the specific operation of step S1 is as follows:

[0014] S11: Align the central axis of the three-dimensional bowl-shaped ultrasonic array arranged in the Fibonacci Fermat double helix with the center of the imaging area of ​​the biological tissue or phantom sample, and adjust the relative distance between the three-dimensional bowl-shaped ultrasonic array and the biological tissue or phantom sample so that the imaging area is within the set sound field range of the three-dimensional bowl-shaped ultrasonic array.

[0015] S12: Perform three-dimensional photoacoustic volume imaging on biological tissues or phantom samples to obtain a sparse photoacoustic signal data matrix and the three-dimensional spatial coordinates of each ultrasonic array element, as sparse data for non-rotation scanning.

[0016] S13: Perform multi-angle three-dimensional photoacoustic volumetric imaging of the same biological tissue or phantom sample by axial rotation, and obtain the photoacoustic signal data matrix corresponding to each rotation angle and the three-dimensional spatial coordinates corresponding to each ultrasonic array element, as interpolation data of the rotation angle.

[0017] S14: Based on the rotation angle and array element channel number corresponding to the pre-generated interpolation sequence, the sparse data of the non-rotation scanning is recombined to obtain the photoacoustic string map before amplification; the photoacoustic data of the corresponding array element channel is extracted from the interpolation data of the rotation angle, and the photoacoustic string map before amplification is interpolated and amplified to obtain the photoacoustic string map after amplification.

[0018] Furthermore, in the Fibonacci Fermat double helix arrangement, the three-dimensional spatial coordinates corresponding to each ultrasonic array element... The calculation is as follows:

[0019]

[0020] In the formula, The radius of the sphere of the three-dimensional bowl-shaped ultrasound array is given. The total number of array element channels. Number the array element channels , To cover the rotation angle of the array, The golden ratio constant has a value of , For the first Individual Planar projection radius.

[0021] Furthermore, the arrangement of the pre-generated interpolation sequence is determined by the position of the array element channel corresponding to the rotation angle;

[0022] Specifically, this includes: selecting any element channel within the spiral interval, rotating it around the depth axis, obtaining the intersection point where the rotation trajectory intersects the spiral line, calculating the rotation angle, and determining the staggered arrangement of the element channels on the spiral line before and after rotation under the rotation angle, which is the arrangement of the pre-generated interpolation sequence.

[0023] Furthermore, the U-Net interpolation network employs an asymmetric convolutional kernel structure, and performs downsampling and upsampling in the time dimension.

[0024] Furthermore, the specific operation of virtual rotation interpolation is as follows: a virtual rotated array element channel is inserted between the non-rotated array element channels, and its position is consistent with the position of the real rotated array element channel.

[0025] An imaging system utilizing a spiral multi-point amplification sampling photoacoustic volumetric imaging method includes a pulsed laser emission module, a rotation control module, a photoacoustic volumetric imaging probe, and a computer data processing module.

[0026] The pulsed laser emitting module includes a pulsed laser and a diffuser, with the output end of the pulsed laser connected to the diffuser.

[0027] The rotation control module includes a logic control circuit, a voltage regulator, a comparator, and a rotating motor;

[0028] The photoacoustic volumetric imaging probe includes a probe housing, a three-dimensional bowl-shaped ultrasonic transducer, a rotary connector, a bearing slip ring, an optical fiber bundle, and a diffusion lens;

[0029] A three-dimensional bowl-shaped ultrasonic transducer is installed in the bowl-shaped cavity of the probe housing, with the three-dimensional bowl-shaped ultrasonic array arranged in the Fibonacci-Fermat double helix.

[0030] The probe housing has a one-inch internally threaded post channel in the center, and the fiber optic bundle and the diffusion lens are pressed and fixed inside the channel by an externally threaded iron ring.

[0031] The diffusion lens is located at the front end of the fiber bundle;

[0032] The probe housing is connected to the rotary motor via a rotary connector, and the logic control circuit is electrically connected to the rotary motor. The logic control circuit outputs a drive signal to control the rotary motor to rotate axially according to the rotation angle.

