Three-dimensional ultrasonic imaging method and system, computer equipment and computer program product

Abstract

The invention is suitable for the technical field of ultrasonic imaging, and provides a three-dimensional ultrasonic imaging method and system, computer equipment and a computer program product, and the method comprises the steps: transmitting a multi-angle composite plane wave sequence to a target region through a one-dimensional ultrasonic array with the frequency gradual change characteristic in the elevation direction, and receiving a reflected echo signal sequence, generating a target gray-scale image sequence corresponding to the echo signal sequence, acquiring coordinates of each ultrasonic contrast agent microbubble in each frame of gray-scale image, generating a two-dimensional coordinate set, calculating the axial half-peak full width of a point spread function region of each ultrasonic contrast agent microbubble, and calculating the axial half-peak full width of the point spread function region of each ultrasonic contrast agent microbubble; determining the coordinate of each ultrasonic contrast agent microbubble in the elevation angle direction based on the axial half-peak full width, generating an elevation angle coordinate set, and generating a three-dimensional contrast image of the target area based on the two-dimensional coordinate set and the elevation angle coordinate set. According to the method provided by the invention, the problem that three-dimensional ultrasonic imaging is limited by the surface ultrasonic array can be solved, and low-cost, simple and efficient three-dimensional ultrasonic imaging based on the one-dimensional ultrasonic array is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of ultrasonic imaging, and particularly relates to a three-dimensional ultrasonic imaging method, system, computer device and computer program product. BACKGROUND

[0002] Due to the advantages of non-invasiveness, no ionizing radiation, convenient operation, rich imaging modes and economy, ultrasonic imaging technology has become an indispensable imaging tool in clinical diagnosis and basic research. Ultrasonic localization microscopic imaging technology is a kind of ultrasonic imaging technology, which can realize sub-wavelength spatial resolution while maintaining centimeter-level penetration depth by detecting, locating and tracking ultrasonic contrast agent microbubbles, and provides an innovative solution for high-resolution visualization of deep tissue microvascular systems.

[0003] With the development of electronic technology, a planar array has been introduced into the field of ultrasonic localization microscopic imaging technology, which can synchronously obtain three-dimensional spatial coordinate parameters of ultrasonic contrast agent microbubbles, thereby realizing three-dimensional super-resolution angiography. However, the planar array ultrasonic array adopts a high-density element arrangement structure, which leads to significant technical challenges in manufacturing process complexity, material performance requirements and production cost control. In addition, the planar array ultrasonic array usually needs thousands of electronic channels to independently control each element, which puts forward strict technical requirements for the multi-channel hardware system. The above challenges jointly restrict the large-scale popularization and application of three-dimensional super-resolution angiography technology in clinical practice.

[0004] Therefore, there is an urgent need for a more efficient and simple three-dimensional ultrasonic imaging method. SUMMARY

[0005] Therefore, the embodiments of the present application provide a three-dimensional ultrasonic imaging method, system, computer device and computer program product to solve the problem that three-dimensional ultrasonic imaging is limited by a planar ultrasonic array, and realize simpler and more efficient three-dimensional ultrasonic imaging based on a one-dimensional ultrasonic array with low cost.

[0006] The first aspect of the embodiments of the present application provides a three-dimensional ultrasonic imaging method, comprising: emitting a multi-angle composite plane wave sequence to a target region through a one-dimensional ultrasonic array with an elevation direction frequency gradient characteristic; receiving echo signals reflected by the target region based on the multi-angle composite plane wave sequence to obtain an echo signal sequence; generating a target gray scale image sequence corresponding to the echo signal sequence through beam synthesis; obtaining coordinates of each ultrasonic contrast agent microbubble in each gray scale image in the target gray scale image sequence, and generating a two-dimensional coordinate set; Calculate the full width at half maximum (FWHM) of the point diffusion function region for each of the ultrasound contrast agent microbubbles; Based on the axial full width at half maximum (FWHM) and the pre-established full width at half maximum (FWHM) - elevation coordinate mapping relationship, the coordinates of each ultrasound contrast agent microbubble in the elevation direction are determined and an elevation coordinate set is generated. A three-dimensional angiographic image of the target region is generated based on the two-dimensional coordinate set and the elevation coordinate set.

[0007] In one implementation of the first aspect, generating the target grayscale image sequence corresponding to the echo signal sequence through beamforming includes: Each echo signal in the echo signal sequence is preprocessed to obtain a preprocessed echo signal sequence. The signal preprocessing includes Hilbert transform, quadrature demodulation, and downsampling. Delay compensation is performed on each echo signal in the preprocessed echo signal sequence to obtain the target echo signal sequence. An initial grayscale image sequence is obtained by coherently superimposing the target echo signal sequence; The initial grayscale image sequence is used as the target grayscale image sequence.

[0008] In one implementation of the first aspect, after using the initial grayscale image sequence as the target grayscale image sequence, the method further includes: The initial grayscale image sequence is filtered using a spatiotemporal filter based on singular value decomposition to obtain a filtered grayscale image sequence. The filtered grayscale image sequence is used as the target grayscale image sequence.

[0009] In one implementation of the first aspect, the step of using a spatiotemporal filter based on singular value decomposition to filter the initial grayscale image sequence to obtain a filtered grayscale image sequence includes: grayscale image sequence Convert to a two-dimensional spatiotemporal sequence matrix , where the matrix The size of the first dimension is The size of the second dimension is ,in, The target grayscale image sequence are respectively The number of spatial samples in the x-axis, the number of spatial samples in the z-axis, and the number of temporal samples; For the matrix Perform singular value decomposition to obtain the corresponding spatial singular matrix. Time Singular Matrix Non-square diagonal matrix The matrix and the matrix Each column is the matrix Spatial and temporal singular vectors, the matrix Each element is the matrix The singular values ​​of the matrix, and the matrix and Satisfying Relationship: ; The matrix Decomposed into multiple separable matrices An ordered weighted sum, where each matrix It can be represented as the tensor product of two vector matrices:

[0010] in, and The first and second parts of matrix U and matrix V are respectively the first and second parts of matrix V. List, For the first One singular value; By calculating the first derivative of the singular value curve, the starting point where the singular value curve becomes flat can be identified, and the optimal cutoff threshold can be determined. ; Filtered grayscale image sequence The following representation is made:

[0011] in, Let S be the rank of matrix S.

