Volume Data Rendering Method, Apparatus, Computer Device, and Readable Storage Medium

Abstract

This application relates to the field of medical technology, and in particular, to a method and apparatus for rendering volume data, a computer device, and a readable storage medium. The method includes: obtaining volume data to be rendered, where the volume data includes volume data of at least two tissue bodies; determining sampling points; and rendering the tissue bodies according to the rendering parameters of each sampling point to obtain tissue body images, where the rendering parameters include tissue labels and tissue gradients. Using this method can make the rendered tissue boundaries look smoother and improve the image quality of the rendered tissue body images.

Description

Technical Field

[0001] This application relates to the field of medical technologies, and particularly to a method and apparatus for rendering volume data, a computer device, and a readable storage medium. Background Art

[0002] On modern medical post-processing workstations, technologies such as ray casting or ray tracing are generally used to render volume data obtained by imaging devices. For some special organ tissues such as blood vessels and the heart, since the voxel values obtained by scanning are similar to those of adjacent other tissues, it is necessary to first perform tissue segmentation on the volume data and then perform rendering. Generally, an image algorithm is used to assign a tissue label (Label) to each voxel of the volume data. Different tissue labels represent different tissue organs, and during rendering, rendering is performed according to the labels set by the user.

[0003] On the one hand, since tissue labels are discrete and not continuously variable, if the tissue label of a sampling point is determined by nearest-neighbor interpolation during the sampling process, discontinuity will occur at the junction of tissue bodies, forming jagged edges, resulting in poor quality of the generated image rendering.

[0004] On the other hand, the normal vectors for lighting calculations during the rendering process are usually determined by the gradients of the volume data. If the gradients of the volume data at the junction of tissue bodies are inconsistent with the normal vectors of the interface, or even distributed chaotically, it will also cause the lighting effect at the interface to be uneven and the overall display to be not smooth. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a method and apparatus for rendering volume data, a computer device, and a readable storage medium that can improve the image quality of tissue body rendering.

[0006] A method for rendering volume data, the method comprising:

[0007] Obtain volume data to be rendered, the volume data including volume data of at least two tissue bodies;

[0008] Determine sampling points;

[0009] Perform rendering of tissue bodies according to the rendering parameters of each sampling point to obtain a tissue body image, the rendering parameters including tissue labels and tissue gradients.

[0010] In one embodiment, the rendering parameters further include voxel data;

[0011] Performing rendering of tissue bodies according to the rendering parameters of each sampling point to obtain a tissue body image, includes:

[0012] Based on the tissue labels, determine sampling points corresponding to at least two tissue bodies;

[0013] Based on the voxel data and tissue gradients of the corresponding sampling points of at least two tissue bodies, draw the tissue bodies to obtain tissue body images.

[0014] In one embodiment, after determining the sampling points, it further includes:

[0015] According to the positional relationship between the sampling points and the voxel points in the volume data to be drawn, determine multiple initial voxel points corresponding to the sampling points;

[0016] According to the coordinate positions of each initial voxel point and the coordinate position of the sampling point, determine the voxel weights of each initial voxel point relative to the sampling point;

[0017] Based on the drawing parameters of each initial voxel point and each voxel weight, determine the drawing parameters corresponding to the sampling points.

[0018] In one embodiment, the drawing parameters further include voxel data;

[0019] Based on the drawing parameters of each initial voxel point and each voxel weight, determining the drawing parameters corresponding to the sampling points includes:

[0020] Based on the tissue labels of each initial voxel point, determine the tissue label corresponding to the sampling point;

[0021] According to the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight, determine the initial voxel data corresponding to the sampling point;

[0022] According to the initial voxel data of the sampling point, determine the tissue gradient and voxel data corresponding to the sampling point.

[0023] In one embodiment, according to the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight, determining the voxel data corresponding to the sampling point includes:

[0024] According to the voxel data of each initial voxel point and the corresponding voxel weights, determine the first initial voxel data corresponding to the sampling point;

[0025] According to the tissue label of the sampling point and the tissue labels of each initial voxel point, determine the target voxel points corresponding to the sampling point;

[0026] According to the voxel data of each target voxel point and the corresponding voxel weights, determine the second initial voxel data corresponding to the sampling point;

[0027] According to the initial voxel data of the sampling point, determining the tissue gradient and voxel data corresponding to the sampling point includes:

[0028] According to the first initial voxel data and the second initial voxel data, determine the tissue gradient and voxel data corresponding to the sampling point.

