An ultrasonic three-dimensional wide-field imaging method and an ultrasonic apparatus

Abstract

The application discloses an ultrasonic three-dimensional wide-view imaging method and ultrasonic equipment. Firstly, a plurality of initial three-dimensional data volumes on a tissue organ are collected through a volume probe; then, polar coordinate information is converted into Cartesian coordinate information for each initial three-dimensional data volume to obtain a target three-dimensional data volume; feature points and feature point descriptors of each target three-dimensional data volume are extracted based on an ORB algorithm, the target three-dimensional data volumes are matched, and a plurality of space transformation matrices are obtained; finally, a weighted splicing method is used to obtain three-dimensional wide-view image data. According to the application, the target three-dimensional data volumes are registered based on the ORB algorithm, and finally, a three-dimensional wide-view image is obtained, so that the fusion effect of the scanning images with a certain rotation angle is good, and the finally obtained three-dimensional wide-view image has good continuity, smoothness and stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, in particular to an ultrasonic three-dimensional wide-view imaging method and an ultrasonic device. BACKGROUND

[0002] Traditional ultrasonic two-dimensional wide-view imaging technology acquires a series of two-dimensional images of the surface of an observed tissue organ through a probe, and then splices them into a continuous large-view image. However, since the ultrasonic two-dimensional wide-view imaging technology can only display a section view of the tissue organ at a certain angle, it cannot display the change in the curvature of the surface of the tissue organ, and thus cannot completely present the three-dimensional structure of the tissue organ, which has limitations. The traditional ultrasonic three-dimensional imaging technology can break through the limitations of the ultrasonic two-dimensional wide-view imaging technology and can present the three-dimensional structure of the observed tissue organ, but is limited by the width and scanning angle of the ultrasonic volume probe and cannot completely present the three-dimensional structure of a larger tissue organ.

[0003] To solve the above problems, three-dimensional wide-view imaging technology has emerged. However, the three-dimensional wide-view imaging technology in the prior art adopts a manual scanning mode, and only when two three-dimensional images are parallel can a three-dimensional wide-view image with good fusion effect be obtained. For scanning images with a certain rotation angle, the fusion effect is poor. SUMMARY

[0004] The present application provides an ultrasonic three-dimensional wide-view imaging method and an ultrasonic device to solve the problem of poor fusion effect for scanning images with a certain rotation angle existing in the prior art three-dimensional wide-view imaging technology.

[0005] In a first aspect, the present application provides an ultrasonic three-dimensional wide-view imaging method, which comprises:

[0006] acquiring, by a volume probe, a plurality of initial three-dimensional data volumes on a tissue organ in a polar coordinate system;

[0007] For each initial three-dimensional data volume, converting the polar coordinate information corresponding to each initial three-dimensional data into Cartesian coordinate information based on a scan conversion algorithm, and then performing interpolation calculation on each Cartesian coordinate information to obtain a target three-dimensional data volume;

[0008] After extracting feature points and feature point descriptors of each target three-dimensional data volume based on an ORB algorithm, matching each target three-dimensional data volume with each to-be-registered three-dimensional data volume based on the extracted feature points and feature point descriptors to obtain a plurality of spatial transformation matrices, wherein the fixed three-dimensional data volume is any one of the obtained target three-dimensional data volumes, and the to-be-registered three-dimensional data volume is other target three-dimensional data volume except the fixed three-dimensional data volume;

[0009] After the plurality of spatial transformation matrices are calculated by using the weighted splicing method to obtain three-dimensional wide-view image data, the three-dimensional wide-view image is displayed based on the three-dimensional wide-view image data.

[0010] In a possible implementation, the interpolation calculation on each Cartesian coordinate information to obtain the target three-dimensional data volume includes:

[0011] For each Cartesian coordinate information, the Cartesian coordinate information is subjected to three-axis interpolation based on adjacent preset number of pixel points on the scanning line in the polar coordinate system, to obtain pixel values of the preset number of pixel points, respectively.

[0012] For each pixel value in the pixel values of the preset number of pixel points, the pixel value is subjected to three-horizontal interpolation along the horizontal axis in the polar coordinate system.

[0013] The data obtained after the three-horizontal interpolation is taken as the target three-dimensional data volume.

[0014] In a possible implementation, after the interpolation calculation on each Cartesian coordinate information to obtain the target three-dimensional data volume, the method further includes:

[0015] The target three-dimensional data volume is subjected to Gaussian filtering processing and / or histogram equalization processing.

[0016] In a possible implementation, the extraction of the feature points and the feature point descriptors of each target three-dimensional data volume based on the ORB algorithm includes:

[0017] For each target three-dimensional data volume, the feature points of the target three-dimensional data volume are extracted by using the Oriented FAST algorithm.

[0018] The feature point descriptors of the target three-dimensional data volume are extracted based on the feature points of the target three-dimensional data volume by using the Rotated BRIEF algorithm.

[0019] In a possible implementation, the matching of each target three-dimensional data volume with the fixed three-dimensional data volume based on the extracted feature points and the feature point descriptors to obtain a plurality of spatial transformation matrices includes:

[0020] For each group of fixed three-dimensional data volume and to-be-registered three-dimensional data volume, the feature points of the fixed three-dimensional data volume and the feature points of the to-be-registered three-dimensional data volume are matched based on the BFMatcher algorithm according to the feature point descriptors of the fixed three-dimensional data volume and the feature point descriptors of the to-be-registered three-dimensional data volume, to obtain a plurality of groups of feature points.

[0021] Based on the RANSAC method, the multiple sets of feature points are filtered, and the filtered feature points are used as a spatial transformation matrix.

[0022] In a possible implementation, after the multiple spatial transformation matrices are obtained by matching the fixed three-dimensional data volume and each three-dimensional data volume to be registered based on the extracted feature points and feature point descriptors, the method further includes:

[0023] For each spatial transformation matrix, the spatial transformation matrix and a global transformation matrix are solved based on the SVD method, to obtain a first rotation angle and a first translation distance corresponding to the spatial transformation matrix, and to obtain a second rotation angle and a second translation distance corresponding to the global transformation matrix, wherein the global transformation matrix is calculated according to the three-dimensional wide-view image data obtained by using the weighted splicing method last time;

[0024] According to the first rotation angle, the second rotation angle, the first translation distance, the second translation distance, a preset threshold, and a number of successful registrations, it is determined whether the spatial transformation matrix is successfully registered.

[0025] If the registration is successful, the spatial transformation matrix that is successfully registered is used to obtain the three-dimensional wide-view image data of this time by using the weighted splicing method; if the registration is unsuccessful, the three-dimensional data volume to be registered corresponding to the spatial transformation matrix that is unsuccessfully registered is discarded.

[0026] In a possible implementation, the determining whether the spatial transformation matrix is successfully registered according to the first rotation angle, the second rotation angle, the first translation distance, the second translation distance, a preset threshold, and a number of successful registrations includes:

[0027] If the following preset conditions are met, it is determined that the spatial transformation matrix is successfully registered, otherwise, it is determined that the spatial transformation matrix is unsuccessfully registered, wherein the preset conditions include all of the following:

[0028] The first translation distance is less than or equal to a first preset threshold;

[0029] The first translation distance is less than or equal to the second translation distance divided by the number of successful registrations;

[0030] The first rotation angle is less than or equal to a second preset threshold;

[0031] The first rotation angle is less than or equal to the second rotation angle divided by the number of successful registrations.

