A simultaneous functional-structural brain imaging system based on acoustoelectric effect

Abstract

The application relates to a synchronous function-structure brain imaging system based on an acoustoelectric effect, which comprises a transcranial ultrasound multi-site focusing and synchronous scanning module, a nerve discharge function and brain tissue structure distribution detection module and a brain function-structure coupling module based on resistivity; wherein the transcranial ultrasound multi-site focusing and synchronous scanning module is used for carrying out multi-site ultrasound focusing on a target brain area and implementing synchronous scanning, so as to generate a nerve acoustoelectric signal based on the acoustoelectric effect; the nerve discharge function and brain tissue structure distribution detection module is used for detecting the nerve acoustoelectric signal which has a response relationship with original brain electricity and tissue resistivity, analyzing time-frequency variation and amplitude-space distribution of the original brain electricity and mapping brain tissue structure distribution; and the brain function-structure coupling module based on resistivity is used for correlating and coupling nerve discharge characteristics based on resistivity and tissue structure distribution, so as to realize synchronous function-structure brain imaging.

Description

TECHNICAL FIELD

[0001] The present application relates to a synchronous function-structure brain imaging system based on acoustoelectric effect. BACKGROUND

[0002] Neuroimaging refers to the technology capable of directly or indirectly imaging the function and structural characteristics of the nervous system (mainly the brain). According to the imaging mode, neuroimaging can be divided into functional imaging (EEG, MEG, etc.) and structural imaging (CT, MRI, etc.). Functional imaging is used to show the metabolic activity of the brain when performing a certain task (including sensory, motor, cognitive, etc. functions). Structural imaging helps diagnose brain diseases by showing the structure of the brain. Simultaneous detection of brain function and structure is of great significance for efficient clinical diagnosis of neurological diseases.

[0003] Due to the physical property differences in detecting biological signals, functional imaging and structural imaging are difficult to integrate, and multi-modal imaging methods have the problem of high matching of different data types. Based on acoustoelectric effect, acoustoelectric imaging can achieve non-invasive high-resolution electroencephalogram detection by using focused ultrasound to target change the local electrical resistivity of the brain. As a potential functional imaging method, acoustoelectric imaging has a solid theoretical and experimental research foundation and has shown important application value in bioelectric current detection such as electroencephalogram and electrocardiogram. The acoustoelectric signal equation shows that the acoustoelectric signal is closely related to the electrical resistivity that affects the current density of biological tissue. Different brain tissues (such as cerebrospinal fluid, white matter, black matter, etc.) have different electrical resistivity, so the distribution of brain tissue structure can be located by detecting the electrical resistivity. By using the response relationship between acoustoelectric signal and electroencephalogram and electrical resistivity, it is expected to achieve synchronous function-structure brain imaging by only detecting electroencephalogram under acoustoelectric effect. Therefore, the present application proposes a synchronous function-structure brain imaging system based on acoustoelectric effect, which provides technical support for directly and efficiently detecting brain function and structure. SUMMARY

[0004] The present application aims to use the direct response relationship between acoustoelectric signal and electroencephalogram and electrical resistivity based on acoustoelectric effect to analyze the original electroencephalogram characteristics and simultaneously map the electrical resistivity distribution characteristics, thereby achieving synchronous function-structure brain imaging. Compared with existing synchronous function-structure neuroimaging methods, this system only needs to detect electroencephalogram under acoustoelectric effect, which can directly reflect the brain neural discharge function and structural distribution, and is expected to become a new type of synchronous function-structure neuroimaging system. Further research and practice can achieve efficient and accurate clinical diagnosis effect and obtain considerable social and economic benefits.

[0005] A synchronous functional-structural brain imaging system based on the electroacoustic effect includes a transcranial ultrasound multi-site focusing and synchronous scanning module, a neural discharge function and brain tissue structure distribution detection module, and a resistivity-based brain function-structure coupling module; wherein the transcranial ultrasound multi-site focusing and synchronous scanning module is used to perform multi-site ultrasound focusing on the target brain area and implement synchronous scanning to generate neural electroacoustic signals based on the electroacoustic effect; the neural discharge function and brain tissue structure distribution detection module is used to detect neural electroacoustic signals that have a response relationship with the original EEG and tissue resistivity, analyze the time-frequency changes and amplitude-space distribution of the original EEG, and map the brain tissue structure distribution; the resistivity-based brain function-structure coupling module is used to correlate and couple the neural discharge characteristics based on resistivity with the tissue structure distribution to achieve synchronous functional-structural brain imaging.

