3D ultrasonic contrast perfusion multi-parameter functional imaging method and system

Abstract

The application discloses a 3D ultrasonic contrast perfusion multi-parameter functional imaging method and system, which comprises the following steps: firstly, acquiring ultrasonic contrast image data and a spatial position parameter matrix of a probe; acquiring a perfusion time intensity curve, perfusion parameters according to the ultrasonic contrast image data, and pseudo-color coding the values of the perfusion parameters to acquire a different spatial phase multi-parameter perfusion two-dimensional image set; performing three-dimensional space reconstruction on the different spatial phase multi-parameter perfusion two-dimensional image set and the spatial position parameter matrix to acquire a three-dimensional data matrix of ultrasonic contrast perfusion multi-parameters; and performing surface reconstruction and rendering on the three-dimensional data matrix of ultrasonic contrast perfusion multi-parameters to complete 3D ultrasonic contrast perfusion multi-parameter functional imaging. The application can greatly help clinicians to comprehensively master perfusion information of tissues or lesions and can quickly diagnose in specific clinical scenarios.

Description

Technical Field

[0001] This invention belongs to the field of medical ultrasound imaging technology and relates to a 3D ultrasound contrast perfusion multi-parameter functional imaging method and system. Background Technology

[0002] Compared to computed tomography (CT) and magnetic resonance imaging (MRI), ultrasound offers advantages such as high real-time performance, non-invasiveness, no radiation, portability, and low cost. Traditional two-dimensional ultrasound methods, lacking anatomical and directional information, cannot provide volumetric data of tissues and organs. In certain clinical scenarios, such as ultrasound-guided preoperative planning, intraoperative intervention, tumor biopsy, tumor thermal ablation guidance, and dynamic cardiac imaging, comprehensive ultrasound images are essential to provide rich anatomical information. Three-dimensional ultrasound images can comprehensively display complex anatomical structures and provide complete volumetric data of the imaging area. Not only can 3D ultrasound slices display internal anatomical structures, but volume rendering or surface rendering techniques can also be used for 3D visualization, enabling physicians to fully grasp the spatial anatomy of the target tissue, comprehensively determine the volume of lesions, and measure the relative positions between tissues. However, existing 3D ultrasound imaging systems suffer from limitations such as high cost, low positioning accuracy, and slow reconstruction speed, making it difficult to meet the needs of certain clinical and advanced imaging scenarios.

[0003] Contrast-enhanced ultrasound perfusion functional parametric imaging (CEPT) is a quantitative imaging technique for tissue perfusion based on contrast-enhanced ultrasound. It can quantitatively convert the temporal distribution of perfusion information, such as blood flow velocity and flow rate, into a spatial distribution display. It has been preliminarily applied to myocardial blood flow perfusion and differentiating between benign and malignant liver lesions, and has significant clinical value and significance for assessing the hemodynamic characteristics of blood vessels in various tissues. However, currently, this imaging technique can only display tissue vascular perfusion information in a single spatial section. For some tissues with complex vascular networks, such as myocardium, this technique cannot display the perfusion information of the vascular network in three dimensions. Two-dimensional contrast-enhanced ultrasound perfusion functional parametric imaging relies on the physician's experience to imagine and reconstruct the three-dimensional structure from a two-dimensional image. Therefore, the use of two-dimensional contrast-enhanced ultrasound perfusion functional parametric imaging is not convenient or intuitive enough, thus limiting its application. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a 3D ultrasound contrast perfusion multi-parameter functional imaging method and system, thereby solving the technical problems that the use of two-dimensional ultrasound contrast perfusion functional parametric imaging is not convenient or intuitive enough, and its use is limited.

[0005] This invention is achieved through the following technical solution:

[0006] A 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging method includes the following steps:

[0007] S1: Acquire ultrasound contrast imaging data and the spatial position parameter matrix of probe movement when acquiring the ultrasound contrast imaging data;

[0008] S2: Obtain the perfusion time-intensity curve based on the ultrasound contrast imaging data, obtain the perfusion parameters based on the perfusion time-intensity curve, and perform pseudo-color encoding on the values ​​of the perfusion parameters to obtain a set of two-dimensional perfusion images with multiple parameters in different spatial phases.