[0032] In a second aspect, an embodiment of the present application provides an ultrasonic device, including a volume probe, a processing unit, and a display unit.

[0033] The volume probe is configured to collect a plurality of initial three-dimensional data volumes in a polar coordinate system on a tissue organ.

[0034] The processing unit is configured to, for each initial three-dimensional data volume, convert the polar coordinate information corresponding to each initial three-dimensional data volume into Cartesian coordinate information based on a scan conversion algorithm, perform interpolation calculation on each Cartesian coordinate information to obtain a target three-dimensional data volume, extract feature points and feature point descriptors corresponding to each target three-dimensional data volume based on an ORB algorithm, and perform matching between a fixed three-dimensional data volume and each to-be-registered three-dimensional data volume based on the extracted feature points and feature point descriptors to obtain a plurality of spatial transformation matrices, wherein the fixed three-dimensional data volume is any one of the obtained target three-dimensional data volumes, and the to-be-registered three-dimensional data volume is any one of the other target three-dimensional data volumes except the fixed three-dimensional data volume; and perform calculation on the plurality of spatial transformation matrices by using a weighted stitching method to obtain three-dimensional wide-view image data.

[0035] The display unit is configured to display a three-dimensional wide-view image corresponding to the three-dimensional wide-view image data.

[0036] In a possible implementation, the processing unit is specifically configured to:

[0037] For each Cartesian coordinate information, perform three-axis interpolation on the Cartesian coordinate information based on a preset number of adjacent pixel points on a scan line in the polar coordinate system to obtain pixel values of the preset number of pixel points, respectively.

[0038] For each pixel value in the pixel values of the preset number of pixel points, perform three horizontal interpolations on the pixel value along a horizontal axis in the polar coordinate system.

[0039] The data obtained after the three horizontal interpolations is taken as the target three-dimensional data volume.

[0040] In a third aspect, an embodiment of the present application provides an ultrasonic device, and the device comprises:

[0041] The acquisition module is configured to collect, by using a volume probe, a plurality of initial three-dimensional data volumes in a polar coordinate system on a tissue organ.

[0042] The reconstruction module is configured to, for each initial three-dimensional data volume, convert the polar coordinate information corresponding to each initial three-dimensional data volume into Cartesian coordinate information based on a scan conversion algorithm, perform interpolation calculation on each Cartesian coordinate information to obtain a target three-dimensional data volume.

[0043] The registration module, based on the ORB algorithm, extracts feature points and feature point descriptors of each target three-dimensional data body, and based on the extracted feature points and feature point descriptors, matches each to-be-registered three-dimensional data body with a fixed three-dimensional data body to obtain a plurality of spatial transformation matrices, wherein the fixed three-dimensional data body is any one of the obtained target three-dimensional data bodies, and the to-be-registered three-dimensional data body is other target three-dimensional data body than the fixed three-dimensional data body;

[0044] The fusion module calculates the plurality of spatial transformation matrices by using a weighted splicing method to obtain three-dimensional wide-view image data.

[0045] The display module is configured to display a three-dimensional wide-view image corresponding to the three-dimensional wide-view image data.

[0046] The application has the following advantages:

[0047] The application provides an ultrasonic three-dimensional wide-view imaging method and an ultrasonic device. First, a plurality of initial three-dimensional data bodies in a polar coordinate system on a tissue organ are collected by a volume probe. Then, for each initial three-dimensional data body, polar coordinate information corresponding to each initial three-dimensional data body is converted into Cartesian coordinate information based on a scan conversion algorithm, and each Cartesian coordinate information is subjected to interpolation calculation to obtain a target three-dimensional data body. Feature points and feature point descriptors of each target three-dimensional data body are extracted based on an ORB algorithm, and each to-be-registered three-dimensional data body is matched with a fixed three-dimensional data body based on the extracted feature points and feature point descriptors to obtain a plurality of spatial transformation matrices. Finally, the plurality of spatial transformation matrices are calculated by using a weighted splicing method to obtain three-dimensional wide-view image data, and a three-dimensional wide-view image is displayed based on the three-dimensional wide-view image data. In the application, the target three-dimensional data bodies are registered based on the ORB algorithm, and a three-dimensional wide-view image is finally obtained, so that the fusion effect of the scanning images with a certain rotation angle is good, and the finally obtained three-dimensional wide-view image has good continuity, smoothness and stability. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0049] Figure 1 The structure diagram of the ultrasonic device provided by the application;

[0050] Figure 2 The flowchart of the ultrasonic three-dimensional wide-view imaging method provided by the application;

[0051] Figure 3 a schematic diagram for converting polar coordinate information into Cartesian coordinate information provided by an embodiment of the present application;

[0052] Figure 4 a flowchart for performing interpolation calculation on each Cartesian coordinate information provided by an embodiment of the present application;

[0053] Fig. 5(a) is a schematic diagram of cubic axial interpolation provided by an embodiment of the present application;

[0054] Fig. 5(b) is a schematic diagram of cubic transverse interpolation provided by an embodiment of the present application;

[0055] Figure 6 a flowchart for extracting feature points and feature point descriptors of each target three-dimensional data volume based on ORB algorithm provided by an embodiment of the present application;

[0056] Figure 7 a flowchart for extracting feature points of target three-dimensional data volume by Oriented FAST algorithm provided by an embodiment of the present application;

[0057] Figure 8 a flowchart for extracting feature point descriptors of target three-dimensional data volume provided by an embodiment of the present application;

[0058] Figure 9 a flowchart for matching fixed three-dimensional data volume with each to-be-registered three-dimensional data volume respectively provided by an embodiment of the present application;

[0059] Figure 10 a flowchart for making a reasonable decision on judging a plurality of spatial transformation matrices provided by an embodiment of the present application;

[0060] Figure 11 a schematic diagram of three-dimensional wide-view image display provided by an embodiment of the present application;

[0061] Figure 12 a structural schematic diagram of an ultrasonic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0063] The application scenarios described in the embodiments of the present disclosure are used to more clearly illustrate the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art can know that the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems as new application scenarios appear.

[0064] In addition, in the description of the embodiments of the present application, unless otherwise specified, " / " represents the meaning of or, for example, A / B can represent A or B; "and / or" in the text only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist together, and B exists alone.

[0065] Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "multiple" is two or more.

[0066] The traditional two-dimensional wide-view imaging technology of ultrasound can only display the section view of the tissue organ at a certain angle, and cannot display the change of the surface curvature of the tissue organ. The traditional three-dimensional imaging technology of ultrasound is limited by the width and scanning angle of the ultrasonic volume probe, and cannot completely present the three-dimensional structure of a larger tissue organ. To solve the above problems, three-dimensional wide-view imaging technology emerges as the times require. However, if the three-dimensional wide-view imaging technology in the prior art adopts a manual scanning mode, only in the case that two three-dimensional images are parallel, a good fusion effect can be obtained. For scanning images with a certain rotation angle, the fusion effect is not good.