[0033] The computer data processing module includes a computer, a multi-element channel data acquisition unit, which is electrically connected to a three-dimensional bowl-shaped ultrasonic transducer. The multi-element channel data acquisition unit, voltage regulator, and three-dimensional bowl-shaped ultrasonic transducer are all connected to the computer. The logic control circuit is connected to a comparator, which is connected to a pulsed laser. The voltage regulator is connected to the logic control circuit and a rotating motor.

[0034] Furthermore, the three-dimensional bowl-shaped ultrasonic transducer has a center frequency of 5MHz.

[0035] Furthermore, the fiber bundle core diameter is 200μm.

[0036] Furthermore, the rotation control module also includes a photoelectric encoder, which is coaxially mounted with the rotary motor to provide real-time feedback of the actual rotation angle and to control the step error within ±0.05° through PID closed-loop control.

[0037] The three-dimensional bowl-shaped ultrasonic transducer is connected to a rotary motor via a rotary connector, and the motor is controlled by a logic control circuit to rotate axially according to the rotation angle by outputting a drive signal.

[0038] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0039] 1. This invention uses deep learning virtual amplification sampling technology to replace traditional multi-rotation angle physical rotation scanning, significantly shortening the imaging time and realizing high-speed three-dimensional imaging under single-pulse excitation, which is suitable for real-time dynamic monitoring scenarios.

[0040] 2. This invention utilizes the Fibonacci-Fermat double helix arrangement to optimize sound field coverage, and combines it with a U-Net network for virtual interpolation. It restores dense sampling features based on sparse sampling, outputs high-resolution three-dimensional images, and improves spatial resolution and imaging accuracy.

[0041] 3. The system integration hardware optimization and closed-loop control of the present invention, such as photoelectric encoders to ensure rotational accuracy and bearing slip rings to prevent fiber optic damage, improve the stability and reliability of the system, while reducing mechanical losses and maintenance costs.

[0042] 4. The method and system of the present invention are highly flexible and can adapt to complex imaging environments. By reducing the dependence on hardware rotation through virtual rotation interpolation, the application scope of photoacoustic imaging in biomedicine and other fields is broadened. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0044] Figure 1 The flowchart shows a spiral multi-point amplification sampling photoacoustic volumetric imaging method based on virtual rotation.

[0045] Figure 2 A schematic diagram showing the intersection of the interpolation point's axial rotation with the helix;

[0046] Figure 3 A schematic diagram of multi-point interpolation of rotation angle and helix;

[0047] Figure 4 This is a diagram showing the array element arrangement after interpolation and superposition in multiple spiral directions.

[0048] Figure 5 This is a structural diagram of a photoacoustic probe for multi-point amplification sampling photoacoustic volumetric imaging based on a virtual rotation spiral.

[0049] Figure 6 This is a comparison chart of network interpolation results. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0051] The terms "left," "right," "front," "back," "up," and "down" used in the following description refer to the orientation from the perspective of the front view.

[0052] Example 1: In some embodiments, please refer to the accompanying drawings. Figure 1 A spiral multi-point amplification sampling photoacoustic volumetric imaging system includes the following steps:

[0053] Step S1, obtain signal amplification training dataset: adjust the imaging area of ​​biological tissue or phantom sample to be within the set sound field range of the three-dimensional bowl-shaped ultrasonic array, and perform three-dimensional photoacoustic volume imaging on biological tissue or phantom sample to obtain non-rotation scanning sparse data, photoacoustic string map before amplification and photoacoustic string map after amplification.

[0054] Step S2, construct and train the interpolation network: construct a U-Net interpolation network, take the photoacoustic string map before amplification as input, and the photoacoustic string map after amplification as the real label for training, perform error analysis training, and save the optimal interpolation model with the smallest error;

[0055] Step S3, Fast sparse sampling without rotation: The sparse data from the non-rotational scan is recombined to obtain the photoacoustic chord map of the non-rotational scan sparse sampling.