[0006] Furthermore, the transcranial ultrasound multi-site focusing and synchronous scanning module includes:

[0007] Transcranial ultrasound multi-point focusing unit: The transcranial ultrasound multi-point focusing unit includes a multi-element phased array and its transducer, which is used to simultaneously transmit focused ultrasound at multiple locations in the target brain area;

[0008] Transcranial ultrasound synchronous scanning unit: The transcranial ultrasound synchronous scanning unit is used to focus multiple sites for programmable dynamic synchronous scanning; based on the location, size, and distribution of the target brain area, the number of sites is selected and the scanning strategy is designed. The starting position of the site, scanning step size, scanning block, scanning sequence, and scanning duration parameters are controlled by programming and executing scripts to achieve transcranial ultrasound scanning.

[0009] Furthermore, the transcranial ultrasound multi-site focusing unit can preset the number of sites = total number of array elements / number of single-focus array elements, select the array element spacing and emission angle parameters according to actual needs, and control the distance and angle between each site.

[0010] Furthermore, the neural discharge function and brain tissue structure distribution detection module includes:

[0011] Neural discharge function analysis unit: reconstructs the current density distribution from the measured voltage at each scanning position; obtains the analytical acoustic and electrical signal reflecting the original EEG characteristics; performs windowing and Fourier transform on the analytical acoustic and electrical signal reflecting the original EEG characteristics to extract the frequency domain information within the time window; slides the window forward along the time axis to analyze the time-frequency variation information of each time window; calculates the power spectral density integral within a certain frequency band and maps it to the corresponding spatial position to characterize its functional amplitude-space distribution;

[0012] Brain tissue structure distribution mapping unit: establishes a positive correlation response relationship between the measured voltage and current density distribution at each scanning position, and obtains the mathematical relationship between the measured voltage and resistivity based on the mapping relationship between current density and resistivity; based on the specific resistivity of different structures of cranial tissue, the mapping relationship between the measured voltage and brain tissue structure is established through resistivity.

[0013] Furthermore, the neural discharge function analysis unit includes: reconstructing the current density distribution J from the measured voltage V(x,y,z) at each scanning position (x,y,z) i (x,y,z); extract the acoustic-electrical signal s from the measured voltage V(x,y,z) at each scanning position (x,y,z) according to the ultrasonic frequency AE (x, y, z, t), take its envelope to get the analytical sound and electric signal s reflecting the original EEG characteristics AE-EEG (x, y, z, t), t represents time; for s AE-EEG (x, y, z, t) is windowed and Fourier transformed to extract the frequency domain information within the time window; the window is moved forward along the time axis to analyze the time-frequency variation information of each time window; the acoustic and electrical signal s is analyzed. AE-EEG (x, y, z, t) calculates the power spectral density integral within a certain frequency band and maps it to the corresponding spatial position to characterize its functional amplitude-space distribution.

[0014] Furthermore, the brain tissue structure distribution mapping unit includes: the measured voltage V(x,y,z) and the current density J at each scanning position (x,y,z) i (ρ, x, y, z) satisfy the following positive correlation response relationship, where N represents N lead fields J L , K represents the action coefficient:

[0015]

[0016] According to the current density J i The mapping relationship between (ρ, x, y, z) and resistivity ρ(x, y, z) (2) gives the mathematical relationship between the measured voltage V(x, y, z) and resistivity:

[0017] |J i (ρ,x,y,z)|=1.809×10 -8 ×ρ(x,y,z) -0.3196 -8.623×10 -9 (2)

[0018]

[0019] According to the specific resistivity of brain tissues with different structures, the mapping relationship between the measured voltage and the brain tissue structure is established through the resistivity ρ(x, y, z).

[0020] Furthermore, the resistivity-based brain function-structure coupling module identifies the brain structure to which each scanning position belongs point by point according to the mapping relationship between the resistivity value output by the brain tissue structure distribution mapping unit and the cranial brain tissue structure, and associates and couples it with the neural discharge function at the corresponding position to achieve synchronous function-structure brain imaging.