[0067] In view of the above problems, the present application provides an ultrasonic three-dimensional wide-view imaging method and an ultrasonic device. The application scenario of an ultrasonic three-dimensional wide-view imaging method provided by the embodiments of the present application will be introduced below in combination with the drawings. As shown in the figure, it is a structural schematic diagram of an ultrasonic device, which can perform three-dimensional wide-view imaging. The ultrasonic device includes a volume probe 101, a processing unit 102, and a display screen 103, wherein: Figure 1

[0068] The volume probe 101 is used to collect a plurality of initial three-dimensional data volumes on the tissue organ in the polar coordinate system;

[0069] ​The processing unit 102 is configured to, for each initial three-dimensional data volume, convert each polar coordinate information into Cartesian coordinate information based on a scan conversion algorithm, and then perform interpolation calculation on each Cartesian coordinate information to obtain a target three-dimensional data volume; based on the extracted feature points and feature point descriptors, match each target three-dimensional data volume with each to-be-registered three-dimensional data volume to obtain a plurality of spatial transformation matrices, wherein the fixed three-dimensional data volume is any one of the obtained target three-dimensional data volumes, and the to-be-registered three-dimensional data volume is any one of the other target three-dimensional data volumes except the fixed three-dimensional data volume; and calculate the plurality of spatial transformation matrices by using a weighted splicing method to obtain three-dimensional wide-view image data.

[0070] The display screen 103 is configured to display a three-dimensional wide-view image corresponding to the three-dimensional wide-view image data.

[0071] Figure 1 In the application, a plurality of initial three-dimensional data volumes on a tissue organ in a polar coordinate system are collected by the volume probe 101, and then the processing unit 102 is configured to, for each initial three-dimensional data volume, convert the polar coordinate information corresponding to each initial three-dimensional data volume into Cartesian coordinate information based on a scan conversion algorithm, and then perform interpolation calculation on each Cartesian coordinate information to obtain a target three-dimensional data volume, register the target three-dimensional data volume, and obtain a plurality of spatial transformation matrices, calculate the plurality of spatial transformation matrices by using a weighted splicing method to obtain three-dimensional wide-view image data, and finally display a three-dimensional wide-view image corresponding to the three-dimensional wide-view image data by using the display screen 103.

[0072] The method provided in the application is not limited to the application scenarios shown in the figures, but can also be applied to other possible application scenarios, which are not limited in the application. Figure 1 The method provided in the application is not limited to the application scenarios shown in the figures, but can also be applied to other possible application scenarios, which are not limited in the application.

[0073] As shown in the figure, the flowchart of the three-dimensional wide-view imaging method of the application is shown in the figure, and the specific steps are as follows: Figure 2

[0074] S201, a plurality of initial three-dimensional data volumes on a tissue organ in a polar coordinate system are collected by a volume probe;

[0075] S202, for each initial three-dimensional data volume, the polar coordinate information corresponding to each initial three-dimensional data volume is converted into Cartesian coordinate information based on a scan conversion algorithm, and then interpolation calculation is performed on each Cartesian coordinate information to obtain a target three-dimensional data volume;

[0076] ​S203, after extracting the feature points and the feature point descriptors of each target three-dimensional data volume based on the ORB algorithm, performing matching on the fixed three-dimensional data volume and each to-be-registered three-dimensional data volume based on the extracted feature points and the feature point descriptors, and obtaining a plurality of spatial transformation matrices, wherein the fixed three-dimensional data volume is any one of the obtained target three-dimensional data volumes, and the to-be-registered three-dimensional data volume is any one of the other target three-dimensional data volumes except the fixed three-dimensional data volume;

[0077] S204, after calculating the plurality of spatial transformation matrices by using the weighted splicing method and obtaining three-dimensional wide-view image data, displaying the three-dimensional wide-view image based on the three-dimensional wide-view image data.

[0078] The method provided by the embodiment of the application comprises the following steps: first, collecting a plurality of initial three-dimensional data volumes on a tissue organ in a polar coordinate system by using a volume probe; then, for each initial three-dimensional data volume, converting polar coordinate information corresponding to each initial three-dimensional data volume into Cartesian coordinate information based on a scan conversion algorithm, and performing interpolation calculation on each Cartesian coordinate information to obtain a target three-dimensional data volume; extracting feature points and feature point descriptors of each target three-dimensional data volume based on an ORB algorithm, and performing matching on a fixed three-dimensional data volume and each to-be-registered three-dimensional data volume based on the extracted feature points and the feature point descriptors to obtain a plurality of spatial transformation matrices; finally, calculating the plurality of spatial transformation matrices by using a weighted splicing method to obtain three-dimensional wide-view image data, and displaying a three-dimensional wide-view image based on the three-dimensional wide-view image data. In the embodiment of the application, the target three-dimensional data volumes are registered based on the ORB algorithm, and finally a three-dimensional wide-view image is obtained, so that the fusion effect of the scanning images with a certain rotation angle is good, and the finally obtained three-dimensional wide-view image has good continuity, smoothness and stability.

[0079] It should be noted that the working principle of the volume probe is to swing the motor inside the volume probe to emit ultrasonic waves, and to receive echoes with two-dimensional section information of the tissue organ. In the process of collecting the tissue organ by using a manual scanning mode through the volume probe multiple times, first, the volume probe is stopped at a certain position of the tissue organ to collect a plurality of two-dimensional sections, and the collected plurality of two-dimensional sections form an initial three-dimensional data volume, then the volume probe is moved to another position to collect a plurality of two-dimensional sections, and the volume probe is moved multiple times in turn to obtain a plurality of initial three-dimensional data volumes, wherein the first collected position and the subsequently collected positions need to have an overlapping area.

[0080] In specific embodiments, the plurality of initial three-dimensional data volumes on the tissue organ in the polar coordinate system are collected from the volume probe, and the plurality of polar coordinate information in each initial three-dimensional data volume is obtained, therefore, the polar coordinate information needs to be reconstructed into three-dimensional data.

[0081] In this embodiment of the application, the three-dimensional data in the polar coordinate system are the included angle θ and the depth R. For example, the initial three-dimensional data of point a1 is (θ1, R1), the initial three-dimensional data of point a2 is (θ2, R2), the initial three-dimensional data of point a3 is (θ3, R3), and the initial three-dimensional data of point a4 is (θ4, R4). Then the initial three-dimensional data volume is {(θ1, R1), (θ2, R2), (θ3, R3), (θ4, R4)}.

[0082] In one feasible implementation, a scan conversion algorithm is adopted and accelerated using a GPU (Graphics Processing Unit) to improve performance. The following is a method for 3D data reconstruction based on the scan conversion algorithm.

[0083] 1) Convert polar coordinate information to Cartesian coordinate information

[0084] As a possible implementation method, such as Figure 3 The diagram shown illustrates the conversion of polar coordinate information to Cartesian coordinate information according to an embodiment of this application. For example, taking a pixel A in the Cartesian coordinate system as an example, if the Cartesian coordinate information of A is (X... a Y a Using the included angle θ and depth R from the known polar coordinate information, the coordinates (X, Y) of A in the corresponding Cartesian coordinate information can be obtained. a Y a) :

[0085]

[0086] Y a =(R-probeRadius)+sampleNum×deadCoe

[0087] Where startAngle is the scanning angle of the volume probe, 2α is the scanning radian of the volume probe, σ is the radian interval between each two scan lines, probeRadius is the radius of the volume probe, sampleNum is the number of pixels on the scan line with depth R, and deadCoe is the dead zone coefficient.