[0029] In one embodiment, determining the tissue gradient and voxel data of corresponding sampling points according to the first initial voxel data and the second initial voxel data includes:

[0030] Determining a corresponding first tissue gradient based on the first initial voxel data;

[0031] Determining a corresponding second tissue gradient based on the second initial voxel data;

[0032] Determining a corresponding gradient angle according to the first tissue gradient and the second tissue gradient;

[0033] Judging whether the gradient angle is less than a preset threshold;

[0034] When the gradient angle is less than the preset threshold, determining the first initial voxel data as the voxel data of the corresponding sampling point and determining the first tissue gradient as the tissue gradient of the corresponding sampling point;

[0035] When the gradient angle is greater than or equal to the preset threshold, determining the second initial voxel data as the voxel data of the corresponding sampling point and determining the second tissue gradient as the tissue gradient of the corresponding sampling point.

[0036] In one embodiment, determining the tissue label of a corresponding sampling point based on the tissue labels of each initial voxel point includes:

[0037] Determining the proportion of each tissue label based on the tissue labels of each initial voxel point;

[0038] Determining the tissue label with the largest proportion as the tissue label of the sampling point.

[0039] A volume data rendering device, the device includes:

[0040] A volume data acquisition module, configured to acquire volume data to be rendered, where the volume data includes volume data of at least two tissue bodies;

[0041] A sampling point determination module, configured to determine sampling points;

[0042] A rendering module, configured to perform rendering of tissue bodies according to the rendering parameters of each sampling point to obtain a tissue body image, where the rendering parameters include tissue labels and tissue gradients.

[0043] A computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0044] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0045] The above method, apparatus, computer device, and readable storage medium for rendering volume data obtain the volume data to be rendered, where the volume data includes volume data of at least two tissue bodies, determine sampling points, and perform rendering of the tissue bodies according to the tissue labels and tissue gradients in the rendering parameters of each sampling point, so as to obtain tissue body images. Thus, at the junction of different tissues, rendering can be performed according to the tissue labels and tissue gradients of the voxel points. The rendering at the tissue junction combines the tissue labels and tissue gradients of the tissue bodies. By combining the tissue labels and tissue gradients, different tissues can be accurately distinguished, making the rendered tissue boundaries look smoother, and thus improving the image quality of the obtained tissue body images. Brief Description of the Drawings

[0046] Figure 1 It is an application scenario diagram of the volume data rendering method in an embodiment;

[0047] Figure 2 It is a schematic flowchart of the volume data rendering method in an embodiment;

[0048] Figure 3 It is a schematic diagram of the correspondence between the initial voxel points and the sampling points in an embodiment;

[0049] Figure 4 It is a comparison schematic diagram of the generated tissue body images in an embodiment;

[0050] Figure 5 It is a structural block diagram of the volume data rendering apparatus in an embodiment;

[0051] Figure 6 It is an internal structure diagram of a computer device in an embodiment. Detailed Description of the Embodiments

[0052] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] The volume data rendering method provided by the present application can be applied, for example, Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. Among them, the terminal 102 can collect the volume data of the object to be detected. The volume data includes the volume data of at least two tissue bodies, and is sent to the server 104 for subsequent processing. Then, the server 104 can determine the sampling points. Further, the server 104 can draw the tissue bodies according to the drawing parameters of each sampling point to obtain the tissue body images. The drawing parameters include tissue labels and tissue gradients. The terminal 102 can be, but is not limited to, various imaging devices applied in the medical field, such as Computed Tomography (CT) devices, Magnetic Resonance (MR), Positron Emission Computed Tomography (PET), etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0054] In one embodiment, as Figure 2 shown, a method for drawing volume data is provided. Taking the server in Figure 1 as an example, the method includes the following steps:

[0055] Step S202, obtain the volume data to be drawn. The volume data includes the volume data of at least two tissue bodies.

[0056] Among them, a tissue body refers to the tissues that make up the human body or animal body, such as blood vessel tissue, bone tissue, or soft tissue, etc. It can also refer to different sub-tissues of the same tissue, such as each bronchus, each lung lobe, each blood vessel, etc. Multiple tissue bodies refer to the combination of at least two different tissue bodies, such as blood vessels and bones, or the combination of blood vessels, bones, and soft tissues, etc. It can also refer to the combination of one or more tissue bodies and the background area, or different tissues in the same organ, such as the lungs and lung tumors, pulmonary alveoli, etc.