[0012] In one implementation of the first aspect, obtaining the coordinates of each ultrasound contrast agent microbubble in each frame of the target grayscale image sequence and generating a two-dimensional coordinate set includes: Upsampling is performed on each frame of grayscale image in the target grayscale image sequence; The grayscale image of each frame that has been upsampled is cross-correlated with the standard ultrasound image of the ultrasound contrast agent microbubble to obtain the corresponding correlation coefficient spectrum. In each correlation coefficient map, pixel regions with correlation values ​​lower than a preset correlation coefficient threshold are zeroed out. Connectivity analysis is performed on each correlation coefficient graph after zeroing to obtain the area of ​​each connected region in each correlation coefficient graph after zeroing. Connected regions with an area greater than a preset area threshold are identified and retained as target connected regions, and each target connected region corresponds to a point diffusion function region of the ultrasound contrast agent microbubble; Extract the centroid coordinates of the target connected domain, and each centroid coordinate corresponds to a two-dimensional coordinate of the ultrasound contrast agent microbubble; A two-dimensional coordinate set is generated based on the centroid coordinates.

[0013] In one implementation of the first aspect, calculating the full width at half maximum (FWHM) of the point diffusion function region for each of the ultrasound contrast agent microbubbles includes: With the centroid of the point spread function region of each ultrasound contrast agent microbubble as the center, the intensity distribution profile curve of the point spread function region of each ultrasound contrast agent microbubble is extracted along the vertical direction. Multiply the peak intensity on the intensity distribution profile curve by a coefficient of 0.5 to obtain the corresponding half-peak intensity threshold; For each intensity distribution profile curve, search from the peak position of the intensity distribution profile curve to both sides to determine the first pixel point and the second pixel point whose intensity value is equal to the half-peak intensity threshold; Within the neighborhood of the first pixel and the second pixel, a cubic spline interpolation algorithm is used to reconstruct the data and calculate the first sub-pixel and the second sub-pixel whose intensity value is equal to the half-intensity threshold. The full width at half maximum (WHM) of the point diffusion function region of each ultrasound contrast agent microbubble is calculated based on the first subpixel and the second subpixel.

[0014] In one implementation of the first aspect, the method further includes: For each of the ultrasound contrast agent microbubbles in two adjacent grayscale images, pair them up and obtain the inter-frame displacement of each ultrasound contrast agent microbubble. ; Based on the following formula and inter-frame time interval The velocity of each ultrasound contrast agent microbubble was obtained. : ; The movement speed of each of the ultrasound contrast agent microbubbles Assign the corresponding three-dimensional coordinate points The three-dimensional blood flow velocity map of the target area is obtained.

[0015] A second aspect of this application provides a three-dimensional ultrasound imaging system, comprising: The transmitting module is used to transmit a multi-angle composite plane wave sequence to the target area through a one-dimensional ultrasonic array with frequency gradient characteristics in the elevation direction; The receiving module is used to receive the echo signal reflected by the target area based on the multi-angle composite plane wave sequence within a preset time window, and obtain the echo signal sequence. The image synthesis module is used to generate a target grayscale image sequence corresponding to the echo signal sequence through beamforming; The two-dimensional coordinate module is used to obtain the coordinates of each ultrasound contrast agent microbubble in each frame of grayscale image in the target grayscale image sequence, and generate a two-dimensional coordinate set. The axial half-peak full width calculation module is used to calculate the axial half-peak full width of the point diffusion function region of each of the ultrasound contrast agent microbubbles; The elevation coordinate module is used to determine the coordinates of each ultrasound contrast agent microbubble in the elevation direction based on the axial full width at half maximum and the pre-established full width at half maximum to elevation coordinate mapping relationship, and to generate an elevation coordinate set. A three-dimensional angiography module is used to generate a three-dimensional angiography image of the target area based on the two-dimensional coordinate set and the elevation coordinate set.

[0016] A third aspect of this application provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the three-dimensional ultrasound imaging method as described in the first aspect.

[0017] A fourth aspect of this application provides a computer program product including a computer program that, when run, causes the three-dimensional ultrasound imaging method as described in the first aspect to be executed.

[0018] The beneficial effect of the first aspect of the embodiments of this application is as follows: by using a one-dimensional ultrasound array with frequency gradient characteristics in the elevation direction to emit a multi-angle composite plane wave sequence to the target area, receiving the echo signal reflected by the target area based on the multi-angle composite plane wave sequence, obtaining the echo signal sequence, and then generating the target grayscale image sequence corresponding to the echo signal sequence through beamforming, obtaining the coordinates of each ultrasound contrast agent microbubble in each frame of the target grayscale image sequence, and generating a two-dimensional coordinate set, then calculating the axial half-peak full width of the point spread function region of each ultrasound contrast agent microbubble, and determining the coordinates of each ultrasound contrast agent microbubble in the elevation direction based on the axial half-peak full width and the pre-established half-peak full width-elevation coordinate mapping relationship, and generating an elevation coordinate set, finally generating a three-dimensional contrast image of the target area based on the two-dimensional coordinate set and the elevation coordinate set, solving the problem that three-dimensional ultrasound imaging is limited by planar ultrasound arrays, and realizing simpler and more efficient three-dimensional ultrasound imaging at low cost based on a one-dimensional ultrasound array.

[0019] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram illustrating the implementation process of the three-dimensional imaging method provided in the embodiments of this application; Figure 1-a This is a schematic diagram of a one-dimensional ultrasonic array provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the implementation process of the three-dimensional imaging method provided in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the implementation process of the three-dimensional imaging method provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the three-dimensional imaging system provided in the embodiments of this application; Figure 5 This is a schematic diagram of the computer device provided in an embodiment of this application; Figure 6 This is a schematic diagram of a computer program product provided in an embodiment of this application. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0023] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0026] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] This application provides a three-dimensional ultrasound imaging method to overcome the limitation of three-dimensional ultrasound imaging by planar ultrasound arrays, achieving low-cost, simple, and efficient three-dimensional ultrasound imaging based on a one-dimensional ultrasound array. The three-dimensional ultrasound imaging method provided in this application uses a one-dimensional ultrasound array with frequency gradient characteristics in the elevation direction to emit a multi-angle composite plane wave sequence to a target area, receives the echo signal reflected from the target area based on the multi-angle composite plane wave sequence, obtains the echo signal sequence, and then generates a target grayscale image sequence corresponding to the echo signal sequence through beamforming. The coordinates of each ultrasound contrast agent microbubble in each frame of the target grayscale image sequence are obtained and a two-dimensional coordinate set is generated. Then, the axial half-peak full width at half-maximum (HWHM) of the point spread function region of each ultrasound contrast agent microbubble is calculated, and the coordinates of each ultrasound contrast agent microbubble in the elevation direction are determined based on the axial HWHM and a pre-established HWHM-elevation coordinate mapping relationship, generating an elevation coordinate set. Finally, a three-dimensional contrast image of the target area is generated based on the two-dimensional coordinate set and the elevation coordinate set. This solves the problem of three-dimensional ultrasound imaging being limited by planar ultrasound arrays, achieving low-cost, simple, and efficient three-dimensional ultrasound imaging based on a one-dimensional ultrasound array.