[0056] Step S4: The optimal interpolation model performs virtual rotation interpolation: The photoacoustic string map with sparse sampling without rotation scanning is input into the optimal interpolation model, and the photoacoustic string map after virtual rotation amplification is obtained through virtual rotation interpolation of the optimal interpolation model; the array element channel data in the photoacoustic string map after virtual rotation amplification is extracted according to the array element channel number defined by the pre-generated interpolation sequence, and photoacoustic data of virtual rotation angle is generated.

[0057] Step S5, 3D reconstruction: The photoacoustic chord diagram of sparse sampling without rotation and the photoacoustic data corresponding to multiple virtual rotation angles are recombined into a photoacoustic data matrix of coordinate data according to their three-dimensional spatial coordinates. A high-resolution three-dimensional photoacoustic image equivalent to the real dense sampling is obtained through the three-dimensional reconstruction algorithm.

[0058] The specific operation of step S1 is as follows:

[0059] S11: Align the central axis of the three-dimensional bowl-shaped ultrasonic array arranged in the Fibonacci Fermat double helix with the center of the imaging area of ​​the biological tissue or phantom sample, and adjust the relative distance between the three-dimensional bowl-shaped ultrasonic array and the biological tissue or phantom sample so that the imaging area is within the set sound field range of the three-dimensional bowl-shaped ultrasonic array.

[0060] S12: Perform three-dimensional photoacoustic volume imaging on biological tissues or phantom samples to obtain a sparse photoacoustic signal data matrix and the three-dimensional spatial coordinates of each ultrasonic array element, as sparse data for non-rotation scanning.

[0061] S13: Perform multi-angle three-dimensional photoacoustic volumetric imaging of the same biological tissue or phantom sample by axial rotation, and obtain the photoacoustic signal data matrix corresponding to each rotation angle and the three-dimensional spatial coordinates corresponding to each ultrasonic array element, as interpolation data of the rotation angle.

[0062] S14: Based on the rotation angle and array element channel number corresponding to the pre-generated interpolation sequence, the sparse data of the non-rotation scanning is recombined to obtain the photoacoustic string map before amplification; the photoacoustic data of the corresponding array element channel is extracted from the interpolation data of the rotation angle, and the photoacoustic string map before amplification is interpolated and amplified to obtain the photoacoustic string map after amplification.

[0063] In the Fibonacci Fermat double helix arrangement, the three-dimensional spatial coordinates of each ultrasonic array element are as follows: The calculation is as follows:

[0064]

[0065] In the formula, The radius of the sphere of the three-dimensional bowl-shaped ultrasound array is given. The total number of array element channels. Number the array element channels , To cover the rotation angle of the array, The golden ratio constant has a value of , For the first Individual Planar projection radius. Array element channels are positioned according to their projection onto... The radii on the plane are numbered from the inside out, and these numbers represent the array element channel numbers. By extracting array elements at numbered intervals from the three-dimensional bowl-shaped ultrasonic array, spirals in different directions are formed, and the three-dimensional bowl-shaped ultrasonic array data is reconstructed into a two-dimensional photoacoustic chord map, where the horizontal axis of the two-dimensional photoacoustic chord map represents the sampling channel. The vertical axis represents the sampling time. .

[0066] The arrangement of the pre-generated interpolation sequences is determined by the position of the array element channel corresponding to the rotation angle;

[0067] Specifically, this includes: selecting any element channel within the spiral interval, rotating it around the depth axis, obtaining the intersection point where the rotation trajectory intersects the spiral line, calculating the rotation angle, and determining the staggered arrangement of the element channels on the spiral line before and after rotation under the rotation angle, which is the arrangement of the pre-generated interpolation sequence.

[0068] The U-Net interpolation network adopts an asymmetric convolutional kernel structure, and performs downsampling and upsampling in the time dimension.

[0069] The asymmetric convolution kernel structure is (3, 9);

[0070] Asymmetric convolutional kernel structures are used to adapt to the different characteristics of photoacoustic signals in the time dimension and the element channel dimension.