[0057] Volume data refers to the original scan data generated when the detection object is scanned by an imaging device.

[0058] In the embodiment, the volume data can be composed of a number of voxels in multiple dimensions. For example, it can be similar to a three-dimensional image and composed of a number of voxels in three dimensions. Each voxel has its corresponding voxel center, and the voxel center has its corresponding voxel value, that is, voxel data. The resolution of the volume data is limited, and the voxel values of points not at the voxel center are not determined.

[0059] In this embodiment, the obtained volume data to be drawn includes the volume data of at least two tissue bodies, such as it can include the blood vessels and bones described above, or the combination of blood vessels, bones, and soft tissues, etc.

[0060] In this embodiment, the server can scan the detection object through the terminal to obtain the volume data to be drawn including at least two tissue bodies, and determine the drawing parameters of each voxel point in the volume data to be drawn.

[0061] Among them, the drawing parameter refers to the parameter required for drawing the tissue body image, which may include the tissue label, voxel data, tissue gradient, etc. of each voxel point in the tissue body.

[0062] In this embodiment, the tissue label is used to identify different tissue bodies in the volume data. For example, the bone is represented by 0, the blood vessel is represented by 1, and the soft tissue is represented by 2. The voxel data, as described above, includes the voxel value corresponding to the voxel center and is used to represent the tissue density. The tissue gradient is used to represent the change of the tissue body in a certain direction.

[0063] In this embodiment, the server can determine the tissue label of each voxel point and the corresponding voxel data of each voxel point based on the obtained volume data, and then determine the corresponding tissue gradient based on the voxel data.

[0064] In this embodiment, the tissue gradient can be determined based on the voxel data by a certain method. For example, by using the central difference algorithm or the Sobel edge detection algorithm, etc., the voxel data is solved to generate the corresponding tissue gradient.

[0065] Step S204, determine the sampling points.

[0066] As described above, the resolution of the volume data is limited, and the voxel values of the points not at the voxel center need to be generated by interpolation calculation.

[0067] In this embodiment, the server can determine the sampling points in the volume data to be drawn based on the resolution of the tissue body image to be generated, the sampling step size for obtaining the volume data to be drawn, etc., and then calculate the voxel data of the sampling points based on the volume data to be drawn.

[0068] Those skilled in the art can understand that this is only an example. In actual applications, the sampling points can also be determined based on other data, and the present application does not limit this.

[0069] In this embodiment, the server can determine the drawing parameters of each sampling point in the multi-tissue body according to the drawing parameters of the voxel points corresponding to each sampling point.

[0070] Specifically, the server can determine the voxel points corresponding to the sampling points from the multi-tissue body based on the sampling points, and then determine the drawing parameters corresponding to the sampling points based on the determined drawing parameters of the multi-tissue body, that is, determine the corresponding tissue label, voxel data, and tissue gradient of the corresponding tissue body, etc.

[0071] In this embodiment, when the sampling point coincides with the voxel point, the server can use the voxel point side rendering parameter as the rendering parameter of the sampling point. When the sampling point does not coincide with the voxel point, the server can perform interpolation through the voxel point to obtain the rendering parameter of the sampling point.

[0072] In this embodiment, the server can determine the rendering parameter of the corresponding sampling point according to the rendering parameters of a preset number of voxel points around the sampling point. For example, referring to Figure 3 , the rendering parameter of the corresponding sampling point D is determined according to the tissue rendering parameters of 8 voxel points D000, D001, D010, D011, D100, D101, D110, and D111 around the sampling point D.

[0073] Those skilled in the art can understand that this is only an example. In other embodiments, the rendering parameters of other numbers of voxel points can also be used to determine the rendering parameter of the corresponding sampling point, such as 27 voxel points or 68 voxel points, etc. The present application does not limit this.

[0074] Step S206: Perform rendering of the tissue volume according to the rendering parameters of each sampling point to obtain a tissue volume image.

[0075] In this embodiment, after the server determines the rendering parameters of each sampling point and each voxel point, it can perform volume rendering based on the determined rendering parameters by using ray casting or ray tracing to generate a tissue volume image corresponding to the acquired volume data and the corresponding resolution, and send it to the display end for display.

[0076] In the existing method, the tissue volume image is directly rendered based on the voxel data.