[0029] The three-dimensional ultrasound imaging method provided in this application embodiment can be applied to computer devices such as desktop computers, servers, laptops, mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). This application embodiment does not impose any restrictions on the specific type of computer device.

[0030] like Figure 1 As shown, this application provides a three-dimensional ultrasound imaging method, including: Step S1: A multi-angle composite plane wave sequence is emitted toward the target area through a one-dimensional ultrasonic array with frequency gradient characteristics in the elevation direction.

[0031] like Figure 1-a As shown, in the application, the array elements of the one-dimensional ultrasonic array used in this application have a gradually varying thickness along the elevation direction, decreasing from h2 to h1, and the center frequency of its emitted sound field gradually changes along the elevation direction from h2 to h1. f 2 changes to f 1. This design ensures the center frequency of the excited sound field. f c It exhibits a gradual change in frequency along the elevation angle. Taking a one-dimensional ultrasonic array based on PZT piezoelectric ceramics as an example, the center frequency f of the sound field excited by the ultrasonic array elements... c The thickness h of the array element satisfies the following relationship: ,in N It is the frequency constant of PZT material.

[0032] In the ultrasound imaging coordinate system, the elevation direction (Y-axis) is the third dimension perpendicular to the azimuth-depth plane, specifically defined as follows: Azimuth (X-axis): The direction of array element arrangement (e.g., the horizontal direction of a linear probe); Depth (Z-axis): The direction of ultrasound propagation (the direction from the probe into the body); Elevation angle (Y-axis): The direction perpendicular to the XZ plane (such as the "thickness direction" of a linear probe, i.e., the direction perpendicular to the upper and lower edges of the probe).

[0033] In applications, a one-dimensional ultrasonic array with frequency gradient characteristics along the elevation angle refers to an array whose emitted ultrasonic waves exhibit a "regular gradient change in center frequency" along the elevation angle (Y-axis). This means that different positions along the elevation angle correspond to different sound field center frequencies, and the relationship between the frequency and the elevation angle coordinates is "known, fixed, and calibrable." Assuming the elevation angle coverage of the one-dimensional ultrasonic array is "Y=0mm (lower edge) ~ Y=2mm (upper edge)," its frequency gradient can be designed as a "linear increase": when the elevation angle coordinate Y=0mm, the sound field center frequency... f 0 = 5MHz; When Y = 1 mm, f 0 = 6MHz; When Y = 2mm, f 0 = 7MHz; Along the Y-axis from 0mm to 2mm, the frequency increases uniformly from 5MHz to 7MHz, forming a one-to-one correspondence between "elevation angle and frequency" (which can be represented as...). f 0 = Y + 5 (unit: MHz, mm).

[0034] In the application, the blood vessels in the target area contain sparsely distributed ultrasound contrast agent microbubbles.

[0035] Step S2: Receive the echo signal reflected by the target area based on the multi-angle composite plane wave sequence to obtain the echo signal sequence.

[0036] In the application, the echo signal reflected from the target area based on the multi-angle composite plane wave sequence is received within a preset time window to obtain the echo signal sequence.

[0037] Step S3: Generate the target grayscale image sequence corresponding to the echo signal sequence through beamforming; In applications, multiple elements of an ultrasonic array receive echo signals from different angles (because it emits multi-angle plane waves). These raw echo signals have spatial angle differences and time delay differences, which can lead to image blurring if used directly. The essence of beamforming is to "calibrate, align, and superimpose" these "scattered and misaligned" signals through algorithms, and finally focus them to form a clear spatial image.

[0038] Step S4: Obtain the coordinates of each ultrasound contrast agent microbubble in each frame of the target grayscale image sequence, and generate a two-dimensional coordinate set; Step S5: Calculate the full width at half maximum (WHM) of the point diffusion function region for each of the ultrasound contrast agent microbubbles.

[0039] In applications, microbubbles are not ideal "points" in ultrasound images, but rather "diffuse spots" formed due to the resolution limitations of the imaging system, i.e., point spread function (PSF). The axial half-peak full width at half-maximum (HWHM) is a key parameter describing the "axial (depth direction, Z-axis) width" of this spot. It is the distance (unit: physical length, e.g., mm) from the "left point where the signal intensity is half the peak value" to the "right point" on the axial cross-section (along the Z-axis) of the PSF. The smaller the HWHM, the "sharper" the spot, indicating a higher axial resolution of the imaging system at that location; conversely, the larger the HWHM, the lower the resolution.

[0040] Step S6: Based on the axial full width at half maximum (FWHM) and the pre-established full width at half maximum (FWHM)-elevation coordinate mapping relationship, determine the coordinates of each ultrasound contrast agent microbubble in the elevation direction and generate an elevation coordinate set.

[0041] In application, based on the gradual change characteristics of the center frequency of the one-dimensional ultrasonic array along the elevation angle and the quantitative relationship between the axial full width at half maximum (FWHM) and the center frequency, a mapping relationship between FWHM and elevation angle is established. Specifically, this is achieved through the FWHM of the point spread function. Determine the center frequency of the sound field in this region: ; The center frequency of the sound field is determined by the thickness of the array elements and varies linearly along the elevation angle. ,in, The speed of sound in the biological tissue of the target region. For bandwidth coefficient, N is the frequency constant of the PZT material, h is the thickness of each array element, and Y is the elevation coordinate. The center frequency of the microbubble is excited at the lower edge (Y=0mm) of the elevation angle coverage of the one-dimensional ultrasound array. Therefore, we can determine the elevation angle coordinate (y) of the microbubble based on the above frequency information.