[0071] Downsampling and upsampling are performed in the time dimension to reduce the feature map size, expand the receptive field and capture macroscopic features, while maintaining integrity in the element channel dimension.

[0072] The specific operation of the asymmetric convolution kernel structure is as follows:

[0073] The training set contains 500 sets of data from the signal amplification training dataset. The mean squared error is used as the loss function, and the Adam optimizer is used for training with a learning rate of 0.001. After 300 iterations, the optimal model with the smallest error is saved.

[0074] Step S3 is performed as follows:

[0075] Using the three-dimensional bowl-shaped ultrasonic array in step S1, a single-pulse excitation scan is performed in a non-rotational state to obtain a sparse three-dimensional photoacoustic signal data matrix, which is then reconstructed into a photoacoustic chord diagram of sparse sampling without rotation.

[0076] This process reduces data acquisition time from several minutes in traditional multi-rotation angle scanning to seconds;

[0077] Virtual rotation interpolation is performed by inserting virtual rotated array element channels between unrotated array element channels, with the positions of the virtual rotated array element channels being consistent with those of the real rotated array element channels.

[0078] The position of the virtual rotating array element channel is obtained by superimposing the rotation matrix on the position of the array element channel. The source of its photoacoustic data is not actual acquisition, but virtual generation by the U-Net interpolation network.

[0079] Thus, under the constraint of real array element channel data, the U-Net interpolation network output signal can be restored to its sampling characteristics through training.

[0080] An imaging system includes a pulsed laser emission module, a rotation control module, a photoacoustic volumetric imaging probe, and a computer data processing module;

[0081] The pulsed laser emitting module includes a pulsed laser and a diffuser, with the output end of the pulsed laser connected to the diffuser.

[0082] The rotation control module includes a logic control circuit, a voltage regulator, a comparator, and a rotating motor;

[0083] The photoacoustic volumetric imaging probe includes a probe housing, a three-dimensional bowl-shaped ultrasonic transducer 1, a rotary connector, a bearing slip ring, an optical fiber bundle 2, and a diffusion lens 3.

[0084] A three-dimensional bowl-shaped ultrasonic transducer 1 is installed in the bowl-shaped cavity of the probe housing, with the three-dimensional bowl-shaped ultrasonic array arranged in a Fibonacci-Fermat double helix pattern.

[0085] The center of the probe housing has a one-inch internal threaded post channel, and the fiber bundle 2 and the diffusion lens 3 are pressed and fixed inside the channel by an external threaded iron ring.

[0086] Diffuser lens 3 is located at the front end of fiber bundle 2;

[0087] The probe housing is connected to the rotary motor via a rotary connector, and the logic control circuit is electrically connected to the rotary motor. The logic control circuit outputs a drive signal to control the rotary motor to rotate axially according to the rotation angle.

[0088] The computer data processing module includes a computer, a multi-element channel data acquisition unit, which is electrically connected to a three-dimensional bowl-shaped ultrasonic transducer. The multi-element channel data acquisition unit, the voltage regulator, and the three-dimensional bowl-shaped ultrasonic transducer 1 are all connected to the computer. The logic control circuit is connected to the comparator, the comparator is connected to the pulsed laser, and the voltage regulator is connected to the logic control circuit and the rotating motor.

[0089] The logic control circuit is responsible for outputting pulse signals for starting, stopping, and rotating the rotary motor; the comparator is used to control the laser pulses of the pulsed laser; the voltage regulator controls the logic control circuit, the voltage regulator, and the rotary motor to output the set voltage.

[0090] The laser emitted from the pulsed laser, after passing through the diffuser, will be converted from a collimated Gaussian beam into a flat-top beam with a relatively uniform energy distribution.

[0091] This makes the distribution of laser energy transmitted in fiber bundle 2 more uniform.