[0021] Furthermore, the resistivity-based brain function-structure coupling module includes:

[0022] Structural current reconstruction resistivity unit: Based on the mapping relationship between measured voltage and resistivity established by the brain tissue structure distribution mapping unit, the reconstructed resistivity at each scanning position is obtained by analyzing the measured voltage;

[0023] Resistivity-based brain structure identification unit: Determines the brain structure to which each scan position belongs based on the approximate relationship between the reconstructed resistivity and the reference value of brain tissue resistivity;

[0024] Brain function-structure coupling unit: maps the analytical acoustic and electrical signals to the coupled tissue structures through position coordinates to achieve synchronous functional-structural brain imaging.

[0025] Furthermore, the specific process of the structural current reconstruction resistivity unit is as follows:

[0026] According to the mapping relationship between the measured voltage and resistivity established by the brain tissue structure distribution mapping unit, the reconstructed resistivity ρ at each scanning position (x, y, z) is obtained by analyzing the measured voltage V(x, y, z) J-AE (x,y,z) and satisfy the following mathematical relationship:

[0027]

[0028] Furthermore, the specific process of the resistivity-based brain structure discrimination unit is as follows:

[0029] Calculate and reconstruct the resistivity ρ point by point J-AE The absolute value of the difference between (x, y, z) and the reference values ​​of various brain tissue resistivity Δ i (x,y,z) is as follows:

[0030]

[0031] i = scalp, skull, cerebrospinal fluid, gray matter, white matter

[0032] Take Δ i The minimum value of (x,y,z)min{Δ i(x, y, z)} as the decision value, and its corresponding brain structure i is the judgment result I(x, y, z, i) of the scanning position (x, y, z).

[0033] The synchronous functional-structural brain imaging system designed by the present invention, based on the electroacoustic effect, utilizes the direct response relationship between electroacoustic signals based on the electroacoustic effect and EEG and resistivity to comprehensively analyze the original EEG characteristics and simultaneously map the resistivity distribution characteristics to achieve synchronous functional-structural brain imaging. Compared with existing synchronous functional-structural neuroimaging systems, the synchronous functional-structural brain imaging system based on the electroacoustic effect has the following advantages:

[0034] (1) Simply by detecting the scalp EEG signal under the acoustic-electric effect, the brain's neural discharge function and structural distribution can be directly reflected.

[0035] (2) Use non-invasive, highly focused transcranial focused ultrasound to detect EEG signals in the target brain area, with both high temporal and high spatial resolution.

[0036] (3) It is expected that a new type of synchronous functional-structural brain imaging system will be established to provide technical support for efficient and rapid clinical diagnosis and neuroscience research. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the system structure of the present invention;

[0038] Figure 2 It is a workflow diagram of the present invention;

[0039] Figure 3 This is a workflow diagram of the neural discharge function and brain tissue structure distribution detection module of the present invention;

[0040] Figure 4 This is a workflow diagram of the resistivity-based brain function-structure coupling module of the present invention. DETAILED DESCRIPTION

[0041] The present invention designs a synchronous functional-structural brain imaging system based on the acoustoelectric effect. Brain imaging can detect changes in the structure and function of the living brain non-invasively or minimally invasively, and provide a reliable reference for studying the pathogenesis and diagnosis and treatment evaluation of diseases. The research and development of new technologies for synchronous functional-structural brain imaging plays a vital role in clinical disease diagnosis and neuroscience research. The acoustoelectric effect is a basic physical phenomenon of the mutual coupling of sound and electric fields, that is, ultrasound can cause changes in the local resistivity of the medium. Based on the acoustoelectric effect, focused ultrasound can be used to target and change the local resistivity of the brain to obtain high-resolution EEG information of the target brain area, and simultaneously map brain tissue structures with different resistivity distribution characteristics. It is expected that synchronous functional-structural brain imaging can be achieved by non-invasively detecting EEG signals under the acoustoelectric effect.

[0042] The technical process of the present invention is: using transcranial focused ultrasound scanning to detect brain areas in a targeted manner, and simultaneously collecting scalp EEG signals under the electroacoustic effect; preprocessing the collected EEG signals to obtain electroacoustic signals; analyzing the time-frequency-amplitude-space response of the original EEG of the target brain area from the electroacoustic signals to map its neural function changes; analyzing the resistivity characteristics of the scanned brain area tissue from the electroacoustic signals to depict its brain tissue structure distribution; correlating the functional changes and structural distribution analyzed by the coupled electroacoustic signals to achieve synchronous functional-structural brain imaging based on the electroacoustic effect.

[0043] The present invention will be described below with reference to the accompanying drawings and embodiments.