[0088] 2) Interpolate each Cartesian coordinate to obtain the target 3D data volume.

[0089] As a possible implementation method, such as Figure 4 The diagram shows the steps for interpolating each Cartesian coordinate information according to an embodiment of this application.

[0090] S401. For each Cartesian coordinate information, perform cubic axial interpolation on the Cartesian coordinate information based on a preset number of adjacent pixels on the scan line in polar coordinate system to obtain the pixel values ​​of the preset number of pixels respectively.

[0091] In a specific embodiment, as shown in Figure 5(a), a cubic axial interpolation diagram provided by the present application is shown. For example, taking point (X,Y) as an example, cubic axial interpolation is performed based on four adjacent pixels on the scan line to obtain the pixel values ​​of four pixels (X_1,Y), (X0,Y), (X1,Y), and (X2,Y). The formula for calculating the axial difference is as follows:

[0092] Pixel(X_1,Y)=h(1+YoffSet)×Pixel(X_1,Y_1)+h(YoffSet)×Pixel(X_1,Y0)+h(1-YoffSet)×Pixel(X_1,Y1)+h(2-YoffSet)×Pixel(X_1,Y2)

[0093] Pixel(X0,Y)=h(1+YoffSet)×Pixel(X0,Y_1)+h(YoffSet)×Pixel(X0,Y0)+h(1-YoffSet)×Pixel(X0,Y1)+h(2-YoffSet)×Pixel(X0,Y2)

[0094] Pixel(X1,Y)=h(1+YoffSet)×Pixel(X1,Y_1)+h(YoffSet)×Pixel(X1,Y0)+h(1-YoffSet)×Pixel(X1,Y1)+h(2-YoffSet)×Pixel(X1,Y2)

[0095] Pixel(X2,Y)=h(1+YoffSet)×Pixel(X2,Y_1)+h(YoffSet)×Pixel(X2,Y0)+h(1-YoffSet)×Pixel(X2,Y1)+h(2-YoffSet)×Pixel(X2,Y2)

[0096] The formula for calculating h(x) is as follows:

[0097]

[0098] It should be noted that α is a constant, with a value of -1, -0.75, or -0.5.

[0099] S402, for each pixel value in the pixel values of the preset number of pixel points, performing cubic horizontal interpolation on the pixel value along the horizontal axis in the polar coordinate system;

[0100] In specific embodiments, as shown in FIG. 5(b), a cubic horizontal interpolation diagram provided by the embodiments of the application is shown. Based on the pixel values of the four pixel points (X_1, Y), (X0, Y), (X1, Y), (X2, Y) obtained by axial interpolation, cubic horizontal interpolation is performed to obtain the pixel value of (X, Y). The horizontal difference calculation formula is as follows:

[0101] Pixel(X, Y) = h(1+XoffSet) x Pixel(X_1, Y) + h(XoffSet) x Pixel(X_1, Y) + h(1-XoffSet) x Pixel(X_1, Y1) + h(2-XoffSet) x Pixel(X_1, Y)

[0102] Wherein, XoffSet is the offset in the X-axis direction, and YoffSet is the offset in the Y-axis direction.

[0103] S403, taking the data obtained after the cubic horizontal interpolation as the target three-dimensional data body.

[0104] The embodiments of the application can perform cubic axial interpolation calculation on the data to be reconstructed through CUDA (Compute Unified Device Architecture, Compute Unified Device Architecture), and then perform cubic horizontal interpolation calculation, and finally obtain the target three-dimensional data body after three-dimensional reconstruction. For example, there are 1 million data to be reconstructed, and each data needs to be calculated once in the target three-dimensional data reconstruction process. Cubic axial interpolation calculation is performed again on each data, and GPU acceleration processing is adopted to improve performance and ensure timeliness.

[0105] It should be noted that the commonly used interpolation methods in the scan conversion algorithm include linear interpolation method, quadratic interpolation method and cubic interpolation method. Among them, the linear interpolation method uses two adjacent pixel points near the scan line for interpolation; the quadratic interpolation method uses three adjacent pixel points near the scan line for interpolation; and the cubic interpolation method used by the application uses four adjacent pixel points near the scan line for interpolation, and the image quality is better.

[0106] In addition, after performing interpolation calculation on each Cartesian coordinate information to obtain the target three-dimensional data body, it further includes: performing Gaussian filtering processing and / or histogram equalization processing on the target three-dimensional data body.

[0107] In a feasible implementation, Gaussian filtering is used to process the noise generated in the three-dimensional reconstruction process of the target three-dimensional data body, and histogram equalization is used to enhance the image contrast of the target three-dimensional data body.

[0108] In specific embodiments, the target three-dimensional data body also needs to be registered before being fused, for example, taking the mapping transformation of two target three-dimensional data bodies in spatial coordinate positions and voxel values as an example, fixing the three-dimensional data body I1(x, y, z) and the three-dimensional data body I2(x, y, z) to be registered, and the mapping transformation of the two target three-dimensional data bodies in the voxel values and the spatial coordinate positions is as follows:

[0109] I1(x, y, z) = g(I2(T(x, y, z)))

[0110] where g is a voxel value transformation matrix, and T is a three-dimensional spatial transformation matrix. Since there is a large overlap area between two adjacent target three-dimensional data bodies, the voxel value does not change much, and therefore, the target three-dimensional data bodies need to be registered by obtaining the spatial transformation matrix between the target three-dimensional data bodies.

[0111] In a feasible implementation, after the feature points and the feature point descriptors of each target three-dimensional data body are extracted based on the ORB algorithm, the fixed three-dimensional data body is matched with each three-dimensional data body to be registered based on the extracted feature points and the feature point descriptors, and a plurality of spatial transformation matrices are obtained. The following describes the method for extracting the feature points and the feature point descriptors and matching the fixed three-dimensional data body with each three-dimensional data body to be registered.

[0112] 1) Extracting the feature points and the feature point descriptors of each target three-dimensional data body based on the ORB algorithm

[0113] As a feasible implementation, as shown in Figure 6 the figure, the steps for extracting the feature points and the feature point descriptors of each target three-dimensional data body based on the ORB algorithm are provided. It should be noted that the ORB algorithm includes the Oriented FAST algorithm and the Rotated BRIEF algorithm, wherein the Oriented FAST algorithm is used to extract the feature points of the target three-dimensional data body, and the Rotated BRIEF algorithm is used to extract the feature point descriptors of the target three-dimensional data body.

[0114] S601, for each target three-dimensional data body, the feature points of the target three-dimensional data body are extracted by the Oriented FAST algorithm; as shown in Figure 7 the figure, taking a pixel point p as an example, I p is the pixel value of the pixel point p, and the specific steps are as follows:

[0115] S701, taking the pixel point p as the center and a radius of 3 pixel points, a circle is determined, and 16 pixel points on the circle are p1, p2,..., p 16 ;

[0116] S702, for the pixel values of the 16 pixel points on the circle, the difference between the pixel value I p1 , I p2 ... I p16 of the 16 pixel points on the circle and the pixel value I p of the pixel point p is calculated respectively;

[0117] S703, if there are n pixel points on the circle that satisfy the difference greater than a preset value t or the difference less than a preset value-t, the pixel point p is determined as a candidate point, wherein n can be 12;

[0118] S704, for the obtained multiple candidate points, candidate point screening is performed based on the non-maximum suppression algorithm to obtain multiple feature points.