[0092] Three-dimensional bowl-shaped ultrasonic transducer 1 with a center frequency of 5MHz;

[0093] Fiber bundle 2 core diameter 200μm;

[0094] The fiber bundle 2 is embedded and fixed in the bearing slip ring to prevent rigid torsion damage caused by excessive rotation;

[0095] The diffusion lens 3 is fixed to the front end of the fiber bundle 2 and is used to excite a large area of ​​the imaging region.

[0096] The rotation control module also includes a photoelectric encoder, which is coaxially mounted with the rotary motor to provide real-time feedback of the actual rotation angle and to control the step error within ±0.05° through PID closed-loop control.

[0097] Specific examples: such as Figure 2-4 As shown,

[0098] First, the central axis of a three-dimensional bowl-shaped ultrasound array arranged in a Fibonacci-Fermat double helix pattern is aligned with the center of the imaging area of ​​the phantom blood vessel sample. The spherical radius of the three-dimensional bowl-shaped ultrasound array is R=50mm, the total number of array elements is N=256, and the array coverage angle is θ=130°. The relative distance between the array and the phantom blood vessel sample is adjusted to 10mm to ensure that the imaging area of ​​the phantom blood vessel sample is within the optimal acoustic field range of the array. Three-dimensional photoacoustic volume imaging is performed on the phantom blood vessel sample to obtain a sparse three-dimensional photoacoustic signal data matrix and the three-dimensional spatial coordinates corresponding to each channel ultrasound array element, which serve as the raw sparse data.

[0099] Subsequently, the required rotation angles (304.5°, 110.7°, 55.4°, etc.) were calculated in reverse by selecting the intersection points of the spiral line and the rotation trajectories of each channel within the 13-element interval. The same sample was then subjected to multi-angle axial rotation scanning to obtain the rotated photoacoustic signal data as interpolation data. Based on the positions of the elements within the spiral interval, the interpolation sequence for each channel was determined. The original sparse data was then recombined to obtain the photoacoustic chord map before amplification, resulting in 13 different photoacoustic chord maps with a size of 19 channels × 1024 time sampling points. Corresponding channel data was extracted from the rotation interpolation data and interpolated to generate amplified photoacoustic chord maps, resulting in 13 different amplified photoacoustic chord maps with a size of 73 channels × 1024 time sampling points, which were used for training the U-Net interpolation network.

[0100] The network framework employs U-Net, whose input is the photoacoustic chord map before amplification, and whose output is the amplified photoacoustic chord map. U-Net uses asymmetric convolutional kernels (3, 9) to adapt to the case where the temporal dimension of the photoacoustic chord map is much larger than the channel dimension, thus expanding the temporal receptive field. Simultaneously, downsampling and upsampling are performed only in the temporal dimension to maintain data integrity in the channel dimension. The training set contains 500 data points, using mean squared error as the loss function, and is trained using the Adam optimizer (learning rate 0.001). After 300 iterations, the optimal model with the smallest error is saved.

[0101] Next, a non-rotational scanning rapid sparse sampling is performed. For the new imaging area, the aforementioned three-dimensional bowl-shaped ultrasonic array is directly used to perform single-pulse excitation scanning in a non-rotational state to obtain a sparse three-dimensional photoacoustic signal data matrix, which is then reconstructed into a non-rotational scanning sparse sampling photoacoustic chord map.

[0102] Virtual rotation interpolation is performed using the optimal interpolation model. The sparsely sampled photoacoustic chord map obtained in step S03 is input into the trained optimal interpolation model to infer and generate amplified photoacoustic chord maps with virtual rotation angles of 304.5°, 110.7°, 55.4°, etc. Subsequently, photoacoustic data corresponding to the virtual rotation angles are generated in reverse based on the angles and channel numbers of the pre-generated interpolation sequence.

[0103] The photoacoustic data corresponding to multiple virtual rotation angles such as 304.5°, 110.7°, and 55.4° are recombined into a coordinate-data matrix according to their three-dimensional spatial coordinates, and then three-dimensional reconstruction is performed through a filtered back projection algorithm to output a high-resolution three-dimensional photoacoustic image.