[0042] It is understandable that a multi-angle composite plane wave actually represents a plane wave sound field emitted from multiple angles, and the sound field has center frequency information. The one-dimensional ultrasonic array provided in this application has a center frequency of emitted sound field that gradually changes linearly along the elevation angle, i.e., from [f1, f2], where f0 belongs to the interval [f1, f2].

[0043] Step S7: Generate a three-dimensional imaging image of the target region based on the two-dimensional coordinate set and the elevation coordinate set.

[0044] In applications, the three-dimensional coordinates of ultrasound contrast agent microbubbles can be obtained based on two-dimensional coordinate sets and elevation coordinate sets. These microbubbles are injected intravenously into the bloodstream and, due to their size (1-5 μm) similar to red blood cells, uniformly fill microvessels of all levels (from arteries to capillaries) with the blood flow. Therefore, the three-dimensional coordinate set of microbubbles is essentially a "discrete sampling point of the vascular lumen." A sufficient number of microbubble coordinates can cover the spatial distribution of blood vessels, providing a data foundation for reconstructing three-dimensional angiography images.

[0045] The data from the 3D coordinate set undergoes preprocessing, including outlier extraction and coordinate calibration. Then, the preprocessed 3D coordinates of all microbubbles are stacked according to their actual physical spatial locations to form a 3D point cloud model of the blood vessel. Each point represents the location of a microbubble within the vessel, and the point density is positively correlated with the microbubble concentration within the vessel (points are denser in the central region and sparser at the edges). "3D skeleton extraction" or "vessel wall contour fitting" is employed. For example, for vessel cross-sections (XY plane) at different depths (Z-axis), "circle fitting" or "ellipse fitting" is used to obtain the vessel center and radius for each cross-section. Connecting the centers of each cross-section along the depth direction (Z-axis) forms the vessel's "central skeleton." Combining the radii of each cross-section generates the vessel's "3D tubular structure" (such as a cylinder or elliptical cylinder). The final result is a "continuous, smooth 3D angiography image" that clearly displays the branches, direction, and diameter changes of the blood vessel (resolution reaches subwavelength levels, far exceeding traditional ultrasound).

[0046] The three-dimensional ultrasound imaging method provided in this application can achieve three-dimensional ultrasound imaging based on a one-dimensional ultrasound array. Compared with traditional super-resolution ultrasound imaging technology based on one-dimensional ultrasound arrays, this invention can achieve three-dimensional super-resolution angiography of microvessels, providing richer vascular information. Compared with traditional three-dimensional super-resolution ultrasound imaging technology based on area array ultrasound arrays, it can achieve three-dimensional super-resolution angiography based on a one-dimensional array with fewer array elements, significantly reducing hardware costs, hardware system complexity, and the number of channels required. Compared with traditional three-dimensional super-resolution ultrasound imaging technology based on area array ultrasound arrays, this invention can reconstruct three-dimensional super-resolution angiography images using two-dimensional planar data, greatly reducing the amount of data required for three-dimensional super-resolution imaging, and reducing data processing complexity and computation time.

[0047] like Figure 2 As shown, in one embodiment, step S3, generating the target grayscale image sequence corresponding to the echo signal sequence through beamforming, includes: Step S31: Perform signal preprocessing on each echo signal in the echo signal sequence to obtain a preprocessed echo signal sequence. The signal preprocessing includes Hilbert transform, quadrature demodulation, and downsampling.

[0048] In applications, each echo signal in the echo signal sequence undergoes Hilbert transform, quadrature demodulation, and downsampling sequentially. The aim is to convert high-frequency, complex radio frequency signals into low-frequency, easily processed baseband signals while preserving key information. Hilbert transform: Converts real-valued echo signals (RF signals, including carrier frequency) into complex-valued analytic signals, extracting the envelope information (reflecting echo intensity) and phase information (used for subsequent phase alignment).

[0049] Quadrature demodulation: Removes the carrier frequency and downconverts the signal from the radio frequency band (such as a few MHz to tens of MHz) to the baseband (near 0 frequency), obtaining two signals, I (in-phase) and Q (quadrature), which simplifies the computational workload of subsequent processing.

[0050] Downsampling: Reduce the sampling rate of the signal (e.g., from 100MHz to 20MHz) without losing key information, reduce the amount of data, and avoid computational overload during subsequent processing.

[0051] Step S32: Delay compensation is performed on each echo signal in the preprocessed echo signal sequence to obtain the target echo signal sequence.

[0052] In applications, because ultrasonic arrays emit plane waves from multiple angles (such as 0°, 5°, 10°, etc.), and the distances from different array elements to the imaging point vary, the arrival times of the echo signals at each array element differ (i.e., "delay"). If these delays are not compensated, the superimposed signals will cancel each other out or become blurred due to "asynchrony." The core of delay compensation is to calculate the theoretical delay of each imaging point at different transmission / reception angles and to perform time offset calibration on the signals. For each pixel in the image (spatial coordinates (x,z)), the "theoretical propagation time" of the signal after reflection from that point to each array element is calculated based on its distance from each array element, the speed of sound propagation (approximately 1540 m / s in biological tissue), and the emission angle. For the echo signal received by each array element, a "time offset" (advanced or delayed) is applied based on the above theoretical time to ensure that the signals received by all array elements from the same pixel are "time-aligned".

[0053] Step S33: Based on the target echo signal sequence, coherent superposition is performed to obtain an initial grayscale image sequence.

[0054] In applications, echo signals from the target echo signal sequence are superimposed, utilizing the principle of "coherent signal enhancement and incoherent noise cancellation" to improve the signal-to-noise ratio and resolution of the image. Coherent superposition refers to the phenomenon where echo signals from the same pixel, after being time-aligned, have the same phase, and the amplitude (intensity) is linearly enhanced after superposition (e.g., the intensity of N superimposed signals becomes N times). Noise suppression refers to the phenomenon where random noise has disordered phase, and after superposition, they cancel each other out, resulting in only a reduction in intensity. This is a multiple (far lower than the signal enhancement amplitude). After superposition, the intensity (grayscale value) of each pixel corresponds to the echo signal energy at that location: the stronger the reflection (such as ultrasound contrast agent microbubbles or dense tissue), the higher the grayscale value (the brighter the image); the weaker the reflection (such as liquid), the lower the grayscale value (the darker the image), ultimately forming a grayscale image sequence (the "sequence" generates multiple frames corresponding to different angles due to multi-angle emission).

[0055] Step S34: Use the initial grayscale image sequence as the target grayscale image sequence.