[0119] It should be noted that in specific embodiments, the Oriented FAST algorithm is based on Gaussian blur, downsampling and feature point detection processing of the target three-dimensional data body, so that the feature points of the obtained target three-dimensional data body have scale invariance; the center O and the center of mass C of the three-dimensional data body are defined and connected to obtain the direction δ of the feature point, so that the feature points of the obtained target three-dimensional data body have rotation invariance.

[0120] S602, the feature point descriptor of the target three-dimensional data body is extracted based on the feature points of the target three-dimensional data body by the Rotated BRIEF algorithm. As shown in Figure 8 , taking one feature point B of the target three-dimensional data body as an example, the steps are as follows:

[0121] S801, for the feature point B of the target three-dimensional data body, taking the feature point B as the center and taking SxS as the neighborhood window, wherein S is pre-set;

[0122] S802, selecting N pairs of random points in the neighborhood window, and repeatedly performing binary assignment for the N pairs of random points to form a binary code, obtaining a binary code which is a description of the feature point B, i.e. a feature point descriptor, wherein N can be 256.

[0123] It should be noted that the Rotated BRIEF algorithm can also calculate S δ based on the direction δ of the feature point, and replace S with S δ , so that the obtained feature point descriptor has rotation invariance.

[0124] wherein let S δ =R δX S.

[0125] 2) based on the extracted feature points and feature point descriptors, matching the fixed three-dimensional data volume with each of the to-be-registered three-dimensional data volumes

[0126] As a feasible implementation manner, as shown in the following Figure 9 , the step of matching the fixed three-dimensional data volume with each of the to-be-registered three-dimensional data volumes provided by the embodiment of the present application.

[0127] S901, for each group of fixed three-dimensional data volume and to-be-registered three-dimensional data volume, based on the BFMatcher algorithm, according to the feature point descriptors of the fixed three-dimensional data volume and the feature point descriptors of the to-be-registered three-dimensional data volume, matching the feature points of the fixed three-dimensional data volume and the feature points of the to-be-registered three-dimensional data volume, to obtain multiple groups of feature points;

[0128] S902, based on the RANSAC method, screening the multiple groups of feature points, and taking the screened feature points as a spatial transformation matrix.

[0129] It should be noted that, in specific embodiments, during the process of matching the fixed three-dimensional data volume with each of the to-be-registered three-dimensional data volumes, GPU can also be used for acceleration to shorten the calculation time and ensure the timeliness in clinical application.

[0130] In addition, as a feasible implementation manner, in specific embodiments, after matching the fixed three-dimensional data volume with each of the to-be-registered three-dimensional data volumes based on the extracted feature points and feature point descriptors to obtain multiple spatial transformation matrices, in order to ensure the continuity and stability of subsequent target three-dimensional data volume fusion, it is also necessary to make a reasonable decision judgment on the obtained multiple spatial transformation matrices. As shown in the following Figure 10 , the step of making a reasonable decision judgment on the multiple spatial transformation matrices provided by the embodiment of the present application.

[0131] S1001, for each spatial transformation matrix, based on the SVD method, solving the spatial transformation matrix and the global transformation matrix to obtain the first rotation angle (θ x1 , θ y1 , θ z1 ) corresponding to the spatial transformation matrix and the first translation distance d1, and obtain the second rotation angle (θ x2 , θ y2 , θ z2 ) corresponding to the global transformation matrix and the second translation distance d2, wherein the global transformation matrix is calculated according to the three-dimensional wide view image data obtained by using the weighted splicing method last time;

[0132] For example, taking a spatial transformation matrix C as an example:

[0133]

[0134] In the spatial transformation matrix C, the 3×3 matrix in the upper left corner is the rotation matrix D.

[0135]

[0136] Based on the SVD method, the rotation matrix D can be decomposed into an orthogonal matrix U, a diagonal matrix ∑, and a transpose of an orthogonal matrix V. T The product of, i.e., D = U∑V T Therefore, the rotation angles θ of the X, Y, and Z axes in the Cartesian coordinate system corresponding to the spatial transformation matrix C can be obtained. x θ y θ z .

[0137] It should be noted that the translation distance

[0138] S1002, Based on the first rotation angle (θ) x1 ,θ y1 ,θ z1 ), second rotation angle (θ) x2 ,θ y2 ,θ z2 The system uses the following parameters to determine whether the spatial transformation matrix has been successfully registered: first translation distance d1, second translation distance d2, first preset threshold D, second preset threshold (α,β,ω), and the number of successful registrations t.

[0139] In a specific embodiment, if the following preset conditions are met, the spatial transformation matrix registration is determined to be successful; otherwise, the spatial transformation matrix registration is determined to be unsuccessful. The preset conditions include all of the following:

[0140] The first translation distance d1 is less than or equal to the first preset threshold D;

[0141] The first translation distance d1 is less than or equal to the second translation distance d2 divided by the number of successful registrations t;

[0142] First rotation angle (θ) x1 ,θ y1 ,θ z1 The value is less than or equal to the second preset threshold (α,β,ω);

[0143] First rotation angle (θ) x1 ,θ y1 ,θ z1 ) less than or equal to the second rotation angle (θ) x2 ,θ y2 ,θ z2 Divide by the number of successful registrations, t.

[0144] S1003、If the registration is successful, the spatial transformation matrix of the successful registration is used to obtain the three-dimensional wide-view image data of this time by using the weighted splicing method; if the registration is unsuccessful, the three-dimensional data volume corresponding to the spatial transformation matrix of the unsuccessful registration is discarded.

[0145] It should be noted that discarding the three-dimensional data volume corresponding to the spatial transformation matrix of the unsuccessful registration does not affect the final display effect.

[0146] In addition, after the spatial transformation matrix of the successful registration is used to obtain the three-dimensional wide-view image data of this time by using the weighted splicing method, the global transformation matrix needs to be solved and fed back to the step of making a rationality decision for the judgment of the spatial transformation matrix.

[0147] For example, in a feasible embodiment, five initial three-dimensional data volumes on a tissue organ are obtained by using a volume probe.

[0148] Step 1, for the obtained five initial three-dimensional data volumes, polar coordinate information is converted into Cartesian coordinate information based on a scan conversion algorithm, and after interpolation calculation, five target three-dimensional data volumes F1, F2, F3, F4 and F5 are obtained.

[0149] Step 2, based on the ORB algorithm, the target three-dimensional data volume F1 can be used as a fixed three-dimensional data volume, and the target three-dimensional data volumes F2, F3, F4 and F5 can be used as three-dimensional data volumes to be registered. Two spatial transformation matrices E1 and E2 are obtained according to the fixed three-dimensional data volume F1 and the three-dimensional data volumes to be registered F2 and F3, the spatial transformation matrices E1 and E2 are calculated by using the weighted splicing method, and three-dimensional wide-view image data is obtained. According to the obtained three-dimensional wide-view image data, the global transformation matrix G1 of this time is calculated.