[0077] In this embodiment, when performing tissue volume rendering, the voxel label and tissue gradient are combined. When drawing the tissue boundary, by combining the voxel label and tissue gradient, each tissue volume is distinguished, and then rendering is performed, so that the drawing of the tissue boundary can be accurately distinguished based on the voxel label and tissue gradient, avoiding confusion, and improving the accuracy of the tissue boundary drawing in the tissue volume image drawing.

[0078] In this embodiment, the user can display the area of interest by adjusting the window width and window level of the display window of the display end.

[0079] In this embodiment, for some special organ tissues such as blood vessels and the heart, after screening the voxels and sampling points through tissue labels, multi-tissue volume images of the tissue volume of interest to the user can be drawn and displayed, such as only drawing and displaying blood vessels or only drawing and displaying bones, etc.

[0080] In the above method for rendering volumetric data, by obtaining the volumetric data to be rendered, where the volumetric data includes volumetric data of at least two tissue bodies, determining sampling points, and performing rendering of the tissue bodies according to the rendering parameters of each sampling point, a tissue body image is obtained. Thus, at the junction of different tissues, rendering can be performed based on the tissue labels and tissue gradients of the voxel points. The rendering at the tissue junction combines the tissue labels and tissue gradients of the tissue bodies. By combining the tissue labels and tissue gradients, different tissues can be accurately distinguished, making the rendered tissue boundaries appear smoother, and thus improving the image quality of the obtained tissue body image.

[0081] In one embodiment, according to the tissue rendering parameters of each sampling point and each voxel point, performing rendering of the tissue bodies to obtain a tissue body image may include: determining the voxel points corresponding to each tissue body based on the tissue labels; and performing rendering of the tissue bodies according to the voxel data and tissue gradients of the voxel points corresponding to each tissue body to obtain a tissue body image.

[0082] Specifically, the server can determine the voxel points corresponding to each tissue body based on the tissue labels, and display, hide, or highlight the corresponding tissue bodies based on the tissue labels, and then perform rendering of the corresponding tissue bodies. For example, if the server determines to only display blood vessels, it can determine the voxel points corresponding to only the blood vessels based on the blood vessel labels, and then perform rendering of multiple tissue bodies according to the voxel data and tissue gradients corresponding to the voxel points of the blood vessels to obtain a tissue body image of the corresponding blood vessels; or it can perform red highlighting on the area corresponding to the blood vessels, etc.

[0083] In one embodiment, after determining the sampling points, it may further include: determining a plurality of initial voxel points corresponding to the sampling points according to the positional relationship between the sampling points and each voxel point; determining the voxel weights of each initial voxel point relative to the sampling point according to the coordinate positions of each initial voxel point and the coordinate position of the sampling point; and determining the rendering parameters of the corresponding sampling point based on the rendering parameters of each initial voxel point and each voxel weight.

[0084] Among them, the coordinate position refers to the coordinates of each point, which can be three-dimensional coordinates and can be expressed as (x d , y d , z d ).

[0085] In this embodiment, the server can obtain the positional relationship between the sampling points and the voxel points according to the volumetric data, such as the three-dimensional spatial positional relationship, so as to determine the voxel points corresponding to the sampling points. For example, it is determined that the 8 voxel points closest to the sampling point in three-dimensional space are the initial voxel points corresponding to the sampling point, or it can also be other relationships, such as the sampling point being exactly located at the center of the cube formed by multiple voxel points, etc.

[0086] Further, the server can determine the voxel weights of each initial voxel point relative to the sampling point according to the determined coordinate positions of each initial voxel point and the coordinate position of the sampling point. Specifically, continue to refer to Figure 3 and take eight initial voxel points as an example to illustrate the calculation of voxel weights.

[0087] In this embodiment, continue to refer to Figure 3 After the server determines the coordinate positions of the sampling point D and the initial voxel points D000, D001, D010, D011, D100, D101, D110, and D111, it can determine the coordinate position of the projection point D00 of the sampling point D on the line connecting the initial voxel point D000 and the initial voxel point D100, and the coordinate position of the projection point D01 of the sampling point D on the line connecting the initial voxel point D001 and the initial voxel point D101, the coordinate position of the projection point D10 of the sampling point D on the line connecting the initial voxel point D010 and the initial voxel point D110, the coordinate position of the projection point D11 of the sampling point D on the line connecting the initial voxel point D011 and the initial voxel point D111, the coordinate position of the projection point D0 of the sampling point D on the projection points D00 and D10, and the coordinate position of the projection point D1 of the sampling point D on the projection points D01 and D11.