[0044] 1 Overall system design plan

[0045] The structural block diagram of the synchronous functional-structural brain imaging system based on the acoustic-electric effect is as follows Figure 1 As shown. The system mainly consists of a transcranial ultrasound multi-site focusing and synchronous scanning module, a neural discharge function and brain tissue structure distribution detection module, and a resistivity-based brain function-structure coupling module. Among them, the transcranial ultrasound multi-site focusing and synchronous scanning module is used to perform multi-site ultrasound focusing on the target brain area and implement synchronous scanning to generate neural acoustic-electrical signals based on the electroacoustic effect; the neural discharge function and brain tissue structure distribution detection module is used to detect neural acoustic-electrical signals that have a response relationship with the original EEG and tissue resistivity, analyze the time-frequency changes and amplitude-space distribution of the original EEG, and map the distribution of brain tissue structures such as sulci and cortex; the resistivity-based brain function-structure coupling module is used to correlate and couple the resistivity-based neural discharge characteristics with tissue structure distribution to achieve synchronous function-structure brain imaging.

[0046] The workflow of the synchronous functional-structural brain imaging system based on the electroacoustic effect is shown in the figure below. Figure 2 As shown, the whole process can be divided into the following three steps: ① multi-site ultrasound focusing and synchronous scanning; ② detection of neural discharge function and brain tissue structure distribution; ③ correlation and coupling between neural discharge characteristics and tissue structure distribution.

[0047] 2Transcranial ultrasound multi-site focusing and synchronous scanning module

[0048] (1) Transcranial ultrasound multi-site focusing unit: The transcranial ultrasound multi-site focusing unit can be composed of a 512-element phased array and its transducer, which is used to simultaneously transmit focused ultrasound at multiple locations in the target brain area. The number of preset sites = total number of elements / number of single-focus elements. If 64 elements are selected to focus on one site, 8-site focusing can be achieved. It is expected that 1-8 sites can be selected, and the effective focal spot diameter is less than 1mm. Parameters such as element spacing and emission angle are selected according to actual needs to control the distance and angle between each site.

[0049] (2) Transcranial ultrasound synchronous scanning unit: The transcranial ultrasound synchronous scanning unit is used to focus multiple sites for programmable dynamic synchronous scanning. Based on the location, size, and distribution of the target brain area, the number of sites is selected and the scanning strategy is designed. By programming and executing scripts, parameters such as the site starting position, scanning step size, scanning block, scanning sequence, and scanning duration are controlled to achieve fast, accurate, and comprehensive transcranial ultrasound scanning.

[0050] 3. Neural discharge function and brain tissue structure distribution detection module

[0051] The acoustic and electrical signals under the acoustic and electrical effect are collected by conventional EEG amplifiers AE (t), the workflow of the neural discharge function and brain tissue structure distribution detection module is as follows Figure 3 shown.

[0052] (1) Neural discharge function analysis unit: First, the current density distribution J is reconstructed from the measured voltage V(x,y,z) at each scanning position (x,y,z) i (x,y,z), which can reflect the distribution of nerve discharge amplitude in space. According to the ultrasound frequency, the acoustic-electrical signal s is extracted from the measured voltage V(x,y,z) at each scanning position (x,y,z). AE (x, y, z, t), take its envelope to get the analytical sound and electric signal s that can reflect the original EEG characteristics AE-EEG (x,y,z,t), t represents time. AE-EEG (x, y, z, t) is windowed and Fourier transformed to extract the frequency domain information within the time window. The window is moved forward along the time axis, and the data within each time window is processed in the same way to analyze its time-frequency variation information; AE-EEG (x, y, z, t) calculates the power spectral density integral within a certain frequency band and maps it to the corresponding spatial position to characterize its functional amplitude-space distribution.

[0053] (2) Brain tissue structure distribution mapping unit: measured voltage V(x,y,z) and current density J at each scanning position (x,y,z) i (ρ, x, y, z) satisfy the following positive correlation response relationship, where N represents N lead fields J L , K represents the action coefficient

[0054]

[0055] According to the current density J iThe mapping relationship between (ρ, x, y, z) and resistivity ρ(x, y, z) is obtained from Equation (2). The mathematical relationship between the measured voltage V(x, y, z) and resistivity is shown in Equation (3) (the model can be found in: Yijie Zhou, Yibo Song, Xizi Song, Minpeng Xu, Feng He, Dong Ming. Synchronous mapping of neural current source and sulcus with acoustoelectric brain imaging. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 4507910).