[0150] Step 3, for the three-dimensional data volume to be registered F4, the fixed three-dimensional data volume F1 and the three-dimensional data volume to be registered F4 are used to generate a spatial transformation matrix E3, and the global transformation matrix G1 is solved based on the SVD method and the three-dimensional wide-view image data obtained by using the weighted splicing method in the last time, and it is judged whether the spatial transformation matrix is successfully registered. If the registration is successful, the spatial transformation matrix E3 of the successful registration is used to obtain the three-dimensional wide-view image data of this time by using the weighted splicing method, and the global transformation matrix G2 of this time is calculated; if the registration is unsuccessful, the three-dimensional data volume F4 corresponding to the spatial transformation matrix of the unsuccessful registration is discarded.

[0151] Step 4, for the three-dimensional data volume F5 to be registered, continue to generate a spatial transformation matrix E4 for the fixed three-dimensional data volume F1 and the three-dimensional data volume F5 to be registered, and solve a global transformation matrix G2 based on the SVD method and the three-dimensional wide-view image data obtained by using the weighted splicing method in the last time, to determine whether the spatial transformation matrix is successfully registered. If the spatial transformation matrix is successfully registered, the three-dimensional wide-view image data of this time is obtained by using the successfully registered spatial transformation matrix E4 and the weighted splicing method; if the spatial transformation matrix is not successfully registered, the three-dimensional data volume F5 to be registered corresponding to the spatial transformation matrix that is not successfully registered is discarded.

[0152] Step 5, display the three-dimensional wide-view image based on the finally obtained three-dimensional wide-view image data.

[0153] The method provided by the embodiments of the present application is not limited to the above-described embodiments, and can also be used in other possible embodiments, which are not limited herein.

[0154] Based on the above description, in specific embodiments, first, a plurality of initial three-dimensional data volumes on a tissue organ are collected by using a volume probe, then for each initial three-dimensional data volume, a target three-dimensional data volume is obtained, the target three-dimensional data volume is registered, a plurality of spatial transformation matrices are obtained, finally, the weighted splicing method is used to calculate the plurality of spatial transformation matrices to obtain three-dimensional wide-view image data, and then the three-dimensional wide-view image is displayed based on the three-dimensional wide-view image data, as shown in FIG. 8, which is a schematic diagram of three-dimensional wide-view image display provided by the embodiments of the present application. Figure 11

[0155] In a feasible implementation, the three-dimensional wide-view image data is rendered by using a GPU to obtain a three-dimensional wide-view image. In clinical operation, the obtained three-dimensional wide-view image can be rotated, translated and scaled, that is, the surface morphology of the entire tissue organ can be presented in 360 degrees; a cutting plane of an arbitrary position and an arbitrary three-dimensional angle can be defined to cut the three-dimensional wide-view image to display the cut surface image, that is, the internal morphology of the entire tissue organ can be presented completely; a three-dimensional ROI (Region of Interest) of an arbitrary irregular shape can be defined to crop the three-dimensional wide-view data volume, that is, an arbitrary partial three-dimensional morphology of the entire tissue organ can be presented; and distance measurement and angle measurement can be performed on the three-dimensional wide-view data volume, that is, an expression of an arbitrary size and angle of the entire tissue organ can be presented. Thus, the accuracy and convenience of clinical diagnosis are improved.

[0156] Based on the same disclosed concept, the ultrasonic three-dimensional wide-view imaging method as described above can also be implemented by an ultrasonic device. The ultrasonic device has similar effects to the above-described method, and details are not described herein.

[0157] The following describes the ultrasonic three-dimensional wide-view imaging device according to the embodiments of the present application with reference to the accompanying drawings. Figure 1 ​The illustrated ultrasound device is described in detail.

[0158] In a possible implementation, the processing unit 102 is specifically configured to:

[0159] For each Cartesian coordinate information, performing three times axial interpolation on the Cartesian coordinate information based on a preset number of adjacent pixel points on a scanning line in a polar coordinate system, to obtain pixel values of the preset number of pixel points, respectively;

[0160] For each pixel value in the pixel values of the preset number of pixel points, performing three times horizontal interpolation on the pixel value along a horizontal axis in the polar coordinate system;

[0161] Taking the data obtained after the three times horizontal interpolation as the target three-dimensional data volume.

[0162] In a possible implementation, the processing unit 102 is specifically configured to:

[0163] Performing Gaussian filtering processing and / or histogram equalization processing on the target three-dimensional data volume.

[0164] In a possible implementation, the processing unit 102 is specifically configured to:

[0165] For each target three-dimensional data volume, extracting feature points of the target three-dimensional data volume by using an Oriented FAST algorithm;

[0166] Extracting a feature point descriptor of the target three-dimensional data volume based on the feature points of the target three-dimensional data volume by using a Rotated BRIEF algorithm.

[0167] In a possible implementation, the processing unit 102 is specifically configured to:

[0168] For each group of fixed three-dimensional data volume and to-be-registered three-dimensional data volume, based on a BFMatcher algorithm, matching the feature points of the fixed three-dimensional data volume and the feature points of the to-be-registered three-dimensional data volume according to the feature point descriptor of the fixed three-dimensional data volume and the feature point descriptor of the to-be-registered three-dimensional data volume, to obtain a plurality of groups of feature points;

[0169] Based on a RANSAC method, screening the plurality of groups of feature points, and taking the screened feature points as a spatial transformation matrix.

[0170] In a possible implementation, the processing unit 102 is specifically configured to:

[0171] For each spatial transformation matrix, the spatial transformation matrix and the global transformation matrix are solved based on the SVD method to obtain the first rotation angle and the first translation distance corresponding to the spatial transformation matrix, and the second rotation angle and the second translation distance corresponding to the global transformation matrix. The global transformation matrix is ​​calculated based on the three-dimensional wide-view image data obtained by the weighted stitching method in the previous step.

[0172] Based on the first rotation angle, the second rotation angle, the first translation distance, the second translation distance, the preset threshold, and the number of successful registrations, determine whether the spatial transformation matrix has been successfully registered;

[0173] If registration is successful, the registered spatial transformation matrix is ​​weighted and stitched together to obtain the current 3D wide-view image data; if registration is unsuccessful, the 3D data volume to be registered corresponding to the unregistered spatial transformation matrix is ​​discarded.

[0174] In one possible implementation, the processing unit 102 is specifically used for:

[0175] If the following preset conditions are met, the spatial transformation matrix registration is determined to be successful; otherwise, the spatial transformation matrix registration is determined to be unsuccessful. The preset conditions include all of the following:

[0176] The first translation distance is less than or equal to the first preset threshold;

[0177] The first translation distance is less than or equal to the second translation distance divided by the number of successful registrations;

[0178] The first rotation angle is less than or equal to the second preset threshold;

[0179] The first rotation angle is less than or equal to the second rotation angle divided by the number of successful registrations.

[0180] Based on the same disclosed concept, the ultrasonic three-dimensional wide-view imaging method described above can also be implemented by an ultrasonic device. The effect of this ultrasonic device is similar to that of the aforementioned method and device, and will not be described again here.