[0009] S3: Perform three-dimensional spatial reconstruction on the set of two-dimensional perfusion images with different spatial phases and the spatial position parameter matrix to obtain a three-dimensional data matrix of ultrasound contrast perfusion with multiple parameters;

[0010] S4: Perform surface reconstruction and rendering on the three-dimensional data matrix of the ultrasound contrast perfusion multi-parameter to complete 3D ultrasound contrast perfusion multi-parameter functional imaging.

[0011] Preferably, the ultrasound contrast imaging data is acquired using a one-dimensional or two-dimensional array probe.

[0012] Preferably, the ultrasound contrast imaging data is acquired through mechanical scanning or freehand handheld scanning.

[0013] Preferably, the ultrasound contrast imaging data includes ultrasound contrast imaging data under conditions of free breathing or cardiac physiological movement disturbance.

[0014] Preferably, the infusion parameters include time-related, intensity-related, and ratio-related infusion parameters.

[0015] Preferably, the time-related perfusion parameters include infusion time, infusion time, and peak time; the intensity-related perfusion parameters include peak value and area under the perfusion time-intensity curve; and the ratio-related perfusion parameters include infusion rate and infusion rate.

[0016] A 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging system includes:

[0017] Data acquisition module: The data acquisition module is used to acquire ultrasound contrast imaging data and the spatial position parameter matrix of probe movement when acquiring the ultrasound contrast imaging data;

[0018] First data processing module: The first data processing module is used to obtain perfusion time-intensity curves based on the ultrasound contrast imaging data, obtain perfusion parameters based on the perfusion time-intensity curves, and perform pseudo-color encoding on the values ​​of the perfusion parameters to obtain a set of two-dimensional perfusion images with multiple parameters in different spatial phases.

[0019] Second data processing module: The second data processing module is used to perform three-dimensional spatial reconstruction on the set of two-dimensional perfusion images with different spatial phases and spatial position parameter matrix to obtain a three-dimensional data matrix of ultrasound contrast perfusion with multiple parameters.

[0020] The third data processing module is used to perform surface reconstruction and rendering of the three-dimensional data matrix of the ultrasound contrast perfusion multi-parameter, and to complete the 3D ultrasound contrast perfusion multi-parameter functional imaging.

[0021] A computer device / apparatus / system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0022] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method.

[0023] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0024] Compared with the prior art, the present invention has the following beneficial technical effects:

[0025] This invention discloses a 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging method. The method involves extracting the perfusion time-intensity curve from acquired ultrasound contrast-enhanced image data, obtaining perfusion parameters from this curve, color-coding these parameters for imaging, performing 3D spatial localization and reconstruction to obtain a 3D data matrix of ultrasound contrast-enhanced perfusion multi-parameters, and finally performing surface reconstruction and rendering on the 3D data matrix to complete 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging and achieve 3D visualization. This method is adaptable to varying physiological and pathological conditions. By adjusting imaging parameters such as thresholds and imaging range through algorithms, it accurately measures and visualizes perfusion parameters, and optimizes 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging in real time. Furthermore, the method performs surface rendering on the reconstructed 3D volume. Users can set thresholds to extract and view different surfaces from the inside to the outside of the 3D volume, and customize camera viewpoint, section depth, transparency, etc., providing users with various visualization options to meet their needs. This invention achieves high-precision and high-efficiency reconstruction of 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging, resulting in higher visualization and greater ease of use.

[0026] Furthermore, the ultrasound contrast imaging data is acquired through a one-dimensional or two-dimensional array probe, which can flexibly match the specific needs of the object being tested, the support of the system platform, and different imaging requirements such as imaging depth and resolution.