[0104] To verify the effectiveness of this invention, network interpolation tests were performed on the phantom blood vessel sample. For example... Figure 6 As shown in the comparison of network interpolation results, the data from this application is highly consistent with real dense sampling data. This application achieves imaging under single-pulse excitation, improving temporal resolution to the second level, making it suitable for live dynamic monitoring scenarios.

[0105] This invention replaces physical rotation with virtual amplification sampling, significantly improving imaging speed and resolution, and is suitable for live dynamic monitoring scenarios.

[0106] The structure of the photoacoustic probe for multi-point amplification sampling photoacoustic volume imaging based on virtual rotation spiral is as follows: multiple sets of three-dimensional bowl-shaped ultrasonic transducers 1 are fixedly installed in the hemispherical groove opened in the outer shell 4 according to the Fibonacci-Fermat double helix arrangement; the fiber bundle 2 and the diffusion lens 3 are both installed in the mounting holes opened in the outer shell 4, and the diffusion lens 3 is located between the fiber bundle 2 and the three-dimensional bowl-shaped ultrasonic transducer 1.

[0107] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A spiral line multi-point extended sampling photoacoustic volumetric imaging method, characterized in that: The method comprises the following steps: Step S1, obtaining signal expansion training data set: adjusting the imaging area of the biological tissue or phantom sample to be within the set sound field range of the three-dimensional bowl-shaped ultrasonic array, performing three-dimensional photoacoustic volume imaging on the biological tissue or phantom sample to obtain non-rotation scanning sparse data, pre-expansion photoacoustic chord diagram and post-expansion photoacoustic chord diagram; Step S2, constructing and training the interpolation network: constructing a U-Net interpolation network, taking the pre-expansion photoacoustic chord diagram as the input and the post-expansion photoacoustic chord diagram as the true label for training, performing error analysis training, and saving the optimal interpolation model with the minimum error; Step S3, non-rotation scanning fast sparse sampling: recombining the non-rotation scanning sparse data to obtain the photoacoustic chord diagram of non-rotation scanning sparse sampling; Step S4, optimal interpolation model for virtual rotation interpolation: inputting the photoacoustic chord diagram of non-rotation scanning sparse sampling into the optimal interpolation model to obtain the virtual rotation post-expansion photoacoustic chord diagram through the virtual rotation interpolation of the optimal interpolation model; extracting the element channel data in the virtual rotation post-expansion photoacoustic chord diagram according to the element channel sequence number defined by the pre-generated interpolation sequence to generate the photoacoustic data of the virtual rotation angle; Step S5, three-dimensional reconstruction: recombining the photoacoustic chord diagram of non-rotation scanning sparse sampling and the photoacoustic data corresponding to multiple virtual rotation angles into a photoacoustic data matrix of coordinate data according to the three-dimensional space coordinates, and obtaining a high-resolution three-dimensional photoacoustic image equivalent to true dense sampling through a three-dimensional reconstruction algorithm.

2. The spiral multi-point extended sampling photoacoustic volumetric imaging method of claim 1, wherein, The specific operation of step S1 is as follows: S11: align the central axis of the three-dimensional bowl-shaped ultrasonic array arranged according to the Fibonacci Fermat double helix with the center of the imaging area of the biological tissue or phantom sample, and adjust the relative distance between the three-dimensional bowl-shaped ultrasonic array and the biological tissue or phantom sample so that the imaging area is within the set sound field range of the three-dimensional bowl-shaped ultrasonic array; S12: perform three-dimensional photoacoustic volume imaging on the biological tissue or phantom sample to obtain a sparse photoacoustic signal data matrix and three-dimensional space coordinates corresponding to each ultrasonic element as non-rotation scanning sparse data; S13: perform multi-angle three-dimensional photoacoustic volume imaging on the same biological tissue or phantom sample through axial rotation to obtain photoacoustic signal data matrices corresponding to each rotation angle and three-dimensional space coordinates corresponding to each ultrasonic element as interpolation data of the rotation angle; S14: recombine the non-rotation scanning sparse data according to the rotation angle and the element channel sequence number corresponding to the pre-generated interpolation sequence to obtain the pre-expansion photoacoustic chord diagram; extract the photoacoustic data of the corresponding element channel from the interpolation data of the rotation angle to interpolate and expand the pre-expansion photoacoustic chord diagram to obtain the post-expansion photoacoustic chord diagram.