[0088] Further, the server can determine the voxel weights of each initial voxel point in the three dimensions of X, Y, and Z based on the coordinate positions of the projection points of the sampling point D and the positional relationship of each projection point between the initial voxel points forming the connection line. For example, for the initial voxel point D000, its voxel weight in the X dimension can be obtained as X D000 = L(D000, D00) / L(D00, D100). Here, L(D000, D00) represents the distance between the initial voxel point D000 and the projection point D00, and L(D00, D100) represents the distance between the projection point D00 and the initial voxel point D100. Similarly, the server can determine that the voxel weight of the initial voxel point D000 in the Y dimension is Y D000 = L(D01, D1) / L(D1, D11), and the voxel weight in the Z dimension is Z D000 = L(D0, D) / L(D0, D1). Here, L(D01, D1) represents the distance between the projection point D01 and the projection point D1, L(D1, D11) represents the distance between the projection point D1 and the projection point D11, L(D0, D) represents the distance between the projection point D0 and the sampling point D, and L(D0, D1) represents the distance between the projection point D0 and the projection point D1.

[0089] Based on the same principle as above, the server can determine the voxel weights of the remaining initial voxel points relative to the sampling point in the three dimensions of X, Y, and Z.

[0090] In this embodiment, after the server determines the voxel weights of each initial voxel point relative to the sampling point in the three dimensions of X, Y, and Z, it can determine the rendering parameters of the corresponding sampling point based on the rendering parameters of each initial voxel point and each voxel weight, and perform subsequent processing.

[0091] As described above, the rendering parameters may include tissue labels, voxel data, and tissue gradients.

[0092] In this embodiment, determining the rendering parameters of the corresponding sampling point based on the rendering parameters of each initial voxel point and each voxel weight may include: determining the tissue label of the corresponding sampling point based on the tissue labels of each initial voxel point; determining the initial voxel data of the corresponding sampling point according to the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight; and determining the tissue gradient and voxel data of the corresponding sampling point according to the initial voxel data of the sampling point.

[0093] In this embodiment, the server may determine and obtain the tissue labels of each initial voxel point based on the volume data of the tissue volume to be rendered, determine the tissue label of the corresponding sampling point according to the tissue label weights of the multiple initial voxel points, and then determine the tissue label with the highest tissue label weight as the tissue label of the corresponding sampling point.

[0094] In one embodiment, determining the tissue label of the corresponding sampling point based on the tissue labels of each initial voxel point may include: determining the proportion of each tissue label based on the tissue labels of each initial voxel point; and determining the tissue label with the largest proportion as the tissue label of the sampling point.

[0095] In this embodiment, the server may count the number of each different tissue label among multiple voxel points, and determine the proportion of each tissue label and obtain the tissue label weights of each different tissue label according to the counted number of each tissue label and the total number of tissue labels. Then the server may determine the tissue label with the largest proportion or the highest tissue label weight as the tissue label of the corresponding sampling point.

[0096] Continuing with Figure 3 the embodiment as an example for illustration, if the tissue labels of the initial voxel points D000, D100, D001, and D101 are all label 0, the tissue labels of D011, D111, and D110 are all label 1, and the tissue label of D010 is label 2, then it can be determined that the proportion of label 0 is 0.5, the proportion of label 1 is 0.375, and the proportion of label 2 is 0.125, and then it can be determined that label 0 is the tissue label of the corresponding sampling point. Those skilled in the art can understand that this is only an example for illustration and does not limit the solution of this application.

[0097] In this embodiment, after the server determines the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight, it can determine the initial voxel data corresponding to the sampling point, and then determine the tissue gradient and voxel data corresponding to the sampling point based on the determined initial voxel data.

[0098] In one embodiment, determining the initial voxel data corresponding to the sampling point according to the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight may include: determining the first initial voxel data corresponding to the sampling point according to the voxel data of each initial voxel point and the corresponding voxel weight; determining the target voxel point corresponding to the sampling point according to the tissue label of the sampling point and the tissue labels of each initial voxel point; and determining the second initial voxel data corresponding to the sampling point according to the voxel data of each target voxel point and the corresponding voxel weight.