[0027] Furthermore, the ultrasound imaging data is acquired through mechanical scanning or freehand scanning, which can adapt to more imaging scenarios. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic flowchart of a 3D ultrasound contrast perfusion multi-parameter functional imaging method according to the present invention.

[0030] Figure 2 This is a schematic diagram of the structure of a 3D ultrasound contrast perfusion multi-parameter functional imaging system according to the present invention;

[0031] Figure 3 This is a schematic flowchart of the 3D ultrasound contrast perfusion multi-parameter functional imaging method in Embodiment 2 of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0033] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0034] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0035] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0036] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0037] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0038] The present invention will now be described in further detail with reference to the accompanying drawings:

[0039] Example 1

[0040] like Figure 1 As shown, this invention discloses a 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging method, comprising the following steps:

[0041] S1: Acquire ultrasound contrast imaging data and the spatial position parameter matrix of probe movement when acquiring the ultrasound contrast imaging data;

[0042] Specifically, depending on the specific needs of the subject, system platform, imaging depth, and resolution, one-dimensional or two-dimensional array probes can be flexibly selected, and various scanning methods can be combined to acquire ultrasound contrast imaging data of the subject under normal breathing or cardiac activity. These various scanning methods include, but are not limited to, mechanical scanning and handheld scanning. Mechanical scanning relies on physical instruments to achieve probe translation and rotation through methods such as linear scanning, tilt scanning, and rotational scanning. Handheld scanning relies on manual operation by the operator and can scan the region of interest in any direction and position, adapting to more imaging scenarios. Mechanical scanning includes methods such as linear scanning, tilt scanning, and rotational scanning; handheld scanning includes scanning with and without position tracking sensors.

[0043] Further optimized, the freehand scanning method offers two modes, providing different levels of operational freedom and imaging accuracy to meet various clinical and research scenarios. One mode is equipped with a position tracking sensor, including an optical locator and inertial measurement unit, to achieve precise control of the scanning path and high-precision spatial positioning. This is suitable for applications requiring high imaging accuracy. When using the position sensor, spatial position parameters such as quaternions w, x, y, z, and 3D positions X, Y, Z are recorded for subsequent reconstruction algorithms. The other mode does not have a position tracking sensor, relying on the operator's experience and skills for handheld scanning, making it more suitable for rapid imaging and flexible operation.

[0044] This invention allows for the free combination of different probes and scanning methods to complete the acquisition of ultrasound contrast imaging data under the physiological conditions of the target, such as free breathing and heartbeat.

[0045] S2: Obtain the perfusion time-intensity curve based on the ultrasound contrast imaging data, obtain the perfusion parameters based on the perfusion time-intensity curve, and perform pseudo-color encoding on the values ​​of the perfusion parameters to obtain a set of two-dimensional perfusion images with multiple parameters in different spatial phases.

[0046] Specifically, the ultrasound contrast imaging data acquired in step S1 is processed using machine learning algorithms to obtain the periodic phase curves C(t) of respiration, heartbeat, etc. Based on C(t), the original data is reconstructed to obtain the data set M under the same spatial phase of probe scanning. i After selecting the region of interest, perform a selection on M. i The perfusion time-intensity curve and perfusion parameters at different spatial phases based on this curve are calculated. Pseudo-color encoding is performed based on the values ​​of the perfusion parameters to obtain a set of two-dimensional perfusion images with multiple parameters at different spatial phases, I. i .

[0047] More specifically, the above process begins by using machine learning algorithms to process the collected data and obtain the periodic phase curves C(t) of respiration, heartbeat, etc.

[0048] Then, motion gating recognition is performed based on the periodicity of C(t) to obtain the set of image or video frame data S within the same period. i At the same time, it also obtains the data set M under the same spatial phase scan by the probe. i .

[0049] After selecting the region of interest, then for M i The perfusion time-intensity curve is calculated, and after noise reduction and filtering, different spatial phase perfusion parameters based on the time, intensity, and ratio categories of the curve are obtained.