[0056] In one embodiment, after using the initial grayscale image sequence as the target grayscale image sequence, step S34 further includes: Step S35: The initial grayscale image sequence is filtered using a spatiotemporal filter based on singular value decomposition to obtain a filtered grayscale image sequence.

[0057] In application, static tissue signals and random noise signals in the initial grayscale image sequence are removed by a filter, while the dynamic signals of the ultrasound contrast agent microbubbles are preserved, enhancing the dynamic information in the grayscale image sequence and providing a clear image basis for subsequent "microbubble coordinate detection".

[0058] Step S36: Use the filtered grayscale image sequence as the target grayscale image sequence.

[0059] In one embodiment, step S35, which involves filtering the initial grayscale image sequence using a spatiotemporal filter based on singular value decomposition to obtain a filtered grayscale image sequence, includes: Step S351, convert the grayscale image sequence Convert to a two-dimensional spatiotemporal sequence matrix , where the matrix The size of the first dimension is The size of the second dimension is ,in, The target grayscale image sequence are respectively The number of spatial samples in the x-axis, the number of spatial samples in the z-axis, and the number of temporal samples.

[0060] In applications, two-dimensional spatiotemporal sequence matrices It conforms to the Cascolatian matrix form.

[0061] Step S352, for the matrix Perform singular value decomposition to obtain the corresponding spatial singular matrix. Time Singular Matrix Non-square diagonal matrix The matrix and the matrix Each column is the matrix Spatial and temporal singular vectors, the matrix Each element is the matrix The singular values ​​of the matrix, and the matrix and Satisfying Relationship: .

[0062] Step S353, the matrix Decomposed into multiple separable matrices An ordered weighted sum, where each matrix It can be represented as the tensor product of two vector matrices:

[0063] in, and The first and second parts of matrix U and matrix V are respectively the first and second parts of matrix V. List, For the first A singular value.

[0064] Step S354: By calculating the first derivative of the singular value curve, identify the starting point where the singular value curve becomes flat, and determine the optimal cutoff threshold. .

[0065] Filtered grayscale image sequence The following representation is made:

[0066] in, Let S be the rank of matrix S.

[0067] After the above singular value decomposition spatiotemporal filtering, the grayscale image sequence will exhibit a significant "dynamic enhancement" effect, specifically manifested as follows: Static tissue signals are suppressed: the singular values ​​of the first few levels corresponding to the tissue are removed, and the brightness of large areas of "static tissue background" (such as muscle and fat) in the image is significantly reduced, even approaching black. Random noise is eliminated: the singular values ​​corresponding to noise in the later orders are removed, the irregular "snowflake-like noise" in the image disappears, and the overall clarity is improved; The dynamic signal of microbubbles is highlighted: the singular values ​​of microbubbles corresponding to intermediate order are preserved, and sparsely distributed microbubbles present "continuous dynamic trajectories" (such as bright spots moving with blood flow) in multiple frames of images, providing a clear signal source for subsequent "microbubble coordinate detection" and "motion velocity calculation".

[0068] like Figure 3 As shown, in one embodiment, step S4, obtaining the coordinates of each ultrasound contrast agent microbubble in each frame of the target grayscale image sequence and generating a two-dimensional coordinate set, includes: Step S41: Upsample each frame of grayscale image in the target grayscale image sequence.

[0069] In applications, upsampling by a factor of 10 or greater is used when the pixel spacing of the target grayscale image is large (e.g., 100 μm / pixel). Direct centroid detection would be limited by the "pixel grid" and unable to break through the original resolution. 10x interpolation upsampling, through algorithms (such as bilinear interpolation or cubic spline interpolation), inserts new pixels between existing pixels, increasing the image resolution by 10 times (e.g., to 10 μm / pixel). This is equivalent to "magnifying the light spot 10 times," making the details of the light spot (such as grayscale gradient changes) clearer, providing a data foundation for subsequent "sub-pixel-level centroid calculation." For example, a grayscale image with 256*256 pixels and each pixel size of 50 μm*50 μm; after 10x interpolation, the number of pixels in the grayscale image becomes 2560*2560, making each pixel size 5 μm*5 μm, providing a more refined data foundation.

[0070] Step S42: Perform cross-correlation calculation between the upsampled grayscale image of each frame and the standard ultrasound image of the ultrasound contrast agent microbubble to obtain the corresponding correlation coefficient spectrum.

[0071] In applications, the point spread function (PSF) of ultrasound contrast agent microbubbles has a specific shape (such as an approximately Gaussian distributed spot). Through cross-correlation calculations, regions similar to standard microbubble images can be quickly found in the image. A standard ultrasound image of the microbubble (i.e., the PSF template of the microbubble under ideal conditions, which can be generated through experimental measurement or simulation) is pre-acquired. The template is then slid pixel by pixel on the upsampled grayscale image, and the cross-correlation coefficient at each location is calculated (the value ranges from -1 to 1, with values ​​closer to 1 indicating a higher similarity between the region and the template). The correlation coefficient values ​​of all locations are combined to form a new image (correlation coefficient spectrum). High-value regions (bright areas) in the spectrum are the suspected locations of ultrasound contrast agent microbubbles.

[0072] Step S43: The pixel regions in each correlation coefficient spectrum whose correlation values ​​are lower than the preset correlation coefficient threshold are zeroed out.

[0073] In applications, in addition to the high-correlation regions of real microbubbles, the correlation coefficient map may also contain low-correlation regions (with lower correlation coefficients) caused by residual tissue signals and random noise. By setting a correlation coefficient threshold (e.g., 0.7, which can be calibrated experimentally), all pixel values ​​below this threshold in the map are set to 0 (zeroed out), retaining only the high-correlation regions. This significantly reduces interference from non-microbubble regions, leaving only a small number of candidate regions highly similar to the microbubble template in the correlation coefficient map, simplifying subsequent analysis.

[0074] Step S44: Perform connected component analysis on each correlation coefficient graph after zeroing to obtain the area of ​​each connected component in each correlation coefficient graph after zeroing.

[0075] In applications, in the correlation coefficient map after zeroing, the high-correlation regions of real microbubbles are usually continuous connected pixel groups (because the microbubble PSF is a continuous light spot), while noise may appear as isolated discrete pixels. Connected component analysis uses algorithms (such as 8-neighborhood search) to identify all interconnected pixel regions (connected components) in the map and counts the area (number of pixels) of each connected component.