[0056] |J i (ρ,x,y,z)|=1.809×10 -8 ×ρ(x,y,z) -0.3196 -8.623×10 -9 (2)

[0057]

[0058] Different structures of brain tissue have specific resistivity, and their reference values ​​are shown in Table 1. At this point, the mapping relationship between the measured voltage and brain tissue structure is established through the resistivity ρ(x, y, z).

[0059] Table 1. Cranial tissue resistivity parameters

[0060] Brain tissue name Resistivity (Ω·m) scalp 3.215 skull 50 cerebrospinal fluid 0.500 gray matter 9.346 White matter 15.152

[0061] 4. Resistivity-based brain function-structure coupling module

[0062] The workflow of the resistivity-based brain function-structure coupling module is as follows: Figure 4 According to the mapping relationship between the output resistivity value of the brain tissue structure distribution mapping unit and the cranial brain tissue structure, the brain structure to which each scanning position belongs is identified point by point, and the structure is correlated with the neural discharge function of the corresponding position.

[0063] (1) Structural current reconstruction resistivity unit: According to the mapping relationship between the measured voltage and resistivity described in the brain tissue structure distribution mapping unit (3), the reconstructed resistivity ρ at each scanning position (x, y, z) can be obtained by analyzing the measured voltage V(x, y, z) J-AE (x,y,z) and satisfy the following mathematical relationship:

[0064]

[0065] (2) Resistivity-based brain structure discrimination unit: According to the reconstructed resistivity ρ J-AE The approximate relationship between (x, y, z) and the reference value of the brain tissue resistivity is used to determine the brain structure to which each scanning position (x, y, z) belongs. Specific process: First, calculate the reconstructed resistivity ρ point by point J-AE The absolute value of the difference between (x, y, z) and the reference values ​​of various brain tissue resistivity Δ i (x,y,z) is as follows:

[0066]

[0067] i = scalp, skull, cerebrospinal fluid, gray matter, white matter

[0068] Then, take Δ i The minimum value of (x,y,z)min{Δ i (x, y, z)} as the decision value, and its corresponding brain structure i is the judgment result I(x, y, z, i) of the scanning position (x, y, z).

[0069] (3) Brain function-structure coupling unit: The analytical acoustic-electrical signal s is converted to AE-EEG (x, y, z, t) is mapped to the coupled tissue structure I(x, y, z, i) to achieve synchronous functional-structural brain imaging, which is expressed as shown in Equation (6).

[0070]

[0071] i = scalp, skull, cerebrospinal fluid, gray matter, white matter.