[0050] Finally, pseudo-color encoding was performed based on the perfusion characteristic intensity and multi-perfusion parameter values ​​of the contrast microbubbles to obtain a set of two-dimensional images of multi-parameter perfusion at different spatial phases, I. i .

[0051] S3: Perform three-dimensional spatial reconstruction on the set of two-dimensional perfusion images with different spatial phases and the spatial position parameter matrix to obtain a three-dimensional data matrix of ultrasound contrast perfusion with multiple parameters;

[0052] Specifically, for different probe scanning methods, specific spatial reconstruction algorithms are applied to realize the multi-parameter perfusion two-dimensional images I with different spatial phases in step S2. i The mapping of each pixel to a three-dimensional voxel is used to obtain three-dimensional voxel data V, and then spatial interpolation algorithms based on machine learning or deep learning are used to fill the empty voxels in V.

[0053] The process of further obtaining a three-dimensional data matrix of multi-parameter ultrasound contrast perfusion includes the following steps:

[0054] S31: For mechanical scanning or freehand scanning equipped with a position sensor, I can be achieved based on the recorded spatial position parameters. i Pixel-to-voxel spatial mapping; for freehand scanning without position sensors, adjacent frame displacement parameters can be obtained based on speckle decorrelation or deep learning reconstruction algorithms, thus achieving I... i Pixel-to-voxel spatial mapping;

[0055] S32: The spatial mapping from pixel to voxel is as follows: Based on the spatial position parameters or inter-frame displacement parameters described in S31, the set of three-dimensional spatial coordinates C of each pixel in each frame of ultrasound contrast imaging is calculated. The minimum and maximum values ​​of the coordinates in the three dimensions are calculated from C to form a three-dimensional voxel matrix envelope that covers all pixels. Then, the position (i.e., index) of each pixel in the three-dimensional voxel matrix is ​​calculated based on the coordinates of each pixel, and the grayscale value or RGB value of the pixel is assigned to the voxel to obtain the three-dimensional voxel data V.

[0056] S33: The 3D voxel data V obtained in S32 above contains many null voxels. First, select the 3D spatial range to be interpolated and calculate the position index of all null voxels in V within this range. Next, traverse each index and set a step size range threshold to obtain a small set of voxels centered on the null value. Finally, a machine learning voxel interpolation algorithm can be used to calculate a certain feature value (such as mean, maximum, etc.) of the voxel data in this set and fill the null value; alternatively, a deep learning-based interpolation network can be used to learn the features of the overall data V and calculate and fill the voxel value at the null index.

[0057] More specifically, for mechanical scanning or freehand scanning equipped with position sensors, I / O can be achieved based on the recorded spatial position parameters. i Pixel-to-voxel spatial mapping; for freehand handheld scanning without position sensors, the relative displacement parameters of adjacent frames can be obtained based on speckle decorrelation or deep learning reconstruction algorithms, thus achieving I... i The pixel-to-voxel spatial mapping yields 3D data V. The 3D data V obtained in the above steps contains many unmapped voxels. First, a 3D spatial range for interpolation is selected, and the position indices of all unmapped voxels within this range in V are calculated. Next, each index is traversed, and a step size threshold is set to obtain a small set of voxels centered on that unmapped value. Finally, a voxel interpolation algorithm based on machine learning or deep learning can be used to calculate a certain feature value (such as mean, maximum, etc.) of the voxel data in this set and fill the unmapped values.

[0058] S4: Perform surface reconstruction and rendering on the three-dimensional data matrix of the ultrasound contrast perfusion multi-parameter to complete 3D ultrasound contrast perfusion multi-parameter functional imaging.

[0059] Specifically, the interpolated 3D voxel data obtained in step S4 is surface-rendered using computer graphics or deep learning-based surface rendering methods to complete the data visualization process, supporting diverse visualization operations such as custom viewing angle, facet depth, and transparency. In this step, computer graphics surface rendering methods include, but are not limited to, rasterization, ray tracing, global illumination algorithms, and shading models; deep learning-based surface rendering methods include, but are not limited to, point cloud-based rendering and view generation based on neural radiation fields.