3. The spiral multi-point extended sampling photoacoustic volumetric imaging method of claim 2, wherein, In the Fibonacci Fermat double-helix arrangement, the three-dimensional space coordinates corresponding to each ultrasonic array element The calculation is as follows: wherein, is the spherical radius of the three-dimensional bowl-shaped ultrasound array, is the total number of channels of the array element, is the channel number of the array element , is the array coverage rotation angle, is the golden ratio constant, which is , is the th array element is the planar projection radius.

4. The spiral multi-point extended sampling photoacoustic volumetric imaging method of claim 3, wherein, The arrangement mode of the pre-generated interpolation sequence is determined by the element channel position under the rotation angle; Specifically, any element channel in the helical interval is selected, which is rotated around the depth direction axis, the intersection of the rotation track and the helix is obtained, the rotation angle is calculated, and the element channels before and after rotation are staggered on the helix under the rotation angle, which is the arrangement mode of the pre-generated interpolation sequence.

5. The spiral multi-point extended sampling photoacoustic volumetric imaging method of claim 4, wherein, The U-Net interpolation network adopts an asymmetric convolution kernel structure and performs down-sampling and up-sampling in the time dimension.

6. The spiral multi-point extended sampling photoacoustic volumetric imaging method of claim 5, wherein, The virtual rotation interpolation specifically inserts a virtual rotation channel between non-rotation channels.

7. An imaging system using the spiral multi-point extended sampling photoacoustic volumetric imaging method according to any one of claims 1-6. The system comprises a pulse laser emission module, a rotation control module, a photoacoustic volumetric imaging probe, and a computer data processing module. The pulse laser emission module comprises a pulse laser and a diffusion sheet, and the output end of the pulse laser is connected with the diffusion sheet. The rotation control module comprises a logic control circuit, a voltage stabilizer, a comparator, and a rotating motor. The photoacoustic volumetric imaging probe comprises a probe shell, a three-dimensional bowl-shaped ultrasonic transducer (1), a rotating connector, a bearing slip ring, a fiber bundle (2), and a diffusion lens (3). The three-dimensional bowl-shaped ultrasonic transducer (1) is arranged in a Fibonacci-Fermat double helix three-dimensional bowl-shaped ultrasonic array in a bowl-shaped cavity of the probe shell. The center of the probe shell is provided with a one-inch internal threaded column channel, and the fiber bundle (2) and the diffusion lens (3) are fixed in the channel by an external threaded iron ring. The diffusion lens (3) is located at the front end of the fiber bundle (2). The probe shell is connected with the rotating motor through the rotating connector, and the logic control circuit is electrically connected with the rotating motor. The computer data processing module comprises a computer and a multi-channel data acquisition device, the multi-channel data acquisition device is electrically connected with the three-dimensional bowl-shaped ultrasonic transducer, the multi-channel data acquisition device, the voltage stabilizer, and the three-dimensional bowl-shaped ultrasonic transducer (1) are connected with the computer, the logic control circuit is connected with the comparator, the comparator is connected with the pulse laser, and the voltage stabilizer is connected with the logic control circuit and the rotating motor.

8. The imaging system of claim 7, wherein, The three-dimensional bowl-shaped ultrasonic transducer (1) has a center frequency of 5 MHz.

9. The imaging system of claim 7, wherein, The fiber bundle (2) has a core diameter of 200 μm.

10. The imaging system of claim 7, wherein, The rotation control module further comprises an optical encoder coaxially installed with the rotating motor, which is used for real-time feedback of the actual rotation angle and controls the stepping error within ±0.05° through PID closed-loop control.

Images