[0181] like Figure 12 The diagram shown is a structural diagram of an ultrasonic device provided in an embodiment of this application. The device includes:

[0182] The acquisition module 1301 acquires multiple initial three-dimensional data volumes located in the polar coordinate system on tissues and organs through a volume probe;

[0183] The reconstruction module 1302, for each initial 3D data volume, converts each polar coordinate information into Cartesian coordinate information based on the scanning conversion algorithm, and then performs interpolation calculations on each Cartesian coordinate information to obtain the target 3D data volume;

[0184] The registration module 1303, based on the ORB algorithm, extracts the feature points and feature point descriptors of each target three-dimensional data body, and based on the extracted feature points and feature point descriptors, matches the fixed three-dimensional data body with each to-be-registered three-dimensional data body respectively, to obtain a plurality of spatial transformation matrices, wherein the fixed three-dimensional data body is any one of the obtained target three-dimensional data bodies, and the to-be-registered three-dimensional data body is other target three-dimensional data body except the fixed three-dimensional data body;

[0185] The fusion module 1304 adopts a weighted splicing method to calculate the plurality of spatial transformation matrices to obtain three-dimensional wide-view image data.

[0186] The display module 1305 is configured to display a three-dimensional wide-view image corresponding to the three-dimensional wide-view image data.

[0187] In a possible implementation, the reconstruction module 1302 is specifically configured to:

[0188] For each Cartesian coordinate information, the Cartesian coordinate information is subjected to three-axis interpolation based on adjacent preset number of pixel points on a scanning line in a polar coordinate system, to obtain pixel values of the preset number of pixel points respectively.

[0189] For each pixel value in the pixel values of the preset number of pixel points, the pixel value is subjected to three horizontal interpolation along a horizontal axis in the polar coordinate system.

[0190] The data obtained after the three horizontal interpolations is taken as the target three-dimensional data body.

[0191] In a possible implementation, the method further includes:

[0192] The preprocessing module 1306 is specifically configured to:

[0193] The target three-dimensional data body is subjected to Gaussian filtering processing and / or histogram equalization processing.

[0194] In a possible implementation, the registration module 1303 is specifically configured to:

[0195] For each target three-dimensional data body, the feature points of the target three-dimensional data body are extracted by using the Oriented FAST algorithm.

[0196] The feature point descriptors of the target three-dimensional data body are extracted based on the feature points of the target three-dimensional data body by using the Rotated BRIEF algorithm.

[0197] In a possible implementation, the registration module 1303 is specifically configured to:

[0198] For each group of fixed three-dimensional data body and to-be-registered three-dimensional data body, based on the BFMatcher algorithm, according to the feature point descriptor of the fixed three-dimensional data body and the feature point descriptor of the to-be-registered three-dimensional data body, the feature points of the fixed three-dimensional data body and the feature points of the to-be-registered three-dimensional data body are matched to obtain a plurality of groups of feature points.

[0199] Based on the RANSAC method, the plurality of groups of feature points are screened, and the screened feature points are used as a spatial transformation matrix.

[0200] In a possible implementation, the method further includes:

[0201] The decision module 1307 is specifically configured to:

[0202] For each spatial transformation matrix, based on the SVD method, the spatial transformation matrix and a global transformation matrix are solved to obtain a first rotation angle and a first translation distance corresponding to the spatial transformation matrix, and to obtain a second rotation angle and a second translation distance corresponding to the global transformation matrix, wherein the global transformation matrix is calculated according to the three-dimensional wide-view image data obtained by using the weighted splicing method last time;

[0203] According to the first rotation angle, the second rotation angle, the first translation distance, the second translation distance, a preset threshold value and a number of successful registrations, it is determined whether the spatial transformation matrix is successfully registered.

[0204] If the registration is successful, the spatial transformation matrix that is successfully registered is used to obtain the three-dimensional wide-view image data of this time by using the weighted splicing method; if the registration is unsuccessful, the to-be-registered three-dimensional data body corresponding to the spatial transformation matrix that is unsuccessfully registered is discarded.

[0205] In a possible implementation, the decision module 1307 is specifically configured to:

[0206] If the following preset conditions are met, it is determined that the spatial transformation matrix is successfully registered, otherwise, it is determined that the spatial transformation matrix is unsuccessfully registered, wherein the preset conditions include all of the following:

[0207] The first translation distance is less than or equal to a first preset threshold value;

[0208] The first translation distance is less than or equal to the second translation distance divided by the number of successful registrations;

[0209] The first rotation angle is less than or equal to a second preset threshold value;

[0210] The first rotation angle is less than or equal to the second rotation angle divided by the number of successful registrations.

[0211] The method and the device are used for three-dimensional wide-view imaging. The method comprises the following steps: collecting, by a volume probe, a plurality of initial three-dimensional data volumes on a tissue organ in a polar coordinate system; converting, for each initial three-dimensional data volume, polar coordinate information corresponding to the initial three-dimensional data volume into Cartesian coordinate information based on a scan conversion algorithm, and performing interpolation calculation on each Cartesian coordinate information to obtain a target three-dimensional data volume; extracting feature points and feature point descriptors of each target three-dimensional data volume based on an ORB algorithm, and matching a fixed three-dimensional data volume with each to-be-registered three-dimensional data volume based on the extracted feature points and feature point descriptors to obtain a plurality of spatial transformation matrices; and calculating the plurality of spatial transformation matrices by using a weighted splicing method to obtain three-dimensional wide-view image data, and displaying a three-dimensional wide-view image based on the three-dimensional wide-view image data. In the embodiment of the present application, the ORB algorithm is used to register the target three-dimensional data volumes, and finally a three-dimensional wide-view image is obtained, so that the fusion effect of the scanning images with a certain rotation angle is good, and the three-dimensional wide-view image obtained finally has good continuity, smoothness and stability.

[0212] The block diagrams and / or flowcharts described above refer to methods, apparatus (system) and / or computer program products according to embodiments of the present application. It should be understood that one block of the block diagrams and / or flowcharts and a combination of blocks of the block diagrams and / or flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, and / or other programmable data processing apparatus to produce a machine, so that the instructions executed by the computer processor and / or other programmable data processing apparatus create a method for implementing the functions / acts specified in the block diagrams and / or flowcharts.

[0213] Accordingly, the present application can also be embodied in hardware and / or in software (including firmware, resident software, micro-code, etc.). Furthermore, the present application can take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. In the context of the present application, a computer-usable or computer-readable medium can be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0214] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the spirit and scope of the present application. Thus, it is intended that the present application encompass such modifications and changes as fall within the scope of the appended claims and their equivalents.

Claims

1. A method for ultrasound three-dimensional wide-view imaging, characterized in that, include: Multiple initial three-dimensional data volumes located in polar coordinates are acquired using a volumetric probe; For each initial 3D data volume, based on the scanning conversion algorithm, the polar coordinate information corresponding to each initial 3D data is converted into Cartesian coordinate information, and then interpolation calculation is performed on each Cartesian coordinate information to obtain the target 3D data volume; After extracting feature points and feature point descriptors for each target 3D data volume using the ORB algorithm, the fixed 3D data volume is matched with each 3D data volume to be registered based on the extracted feature points and feature point descriptors to obtain multiple spatial transformation matrices. The fixed 3D data volume is any one of the obtained target 3D data volumes, and the 3D data volume to be registered is other target 3D data volumes besides the fixed 3D data volume. After calculating the multiple spatial transformation matrices using a weighted stitching method to obtain three-dimensional wide-view image data, the three-dimensional wide-view image is displayed based on the three-dimensional wide-view image data; The step of matching a fixed 3D data volume with each 3D data volume to be registered based on the extracted feature points and feature point descriptors to obtain multiple spatial transformation matrices further includes: For each spatial transformation matrix, the spatial transformation matrix and the global transformation matrix are solved based on the SVD method to obtain the first rotation angle and the first translation distance corresponding to the spatial transformation matrix, and the second rotation angle and the second translation distance corresponding to the global transformation matrix. The global transformation matrix is ​​calculated based on the three-dimensional wide-view image data obtained by the weighted stitching method in the previous step. Based on the first rotation angle, the second rotation angle, the first translation distance, the second translation distance, the preset threshold, and the number of successful registrations, it is determined whether the spatial transformation matrix has been successfully registered. If registration is successful, the registered spatial transformation matrix is ​​weighted and stitched together to obtain the current 3D wide-view image data; if registration is unsuccessful, the 3D data volume to be registered corresponding to the unregistered spatial transformation matrix is ​​discarded.