[0076] Step S45: Identify connected regions with an area greater than a preset area threshold and retain them as target connected regions. Each target connected region corresponds to a point diffusion function region of the ultrasound contrast agent microbubble.

[0077] In applications, the PSF of microbubbles has a stable spatial scale (area within a certain range), while noise typically has a smaller and unstable area, which can be further distinguished by area. Based on the physical size of the microbubbles and the characteristics of the imaging system, a preset area threshold is established (e.g., minimum area = 5 pixels, maximum area = 50 pixels, to be determined experimentally). Connected regions with areas smaller than the area threshold (mostly noise or artifacts) are removed, and connected regions with areas larger than the preset area threshold are retained as target connected regions. Each target connected region corresponds to the point spread function region of a microbubble.

[0078] Step S46: Extract the centroid coordinates of the target connected domain, where each centroid coordinate corresponds to a two-dimensional coordinate of the ultrasound contrast agent microbubble.

[0079] In the application, for each target connected region (microbubble PSF region), the built-in centroid detection algorithm of the programming software (such as MATLAB, Python, and OpenCV) is invoked to calculate its centroid coordinates through "grayscale weighted averaging". The centroid coordinates take into account the grayscale contribution of all pixels in the region (the higher the grayscale of the pixel, the greater the "weight" of the center position), and the accuracy can reach the sub-pixel level (breaking through the pixel limit of the original image), meeting the positioning accuracy requirements of super-resolution imaging.

[0080] Step S47: Generate a two-dimensional coordinate set based on the centroid coordinates.

[0081] In applications, a two-dimensional coordinate set includes multiple two-dimensional coordinate subsets. A subset of two-dimensional coordinates includes the centroid coordinates of all microbubbles in a frame of grayscale image. The centroid coordinates of all microbubbles in different frames of grayscale image are in different two-dimensional coordinate subsets.

[0082] In one embodiment, step S5, calculating the axial full width at half maximum (FWHM) of the point diffusion function region for each ultrasound contrast agent microbubble, includes: With the centroid of the point spread function region of each ultrasound contrast agent microbubble as the center, the intensity distribution profile curve of the point spread function region of each ultrasound contrast agent microbubble is extracted along the vertical direction. Multiply the peak intensity on the intensity distribution profile curve by a coefficient of 0.5 to obtain the corresponding half-peak intensity threshold; For each intensity distribution profile curve, search from the peak position of the intensity distribution profile curve to both sides to determine the first pixel point and the second pixel point whose intensity value is equal to the half-peak intensity threshold; Within the neighborhood of the first pixel and the second pixel, a cubic spline interpolation algorithm is used to reconstruct the data and calculate the first sub-pixel and the second sub-pixel whose intensity value is equal to the half-intensity threshold. The full width at half maximum (WHM) of the point diffusion function region of each ultrasound contrast agent microbubble is calculated based on the first subpixel and the second subpixel.

[0083] In one embodiment, the method further includes: Step S81: Pair each of the ultrasound contrast agent microbubbles in two adjacent grayscale images to obtain the inter-frame displacement of each ultrasound contrast agent microbubble. .

[0084] In application, pairing each of the ultrasound contrast agent microbubbles in two adjacent grayscale images includes: Calculate the set of distances between the j0th ultrasound contrast agent microbubble in the i-th grayscale image and each of the ultrasound contrast agent microbubbles in the (i+1)-th grayscale image. , where k is the index of the kth ultrasound contrast agent microbubble in the (i+1)th grayscale image; Identify the distance set minimum value Determine the index k0 of the microbubble contrast agent corresponding to the minimum value; Calculate the set of distances between the k0th ultrasound contrast agent microbubble in the (i+1)th grayscale image and each of the ultrasound contrast agent microbubbles in the i-th grayscale image. , where j is the index of the j-th ultrasound contrast agent microbubble in the i-th grayscale image; Identify the distance set minimum value Determine the index j1 of the microbubble contrast agent corresponding to the minimum value; If j 0= j1 If the j0th ultrasound contrast agent microbubble in the i-th grayscale image and the k0th ultrasound contrast agent microbubble in the (i+1)-th grayscale image are the microbubbles with the minimum distance from each other, they are considered to be successfully paired; otherwise, the j0th ultrasound contrast agent microbubble is removed.

[0085] Step S82, according to the following formula and inter-frame time interval The velocity of each ultrasound contrast agent microbubble was obtained. : ; Step S83, the movement speed of each ultrasound contrast agent microbubble is... Assign the corresponding three-dimensional coordinate points The three-dimensional blood flow velocity map of the target area is obtained.

[0086] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0087] This application also provides a three-dimensional ultrasound imaging system for performing the steps described in the three-dimensional ultrasound imaging method embodiments above. The three-dimensional ultrasound imaging system can be a virtual appliance in a computer device, run by the computer device's processor, or it can be the computer device itself.

[0088] like Figure 4 As shown, this application embodiment provides a three-dimensional ultrasound imaging system 40, including: Transmission module 401 is used to transmit a multi-angle composite plane wave sequence toward a target area through a one-dimensional ultrasonic array with frequency gradient characteristics in the elevation direction; The receiving module 402 is used to receive the echo signal reflected by the target area based on the multi-angle composite plane wave sequence within a preset time window, and obtain the echo signal sequence. Image synthesis module 403 is used to generate a target grayscale image sequence corresponding to the echo signal sequence through beamforming; The two-dimensional coordinate module 404 is used to obtain the coordinates of each ultrasound contrast agent microbubble in each frame of grayscale image in the target grayscale image sequence, and generate a two-dimensional coordinate set. The axial half-peak full width calculation module 405 is used to calculate the axial half-peak full width of the point diffusion function region of each of the ultrasound contrast agent microbubbles; The elevation coordinate module 406 is used to determine the coordinates of each ultrasound contrast agent microbubble in the elevation direction based on the axial full width at half maximum and the pre-established full width at half maximum to elevation coordinate mapping relationship, and to generate an elevation coordinate set. The three-dimensional contrast imaging module 407 is used to generate a three-dimensional contrast image of the target area based on the two-dimensional coordinate set and the elevation coordinate set.

[0089] In one embodiment, the image synthesis module 403 is used for: Each echo signal in the echo signal sequence is preprocessed to obtain a preprocessed echo signal sequence. The signal preprocessing includes Hilbert transform, quadrature demodulation, and downsampling. Delay compensation is performed on each echo signal in the preprocessed echo signal sequence to obtain the target echo signal sequence. An initial grayscale image sequence is obtained by coherently superimposing the target echo signal sequence; The initial grayscale image sequence is used as the target grayscale image sequence.