[0060] This invention proposes a 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging technique. It provides a complete algorithmic workflow tailored to different clinical scenarios, patient needs, or system platform limitations. This workflow includes data acquisition, extraction of perfusion parameters and color-coded imaging, 3D spatial localization, interpolation reconstruction, and finally, 3D visualization. This invention greatly assists clinicians in comprehensively understanding the perfusion information of tissues or lesions. The method is adaptable to varying physiological and pathological conditions. Through algorithmic adjustments to imaging parameters such as thresholds, imaging range, and interpolation range, it accurately measures and visualizes perfusion parameters, optimizing 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging in real time. Specifically, this method utilizes computer graphics or deep learning-based surface rendering methods to render the reconstructed 3D volume surface represented by discrete voxel meshes. Existing volume rendering software or libraries can be used to view the rendered surface after reconstruction. Thresholds can be set to extract and view different surfaces from the inside to the outside of the 3D volume. Users can also customize camera perspective, section depth, transparency, etc., providing various visualization operations to meet their needs.

[0061] In addition, such as Figure 2 As shown, the present invention also discloses a 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging system, comprising:

[0062] Data acquisition module: The data acquisition module is used to acquire ultrasound contrast imaging data and the spatial position parameter matrix of probe movement when acquiring the ultrasound contrast imaging data;

[0063] First data processing module: The first data processing module is used to obtain perfusion time-intensity curves based on the ultrasound contrast imaging data, obtain perfusion parameters based on the perfusion time-intensity curves, and perform pseudo-color encoding on the values ​​of the perfusion parameters to obtain a set of two-dimensional perfusion images with multiple parameters in different spatial phases.

[0064] Second data processing module: The second data processing module is used to perform three-dimensional spatial reconstruction on the set of two-dimensional perfusion images with different spatial phases and spatial position parameter matrix to obtain a three-dimensional data matrix of ultrasound contrast perfusion with multiple parameters.

[0065] The third data processing module is used to perform surface reconstruction and rendering of the three-dimensional data matrix of the ultrasound contrast perfusion multi-parameter, and to complete the 3D ultrasound contrast perfusion multi-parameter functional imaging.

[0066] Example 2

[0067] To further illustrate the technical solution of the present invention, in conjunction with... Figure 3 The following examples illustrate this:

[0068] A 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging technique includes the following steps:

[0069] (1) For contrast-enhanced ultrasound imaging of the liver, under free breathing conditions, a one-dimensional convex array probe was selected, and ultrasound contrast image data were acquired by a free-handed scanning device equipped with a position sensor. At the same time, the spatial position parameters during probe movement were recorded: w, x, y, z, X, Y, Z (wxyz is also known as a quaternion to represent rotation, and XYZ to represent displacement), resulting in the spatial position parameter matrix Ω=[…ξ t …],ξ t =[w t ,x t ,y t ,z t ,X t ,Y t Z t ] T .

[0070] (2) The ultrasound contrast imaging data acquired in (1) are processed as follows:

[0071] (2.1) First, each frame of the acquired ultrasound contrast imaging data is reconstructed from a two-dimensional matrix into a one-dimensional matrix, and then sorted according to the time series to obtain a two-dimensional matrix X = […d t …],(d t =[(x1) T …(x n ) T ], 1≤t≤T). d t x represents the numerical matrix of each pixel value after reconstruction of the angiographic image at time t. n This represents the nth column vector of the angiographic image at time t.

[0072] (2.2) The two-dimensional matrix X is decomposed into different feature matrices or principal components using machine learning algorithms. A certain feature matrix or principal component (determined according to the specific experiment) can characterize the periodic phase curve C(t) of the subject's free breathing.