2. The method as described in claim 1, characterized in that, The process of interpolating each Cartesian coordinate to obtain the target 3D data volume includes: For each Cartesian coordinate information, the Cartesian coordinate information is interpolated three times along the axial direction based on a preset number of pixels on the scan line in polar coordinates to obtain the pixel values ​​of the preset number of pixels respectively. For each pixel value among the preset number of pixel values, the pixel value is interpolated three times along the horizontal axis in the polar coordinate system; The data obtained after three lateral interpolations is used as the target three-dimensional data volume.

3. The method as described in claim 1, characterized in that, After interpolating each Cartesian coordinate to obtain the target 3D data volume, the process further includes: The target 3D data volume is subjected to Gaussian filtering and / or histogram equalization.

4. The method as described in claim 1, characterized in that, The extraction of feature points and feature point descriptors for each target 3D data volume based on the ORB algorithm includes: For each target 3D data volume, feature points of the target 3D data volume are extracted using the Oriented FAST algorithm; The Rotated BRIEF algorithm is used to extract feature point descriptors of the target 3D data volume based on its feature points.

5. The method as described in claim 1, characterized in that, Based on the extracted feature points and feature point descriptors, a fixed 3D data volume is matched with each 3D data volume to be registered, resulting in multiple spatial transformation matrices, including: For each set of fixed 3D data volume and 3D data volume to be registered, based on the BFMatcher algorithm, the feature points of the fixed 3D data volume and the feature point descriptors of the 3D data volume to be registered are matched to obtain multiple sets of feature points. Based on the RANSAC method, the multiple sets of feature points are filtered, and the filtered feature points are used as the spatial transformation matrix.

6. The method as described in claim 1, characterized in that, The step of determining whether the spatial transformation matrix has been successfully registered based on the first rotation angle, the second rotation angle, the first translation distance, the second translation distance, a preset threshold, and the number of successful registrations includes: If the following preset conditions are met, the spatial transformation matrix registration is determined to be successful; otherwise, the spatial transformation matrix registration is determined to be unsuccessful. The preset conditions include all of the following: The first translation distance is less than or equal to the first preset threshold; The first translation distance is less than or equal to the second translation distance divided by the number of successful registrations; The first rotation angle is less than or equal to the second preset threshold; The first rotation angle is less than or equal to the second rotation angle divided by the number of successful registrations.

7. An ultrasonic device, characterized in that, Includes a volume probe, a processing unit, and a display unit; A volumetric probe is used to acquire multiple initial three-dimensional data volumes located in polar coordinates on tissues and organs. The processing unit is configured to, for each initial 3D data volume, convert the polar coordinate information corresponding to each initial 3D data volume into Cartesian coordinate information based on a scan conversion algorithm, and then perform interpolation calculations on each Cartesian coordinate information to obtain a target 3D data volume; extract the feature points and feature point descriptors corresponding to each target 3D data volume based on the ORB algorithm, and then match the fixed 3D data volume with each 3D data volume to be configured based on the extracted feature points and feature point descriptors to obtain multiple spatial transformation matrices, wherein the fixed 3D data volume is any one of the obtained target 3D data volumes, and the 3D data volume to be registered is other target 3D data volumes besides the fixed 3D data volume; and calculate the multiple spatial transformation matrices using a weighted stitching method to obtain 3D wide-view image data. The display unit is used to display a three-dimensional wide-view image corresponding to the three-dimensional wide-view image data; Specifically, the processing unit is used to solve for each spatial transformation matrix using the SVD method to obtain a first rotation angle and a first translation distance corresponding to the spatial transformation matrix, and a second rotation angle and a second translation distance corresponding to the global transformation matrix. The global transformation matrix is ​​calculated based on the previously obtained 3D wide-view image data using a weighted stitching method. Based on the first rotation angle, the second rotation angle, the first translation distance, the second translation distance, a preset threshold, and the number of successful registrations, the unit determines whether the spatial transformation matrix has been successfully registered. If registration is successful, the successfully registered spatial transformation matrix is ​​then used to obtain the current 3D wide-view image data using a weighted stitching method. If registration fails, the 3D data volume to be registered corresponding to the unregistered spatial transformation matrix is ​​discarded.

8. The ultrasonic device as described in claim 7, characterized in that, The processing unit is specifically used for: For each Cartesian coordinate information, the Cartesian coordinate information is interpolated three times along the axial direction based on a preset number of pixels on the scan line in polar coordinates to obtain the pixel values ​​of the preset number of pixels respectively. For each pixel value among the preset number of pixel values, the pixel value is interpolated three times along the horizontal axis in the polar coordinate system; The data obtained after three lateral interpolations is used as the target three-dimensional data volume.

9. An ultrasonic device, characterized in that, The device includes: The acquisition module acquires multiple initial three-dimensional data volumes located in polar coordinates on tissues and organs through a volume probe; The reconstruction module, for each initial 3D data volume, converts the polar coordinate information corresponding to each initial 3D data volume into Cartesian coordinate information based on the scanning conversion algorithm, and then performs interpolation calculation on each Cartesian coordinate information to obtain the target 3D data volume; The registration module extracts feature points and feature point descriptors for each target 3D data volume based on the ORB algorithm. Then, based on the extracted feature points and feature point descriptors, it matches the fixed 3D data volume with each 3D data volume to be registered to obtain multiple spatial transformation matrices. The fixed 3D data volume is any one of the obtained target 3D data volumes, and the 3D data volume to be registered is any other target 3D data volume other than the fixed 3D data volume. The fusion module uses a weighted stitching method to calculate the multiple spatial transformation matrices to obtain three-dimensional wide-view image data; The display module is used to display a three-dimensional wide-view image corresponding to the three-dimensional wide-view image data; The device further includes a decision module, used to solve for each spatial transformation matrix using the SVD method to obtain a first rotation angle and a first translation distance corresponding to the spatial transformation matrix, and a second rotation angle and a second translation distance corresponding to the global transformation matrix. The global transformation matrix is ​​calculated based on the previously obtained 3D wide-view image data using a weighted stitching method. The module determines whether the spatial transformation matrix has been successfully registered based on the first rotation angle, the second rotation angle, the first translation distance, the second translation distance, a preset threshold, and the number of successful registrations. If registration is successful, the successfully registered spatial transformation matrix is ​​used to obtain the current 3D wide-view image data using a weighted stitching method. If registration fails, the 3D data volume to be registered corresponding to the unregistered spatial transformation matrix is ​​discarded.

Images