[0090] In one embodiment, the image synthesis module 403 is further configured to: The initial grayscale image sequence is filtered using a spatiotemporal filter based on singular value decomposition to obtain a filtered grayscale image sequence. The filtered grayscale image sequence is used as the target grayscale image sequence.

[0091] In one embodiment, the image synthesis module 403 is used for: grayscale image sequence Convert to a two-dimensional spatiotemporal sequence matrix , where the matrix The size of the first dimension is The size of the second dimension is ,in, The target grayscale image sequence are respectively The number of spatial samples in the x-axis, the number of spatial samples in the z-axis, and the number of temporal samples; For the matrix Perform singular value decomposition to obtain the corresponding spatial singular matrix. Time Singular Matrix Non-square diagonal matrix The matrix and the matrix Each column is the matrix Spatial and temporal singular vectors, the matrix Each element is the matrix The singular values ​​of the matrix, and the matrix and Satisfying Relationship: ; The matrix Decomposed into multiple separable matrices An ordered weighted sum, where each matrix It can be represented as the tensor product of two vector matrices:

[0092] in, and The first and second parts of matrix U and matrix V are respectively the first and second parts of matrix V. List, For the first One singular value; By calculating the first derivative of the singular value curve, the starting point where the singular value curve becomes flat can be identified, and the optimal cutoff threshold can be determined. ; Filtered grayscale image sequence The following representation is made:

[0093] in, Let S be the rank of matrix S.

[0094] In one embodiment, the two-dimensional coordinate module 404 is used for: Upsampling is performed on each frame of grayscale image in the target grayscale image sequence; The grayscale image of each frame that has been upsampled is cross-correlated with the standard ultrasound image of the ultrasound contrast agent microbubble to obtain the corresponding correlation coefficient spectrum. In each correlation coefficient map, pixel regions with correlation values ​​lower than a preset correlation coefficient threshold are zeroed out. Connectivity analysis is performed on each correlation coefficient graph after zeroing to obtain the area of ​​each connected region in each correlation coefficient graph after zeroing. Connected regions with an area greater than a preset area threshold are identified and retained as target connected regions, and each target connected region corresponds to a point diffusion function region of the ultrasound contrast agent microbubble; Extract the centroid coordinates of the target connected domain, and each centroid coordinate corresponds to a two-dimensional coordinate of the ultrasound contrast agent microbubble; A two-dimensional coordinate set is generated based on the centroid coordinates.

[0095] In one embodiment, the axial half-peak full width calculation module 405 is used for: The full width at half maximum (FWHM) of the point spread function region for each ultrasound contrast agent microbubble is calculated using the following formula. : With the centroid of the point spread function region of each ultrasound contrast agent microbubble as the center, the intensity distribution profile curve of the point spread function region of each ultrasound contrast agent microbubble is extracted along the vertical direction. Multiply the peak intensity on the intensity distribution profile curve by a coefficient of 0.5 to obtain the corresponding half-peak intensity threshold; For each intensity distribution profile curve, search from the peak position of the intensity distribution profile curve to both sides to determine the first pixel point and the second pixel point whose intensity value is equal to the half-peak intensity threshold; Within the neighborhood of the first pixel and the second pixel, a cubic spline interpolation algorithm is used to reconstruct the data and calculate the first sub-pixel and the second sub-pixel whose intensity value is equal to the half-intensity threshold. The full width at half maximum (WHM) of the point diffusion function region of each ultrasound contrast agent microbubble is calculated based on the first subpixel and the second subpixel.

[0096] In one embodiment, the method further includes a three-dimensional blood flow velocity map reconstruction module 408, used for: For each of the ultrasound contrast agent microbubbles in two adjacent grayscale images, pair them up and obtain the inter-frame displacement of each ultrasound contrast agent microbubble. ; Based on the following formula and inter-frame time interval The velocity of each ultrasound contrast agent microbubble was obtained. : ; The movement speed of each of the ultrasound contrast agent microbubbles Assign the corresponding three-dimensional coordinate points The three-dimensional blood flow velocity map of the target area is obtained.

[0097] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application. For example... Figure 5 As shown, the computer device 5 of this embodiment includes: at least one processor 50 ( Figure 5 (Only one is shown in the diagram) a processor, a memory 51, and a computer program 52 stored in the memory 51 and executable on the at least one processor 50, wherein the processor 50 executes the computer program 52 to implement the steps in any of the above-described embodiments of the three-dimensional ultrasound imaging methods.

[0098] The computer device may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will understand that... Figure 5The computer device 5 is merely an example and does not constitute a limitation on the computer device 5. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0099] The processor 50 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0100] In some embodiments, the memory 51 may be an internal storage unit of the computer device 5, such as a hard disk or memory of the computer device 5. In other embodiments, the memory 51 may be an external storage device of the computer device 5, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 5. Furthermore, the memory 51 may include both internal and external storage units of the computer device 5. The memory 51 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 51 can also be used to temporarily store data that has been output or will be output.

[0101] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0102] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0103] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0104] like Figure 6 As shown, an embodiment of this application provides a computer program product 6, including a computer program 52, which, when run, causes the three-dimensional ultrasound imaging method described above to be executed.

[0105] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0107] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] In the embodiments provided in this application, it should be understood that the disclosed computer devices and methods can be implemented in other ways. For example, the computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0109] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A three-dimensional ultrasound imaging method, characterized in that, include: A multi-angle composite plane wave sequence is emitted toward the target area using a one-dimensional ultrasonic array with frequency gradient characteristics in the elevation direction; Receive the echo signal reflected from the target area based on the multi-angle composite plane wave sequence to obtain the echo signal sequence; The target grayscale image sequence corresponding to the echo signal sequence is generated by beamforming; The coordinates of each ultrasound contrast agent microbubble in each frame of the target grayscale image sequence are obtained, and a two-dimensional coordinate set is generated. Calculate the full width at half maximum (FWHM) of the point diffusion function region for each of the ultrasound contrast agent microbubbles; Based on the axial full width at half maximum (FWHM) and the pre-established full width at half maximum (FWHM) - elevation coordinate mapping relationship, the coordinates of each ultrasound contrast agent microbubble in the elevation direction are determined and an elevation coordinate set is generated. A three-dimensional angiographic image of the target region is generated based on the two-dimensional coordinate set and the elevation coordinate set.