[0073] (2.3) Motion gating recognition is performed based on the periodicity of C(t) to obtain the set of image or video frame data S within the same period. i At the same time, it also obtains the data set M = [m1…m] under the same spatial phase scan by the probe. n ], m i (1≤i≤n) represents the set of all ultrasound contrast imaging data at a certain spatial location within the scanning range.

[0074] (2.4) Select the region of interest in the imaging field of view, and move the image step by step in m pixels. i Calculate the perfusion time-intensity curve. After noise reduction and filtering, obtain different spatial phase perfusion parameters based on the curve, categorized by time, intensity, and ratio. The intensity parameters include peak value and area under the curve; the ratio parameters include inflow rate and outflow rate; and the time parameters include inflow time, outflow time, and time to peak.

[0075] (2.5) The values ​​of various perfusion parameters calculated in (2.4) are pseudo-color encoded to obtain a set of two-dimensional perfusion images with different spatial phases and multi-parameter parameters, I. i .

[0076] (3) Set of two-dimensional perfusion images with different spatial phases I i The following are the specific steps for reconstructing three-dimensional space using the spatial location parameter matrix Ω:

[0077] (3.1) First, consider the w in matrix Ω t ,x t ,y t ,z t The parameters are calculated using the following formulas to obtain the rotation angles φ around the X-axis, θ around the Y-axis, and ψ around the Z-axis:

[0078] φ=atan2(2(wx+yz),1-2(x2+y2))asin(2(wy-zx))

[0079] ψ=atan2(2(wz+xy),1-2(y2+z2))

[0080] In relation to X t ,Y t Z t Together, calculate the set of spatial coordinates of all image frame centers Ψ = […ψ t …],ψ t =[a t ,b t ,c t ] T , where a t ,b t ,c tThese represent the coordinate values ​​on the X-axis, Y-axis, and Z-axis, respectively.

[0081] (3.2) Find the minimum and maximum values ​​in the three dimensions from the spatial coordinate set Ψ, forming a three-dimensional voxel matrix V with size (a max -a min )×(b max -b min )×(c max -c min ). Then, based on I i The coordinates of each pixel are used to calculate its position (i.e., index) in the three-dimensional voxel matrix, and the grayscale or RGB value of the pixel is assigned to that voxel to obtain the uninterpolated three-dimensional data matrix V of ultrasound contrast perfusion multiparameter.

[0082] (3.3) Define the three-dimensional spatial range in V that needs to be interpolated, and calculate the position indices of all null voxels in V within this range. Use the distance-weighted interpolation algorithm from machine learning algorithms to interpolate the null points ψ. i Calculate the predicted value The specific calculation formula is as follows:

[0083]

[0084]

[0085] Where, ψ i It is the distance d(ψ,ψ) between the null point and its surrounding points. i The weights are calculated, and p is a positive real number used to control how distance affects the weights; here, p = 2 is chosen. The calculation for all null points yields the three-dimensional data matrix V of ultrasound contrast-enhanced perfusion multi-parameters.

[0086] (4) The surface reconstruction and rendering of the three-dimensional data matrix V of the ultrasound contrast perfusion multi-parameter model were performed using a method based on the implicit expression function of the neural network to complete the data visualization process. The specific steps are as follows:

[0087] (4.1) First, a deep implicit model based on a multilayer perceptron is constructed. Then, the 3D volume V is sliced ​​in three dimensions to obtain a two-dimensional image set. This dataset and the spatial location parameter matrix Ω are used as inputs to the deep implicit model.

[0088] (4.2) The coordinate set C is positionally encoded as shown in the following formula to improve the model's ability to capture details.

[0089] E(p)=(sin(2^0πp),cos(2^0πp),sin(2^1πp),cos(2^1πp),…,sin(2^(L-1)πp),cos(2^(L-1)πp)), where p is the normalized 3D coordinate and L is the number of encoding layers.