[0099] Specifically, the server may determine the first initial voxel data corresponding to the sampling point based on the voxel data of each initial voxel point and the corresponding voxel weight. The specific calculation formula is as follows:

[0100] V D1 = V D000 (1 - X D000 )(1 - Y D000 )(1 - Z D000 ) + V D001 (1 - X D001 )(1 - Y D001 )(1 - Z D001 ) + V D010 (1 - X D010 )(1 - Y D010 )(1 - Z D010 ) + V D011 (1 - X D011 )(1 - Y D011 )(1 - Z D011 ) + V D100 (1 - X D100 )(1 - Y D100 )(1 - Z D100 ) + V D101 (1 - X D101 )(1 - Y D101 )(1 - Z D101 ) + V D110 (1 - X D110 )(1 - Y D110 )(1 - Z D110 ) + V D111 (1 - X D111 )(1 - Y D111 )(1 - Z D111 )

[0101] Among them, V D1 is the first initial voxel data of the sampling point, V D000 is the voxel data of the sampling point D000, X D000 is the voxel weight of the sampling point D000 in the X dimension, Y D000 is the voxel weight of the sampling point D000 in the Y dimension, Z D000 is the voxel weight of the sampling point D000 in the Z dimension, V D001 is the voxel data of the sampling point D001, X D001 is the voxel weight of the sampling point D001 in the X dimension, Y D001 is the voxel weight of the sampling point D001 in the Y dimension, Z D001 is the voxel weight of the sampling point D001 in the Z dimension, V D010 is the voxel data of the sampling point D010, X D010 is the voxel weight of the sampling point D010 in the X dimension, Y D010 is the voxel weight of the sampling point D010 in the Y dimension, Z D010 is the voxel weight of the sampling point D010 in the Z dimension, V D011 is the voxel data of the sampling point D011, X D011 is the voxel weight of the sampling point D011 in the X dimension, Y D011 is the voxel weight of the sampling point D011 in the Y dimension, Z D011 is the voxel weight of the sampling point D011 in the Z dimension, V D100 is the voxel data of the sampling point D100, X D100 is the voxel weight of the sampling point D100 in the X dimension, Y D100 is the voxel weight of the sampling point D100 in the Y dimension, Z D100 is the voxel weight of the sampling point D100 in the Z dimension, V D101 is the voxel data of the sampling point D101, X D101 is the voxel weight of the sampling point D101 in the X dimension, Y D101 is the voxel weight of the sampling point D101 in the Y dimension, Z D101 is the voxel weight of the sampling point D101 in the Z dimension, V D110 is the voxel data of the sampling point D110, X D110 is the voxel weight of the sampling point D110 in the X dimension, Y D110 is the voxel weight of the sampling point D110 in the Y dimension, Z D110 is the voxel weight of the sampling point D110 in the Z dimension, V D111 is the voxel data of the sampling point D111, X D111 is the voxel weight of the sampling point D111 in the X dimension, Y D111is the voxel weight of the sampling point D111 in the Y dimension, Z D111 is the voxel weight of the sampling point D111 in the Z dimension.

[0102] Further, the server may determine the target voxel points corresponding to the sampling points based on the tissue labels of the sampling points and the tissue labels of the initial voxel points. For example, as described in the foregoing embodiments, the tissue labels of the initial voxel points D000, D100, D001, and D101 are all label 0, the tissue labels of D011, D111, and D110 are all label 1, and the tissue label of D010 is label 2. If the tissue label of the sampling point D is 0, the server may determine, based on the tissue label of the sampling point, that the initial voxel points with consistent tissue labels are the target voxel points, that is, determine that the initial voxel points D000, D100, D001, and D101 are the target voxel points corresponding to the oversampling point D.

[0103] Further, the server may determine the second initial voxel data corresponding to the sampling point according to the voxel data of the target voxel points and the corresponding voxel weights. That is, calculate the second initial voxel data corresponding to the sampling point through the following formula:

[0104] V D2 =V D000 (1 - X D000 )(1 - Y D000 )(1 - Z D000 ) + V D001 (1 - X D001 )(1 - Y D001 )(1 - Z D001 ) + V D100 (1 - X D100 )(1 - Y D100 )(1 - Z D100 ) + V D101 (1 - X D101 )(1 - Y D101 )(1 - Z D101 )

[0105] In this embodiment, determining the tissue gradient and voxel data corresponding to the sampling point according to the initial voxel data of the sampling point may include: determining the tissue gradient and voxel data corresponding to the sampling point according to the first initial voxel data and the second initial voxel data.