2. The three-dimensional ultrasound imaging method as described in claim 1, characterized in that, The step of generating the target grayscale image sequence corresponding to the echo signal sequence through beamforming includes: Each echo signal in the echo signal sequence is preprocessed to obtain a preprocessed echo signal sequence. The signal preprocessing includes Hilbert transform, quadrature demodulation, and downsampling. Delay compensation is performed on each echo signal in the preprocessed echo signal sequence to obtain the target echo signal sequence. An initial grayscale image sequence is obtained by coherently superimposing the target echo signal sequence; The initial grayscale image sequence is used as the target grayscale image sequence.

3. The three-dimensional ultrasound imaging method as described in claim 2, characterized in that, After using the initial grayscale image sequence as the target grayscale image sequence, the method further includes: The initial grayscale image sequence is filtered using a spatiotemporal filter based on singular value decomposition to obtain a filtered grayscale image sequence. The filtered grayscale image sequence is used as the target grayscale image sequence.

4. The three-dimensional ultrasound imaging method as described in claim 3, characterized in that, The step of filtering the initial grayscale image sequence using a spatiotemporal filter based on singular value decomposition to obtain a filtered grayscale image sequence includes: grayscale image sequence Convert to a two-dimensional spatiotemporal sequence matrix , where the matrix The size of the first dimension is The size of the second dimension is ,in, The target grayscale image sequence are respectively The number of spatial samples in the x-axis, the number of spatial samples in the z-axis, and the number of temporal samples; For the matrix Perform singular value decomposition to obtain the corresponding spatial singular matrix. Time Singular Matrix Non-square diagonal matrix The matrix and the matrix Each column is the matrix Spatial and temporal singular vectors, the matrix Each element is the matrix The singular values ​​of the matrix, and the matrix and Satisfying Relationship: ; The matrix Decomposed into multiple separable matrices An ordered weighted sum, where each matrix It can be represented as the tensor product of two vector matrices: in, and The first and second parts of matrix U and matrix V are respectively the first and second parts of matrix V. List, For the first One singular value; By calculating the first derivative of the singular value curve, the starting point where the singular value curve becomes flat can be identified, and the optimal cutoff threshold can be determined. ; Filtered grayscale image sequence The following representation is made: in, Let S be the rank of matrix S.

5. The three-dimensional ultrasound imaging method as described in claim 1, characterized in that, The step of obtaining the coordinates of each ultrasound contrast agent microbubble in each frame of the target grayscale image sequence and generating a two-dimensional coordinate set includes: Upsampling is performed on each frame of grayscale image in the target grayscale image sequence; The grayscale image of each frame that has been upsampled is cross-correlated with the standard ultrasound image of the ultrasound contrast agent microbubble to obtain the corresponding correlation coefficient spectrum. In each correlation coefficient map, pixel regions with correlation values ​​lower than a preset correlation coefficient threshold are zeroed out. Connectivity analysis is performed on each correlation coefficient graph after zeroing to obtain the area of ​​each connected region in each correlation coefficient graph after zeroing. Connected regions with an area greater than a preset area threshold are identified and retained as target connected regions, and each target connected region corresponds to a point diffusion function region of the ultrasound contrast agent microbubble; Extract the centroid coordinates of the target connected domain, and each centroid coordinate corresponds to a two-dimensional coordinate of the ultrasound contrast agent microbubble; A two-dimensional coordinate set is generated based on the centroid coordinates.

6. The three-dimensional ultrasound imaging method as described in claim 1, characterized in that, The calculation of the axial half-peak full width at half maximum (FWHM) of the point spread function region for each of the ultrasound contrast agent microbubbles includes: With the centroid of the point spread function region of each ultrasound contrast agent microbubble as the center, the intensity distribution profile curve of the point spread function region of each ultrasound contrast agent microbubble is extracted along the vertical direction. Multiply the peak intensity on the intensity distribution profile curve by a coefficient of 0.5 to obtain the corresponding half-peak intensity threshold; For each intensity distribution profile curve, search from the peak position of the intensity distribution profile curve to both sides to determine the first pixel point and the second pixel point whose intensity value is equal to the half-peak intensity threshold; Within the neighborhood of the first pixel and the second pixel, a cubic spline interpolation algorithm is used to reconstruct the data and calculate the first sub-pixel and the second sub-pixel whose intensity value is equal to the half-intensity threshold. The full width at half maximum (WHM) of the point diffusion function region of each ultrasound contrast agent microbubble is calculated based on the first subpixel and the second subpixel.

7. The three-dimensional ultrasound imaging method according to any one of claims 1 to 6, characterized in that, The method further includes: For each of the ultrasound contrast agent microbubbles in two adjacent grayscale images, pair them up and obtain the inter-frame displacement of each ultrasound contrast agent microbubble. ; Based on the following formula and inter-frame time interval The velocity of each ultrasound contrast agent microbubble was obtained. : ； The movement speed of each of the ultrasound contrast agent microbubbles Assign the corresponding three-dimensional coordinate points The three-dimensional blood flow velocity map of the target area is obtained.

8. A three-dimensional ultrasound imaging system, characterized in that, include: The transmitting module is used to transmit a multi-angle composite plane wave sequence to the target area through a one-dimensional ultrasonic array with frequency gradient characteristics in the elevation direction; The receiving module is used to receive the echo signal reflected by the target area based on the multi-angle composite plane wave sequence within a preset time window, and obtain the echo signal sequence. The image synthesis module is used to generate a target grayscale image sequence corresponding to the echo signal sequence through beamforming; The two-dimensional coordinate module is used to obtain the coordinates of each ultrasound contrast agent microbubble in each frame of grayscale image in the target grayscale image sequence, and generate a two-dimensional coordinate set. The axial half-peak full width calculation module is used to calculate the axial half-peak full width of the point diffusion function region of each of the ultrasound contrast agent microbubbles; The elevation coordinate module is used to determine the coordinates of each ultrasound contrast agent microbubble in the elevation direction based on the axial full width at half maximum and the pre-established full width at half maximum to elevation coordinate mapping relationship, and to generate an elevation coordinate set. A three-dimensional angiography module is used to generate a three-dimensional angiography image of the target area based on the two-dimensional coordinate set and the elevation coordinate set.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the three-dimensional ultrasound imaging method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, Includes a computer program, which, when run, causes the three-dimensional ultrasound imaging method as described in any one of claims 1 to 7 to be performed.

Images