[0090] Choose by The defined loss function measures the difference between the model output and the actual voxel intensity, where F θ (ψ i ) is the voxel intensity value predicted by the model, ψ i It is the input 3D coordinate, v i Here, represents the true intensity value, and N is the amount of training data. The Adam optimizer is used to optimize the model parameters. In each iteration, the model updates its weights by minimizing the loss function to more accurately predict the voxel intensity values ​​corresponding to the 3D coordinates.

[0091] (4.3) Arbitrary views of 3D volumes can be generated by providing arbitrary 3D coordinates to a trained model. The surface of a 3D volume can be extracted by finding 3D coordinates whose intensity values ​​satisfy a certain threshold (e.g., a zero level set), thus enabling visualization of the external surface and internal structure.

[0092] (4.4) Use existing volumetric rendering software or libraries to view the surface rendered after surface reconstruction. At the same time, you can customize the camera view, facet depth, transparency, etc., to provide users with a variety of visualization operations to meet their needs.

[0093] Additionally, a schematic diagram of a terminal device according to an embodiment of the present invention is provided. This terminal device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0094] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0095] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0096] The processor can be a central processing unit (CPU), or 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.

[0097] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0098] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they 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 can also 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: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0099] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging method, characterized in that, Includes the following steps: S1: Acquire ultrasound contrast imaging data and a spatial position parameter matrix of probe movement during the acquisition of the ultrasound contrast imaging data; the ultrasound contrast imaging data is acquired through a one-dimensional or two-dimensional array probe; the ultrasound contrast imaging data is acquired through mechanical scanning or freehand handheld scanning. S2: Obtain the perfusion time-intensity curve based on the ultrasound contrast imaging data, obtain the perfusion parameters based on the perfusion time-intensity curve, and perform pseudo-color encoding on the values ​​of the perfusion parameters to obtain a set of two-dimensional perfusion images with multiple parameters in different spatial phases. S3: Perform three-dimensional spatial reconstruction on the set of two-dimensional perfusion images with different spatial phases and the spatial position parameter matrix to obtain a three-dimensional data matrix of ultrasound contrast perfusion with multiple parameters; S4: Perform surface reconstruction and rendering on the three-dimensional data matrix of the ultrasound contrast perfusion multi-parameter to complete 3D ultrasound contrast perfusion multi-parameter functional imaging.

2. The 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging method according to claim 1, characterized in that, The ultrasound contrast imaging data includes ultrasound contrast imaging data under conditions of free breathing or disturbance of physiological movement of heartbeat.

3. The 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging method according to claim 1, characterized in that, The infusion parameters include time-based, intensity-based, and ratio-based infusion parameters.

4. The 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging method according to claim 3, characterized in that, The time-related perfusion parameters include infusion time, infusion time, and peak time; the intensity-related perfusion parameters include peak value and area under the perfusion time-intensity curve; the ratio-related perfusion parameters include infusion rate and infusion rate.

5. A 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging system, characterized in that, A method for performing a 3D ultrasound contrast-enhanced perfusion multi-parameter functional imaging method according to any one of claims 1 to 4 includes: Data acquisition module: The data acquisition module is used to acquire ultrasound contrast imaging data and the spatial position parameter matrix of probe movement when acquiring the ultrasound contrast imaging data; First data processing module: The first data processing module is used to obtain perfusion time-intensity curves based on the ultrasound contrast imaging data, obtain perfusion parameters based on the perfusion time-intensity curves, and perform pseudo-color encoding on the values ​​of the perfusion parameters to obtain a set of two-dimensional perfusion images with multiple parameters in different spatial phases. Second data processing module: The second data processing module is used to perform three-dimensional spatial reconstruction on the set of two-dimensional perfusion images with different spatial phases and spatial position parameter matrix to obtain a three-dimensional data matrix of ultrasound contrast perfusion with multiple parameters. The third data processing module is used to perform surface reconstruction and rendering of the three-dimensional data matrix of the ultrasound contrast perfusion multi-parameter, and to complete 3D ultrasound contrast perfusion multi-parameter functional imaging.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4.

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