Claims

1. A synchronous functional-structural brain imaging system based on the acoustic-electric effect, comprising a transcranial ultrasound multi-site focusing and synchronous scanning module, a neural discharge function and brain tissue structure distribution detection module, and a resistivity-based brain function-structure coupling module; wherein, The transcranial ultrasound multi-site focusing and synchronous scanning module is used to perform multi-site ultrasound focusing on the target brain area and implement synchronous scanning to generate neural acoustic-electrical signals based on the acoustic-electric effect; The neural discharge function and brain tissue structure distribution detection module is used to detect neural acoustic and electrical signals that have a response relationship with the original EEG and tissue resistivity, analyze the time-frequency changes and amplitude-space distribution of the original EEG, and map the brain tissue structure distribution. The resistivity-based brain function-structure coupling module is used to correlate and couple the resistivity-based neural discharge characteristics with the tissue structure distribution to achieve synchronous function-structure brain imaging. The neural discharge function and brain tissue structure distribution detection module includes: Neural discharge function analysis unit: reconstructs the current density distribution from the measured voltage at each scanning position; obtains the analytical acoustic and electrical signals reflecting the original EEG characteristics; performs windowing and Fourier transform on the analytical acoustic and electrical signals reflecting the original EEG characteristics to extract the frequency domain information within the time window; slides the window forward along the time axis to analyze the time-frequency change information of each time window; calculates the power spectral density integral within a certain frequency band and maps it to the corresponding spatial position to characterize its functional amplitude-space distribution, specifically: reconstructs the current density distribution J from the measured voltage V(x,y,z) at each scanning position (x,y,z) i (x,y,z); extract the acoustic-electrical signal s from the measured voltage V(x,y,z) at each scanning position (x,y,z) according to the ultrasonic frequency AE (x, y, z, t), take its envelope to get the analytical sound and electric signal s reflecting the original EEG characteristics AE-EEG (x, y, z, t), t represents time; for s AE-EEG (x, y, z, t) is windowed and Fourier transformed to extract the frequency domain information within the time window; the window is moved forward along the time axis to analyze the time-frequency variation information of each time window; the acoustic and electrical signal s is analyzed. AE-EEG (x, y, z, t) calculates the power spectral density integral within a certain frequency band and maps it to the corresponding spatial position to characterize its functional amplitude-space distribution; Brain tissue structure distribution mapping unit: establishes a positive correlation response relationship between the measured voltage and current density distribution at each scanning position, and obtains the mathematical relationship between the measured voltage and resistivity based on the mapping relationship between current density and resistivity; according to the specific resistivity of different structures of cranial tissue, the mapping relationship between the measured voltage and brain tissue structure is established through resistivity, specifically: the measured voltage V(x,y,z) and current density J at each scanning position (x,y,z) are i (ρ, x, y, z) satisfy the following positive correlation response relationship, where N represents N lead fields J L , K represents the action coefficient: According to the current density J i The mapping relationship between (ρ, x, y, z) and resistivity ρ(x, y, z) (2) gives the mathematical relationship between the measured voltage V(x, y, z) and resistivity: |J i (ρ,x,y,z)|=1.809×10 -8 ×ρ(x,y,z) -0.3196 −8,623 × 10 -9 (2) According to the specific resistivity of brain tissues with different structures, the mapping relationship between the measured voltage and brain tissue structure is established through the resistivity ρ(x, y, z); The resistivity-based brain function-structure coupling module, based on the mapping relationship between the resistivity value output by the brain tissue structure distribution mapping unit and the cranial brain tissue structure, identifies the brain structure to which each scanning position belongs point by point, and associates and couples it with the neural discharge function at the corresponding position to achieve synchronous function-structure brain imaging; the resistivity-based brain function-structure coupling module includes: Structural current reconstruction resistivity unit: Based on the mapping relationship between the measured voltage and resistivity established by the brain tissue structure distribution mapping unit, the reconstructed resistivity of each scanning position is obtained by analyzing the measured voltage. Specifically, based on the mapping relationship between the measured voltage and resistivity established by the brain tissue structure distribution mapping unit, the reconstructed resistivity ρ of each scanning position (x, y, z) is obtained by analyzing the measured voltage V(x, y, z). J-AE (x,y,z) and satisfy the following mathematical relationship: Resistivity-based brain structure discrimination unit: Based on the approximate relationship between the reconstructed resistivity and the reference value of the brain tissue resistivity, the brain structure to which each scan position belongs is determined. Specifically, the reconstructed resistivity ρ is calculated point by point. J-AE The absolute value of the difference between (x, y, z) and the reference value of various brain tissue resistivity△ i (x,y,z) is as follows: Take △ i The minimum value of (x,y,z)min{△ i (x, y, z)} as the decision value, and its corresponding brain structure i is the discrimination result I(x, y, z, i) of the scanning position (x, y, z); Brain function-structure coupling unit: maps the analytical acoustic and electrical signals to the coupled tissue structures through position coordinates to achieve synchronous functional-structural brain imaging.

2. The synchronous functional-structural brain imaging system based on the electroacoustic effect according to claim 1, characterized in that: The transcranial ultrasound multi-site focusing and synchronous scanning module includes: Transcranial ultrasound multi-point focusing unit: The transcranial ultrasound multi-point focusing unit includes a multi-element phased array and its transducer, which is used to simultaneously transmit focused ultrasound at multiple locations in the target brain area; Transcranial ultrasound synchronous scanning unit: The transcranial ultrasound synchronous scanning unit is used to focus multiple sites for programmable dynamic synchronous scanning; based on the location, size, and distribution of the target brain area, the number of sites is selected and the scanning strategy is designed. The starting position of the site, scanning step size, scanning block, scanning sequence, and scanning duration parameters are controlled by programming and executing scripts to achieve transcranial ultrasound scanning.

3. The synchronous functional-structural brain imaging system based on the electroacoustic effect according to claim 2, characterized in that: The transcranial ultrasound multi-site focusing unit can preset the number of sites = total number of array elements / number of single-focus array elements, select the array element spacing and emission angle parameters according to actual needs, and control the distance and angle between each site.

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