[0106] Specifically, the server may calculate the tissue gradient for the first initial voxel data and the second initial voxel data based on the central difference algorithm or methods such as Sobel described above, and determine the tissue gradient and voxel data corresponding to the sampling point.

[0107] In one embodiment, determining the tissue gradient and voxel data corresponding to a sampling point based on the first initial voxel data and the second initial voxel data may include: determining a corresponding first tissue gradient based on the first initial voxel data; determining a corresponding second tissue gradient based on the second initial voxel data; determining a corresponding gradient angle according to the first tissue gradient and the second tissue gradient; determining whether the gradient angle is less than a preset threshold; when the gradient angle is less than the preset threshold, determining the first initial voxel data as the voxel data corresponding to the sampling point and determining the first tissue gradient as the tissue gradient corresponding to the sampling point; when the gradient angle is greater than or equal to the preset threshold, determining the second initial voxel data as the voxel data corresponding to the sampling point and determining the second tissue gradient as the tissue gradient corresponding to the sampling point.

[0108] In this embodiment, the server calculates the tissue gradients of the first initial voxel data and the second initial voxel data respectively according to the central difference algorithm or the Sobel method, and generates corresponding first and second tissue gradients.

[0109] Furthermore, the server can calculate the angle between the first tissue gradient and the second tissue gradient. Specifically, the server can perform a multiplication calculation on the first tissue gradient and the second tissue gradient to obtain the angle between the first tissue gradient and the second tissue gradient.

[0110] In this embodiment, after the server determines the gradient angle between the first tissue gradient and the second tissue gradient, it can determine whether the gradient angle is less than the preset threshold based on the preset threshold set in advance.

[0111] In this embodiment, when the gradient angle is less than the preset threshold, it indicates that the first tissue gradient is basically consistent with the boundary normal obtained by tissue segmentation. This means that when using the gradient of the first initial voxel data, the boundary display is smooth and also conforms to the real result. Similarly, when the gradient angle is greater than or equal to the preset threshold, it indicates that there is a deviation between the first tissue gradient and the actual segmentation boundary normal. When using the gradient of the first initial voxel data, the boundary surface will look uneven. In this case, using the gradient obtained by correcting the visibility of the tissue label obtained by tissue segmentation, that is, the second tissue gradient, will make the boundary look smoother. At this time, the second initial voxel data is determined as the voxel data corresponding to the sampling point, and the second tissue gradient is determined as the tissue gradient corresponding to the sampling point.

[0112] In the prior art, the normal interpolation process does not consider the visibility of voxel centers. The solution of the present application takes into account the visibility of voxel centers. If the voxel center is visible, the original voxel value of the voxel center is used, that is, the voxel data of the sampling point is determined by the voxel data of all the initial voxel points of the sampling points described above. If the voxel center is not visible, only the contribution of the voxel values of the visible voxel points to the sampling point is considered, that is, only the initial voxel points corresponding to the tissue label of the sampling point are determined as the target voxel points corresponding to the sampling point, and the voxel data of the sampling point is determined according to the voxel data of the target voxel points, and the subsequent voxel gradient calculation is performed, so that the determination of the voxel data of the sampling point combines the weights of each initial voxel point and considers the visibility of the initial voxel points, making the determination of the voxel data of the sampling point more accurate and improving the image quality of the multi-tissue volume image obtained by rendering.

[0113] Figure 4 Fig. shows a comparison diagram of volume images obtained by performing volume rendering on the same volume data. Figure 4 Among them, (a) is the volume image obtained by rendering based on the prior art. Figure 4 Among them, (b) is the volume image obtained by rendering based on the solution of the present application. It can be seen that the edges of the bone-attached blood vessels in the volume image rendered based on the solution of the present application become smoother, there are no obvious unevenness on the surface of the bone-attached blood vessels, the lighting effect is improved, and it looks smoother, greatly improving the image quality of the multi-tissue volume image obtained.

[0114] It should be understood that although Figure 2 the steps in the flowchart of Figure 2 are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover,

[0115] In one embodiment, as Figure 5 shown, a volume data rendering device is provided, including: a volume data acquisition module 100, a sampling point determination module 200, and a rendering module 300, where:

[0116] The volume data acquisition module 100 is configured to acquire the volume data to be rendered.

[0117] The sampling point determination module 200 is configured to determine sampling points.

[0118] A drawing module 300, configured to draw an organ based on the drawing parameters of each sampling point to obtain an organ image, where the drawing parameters include tissue labels and tissue gradients.

[0119] In one embodiment, the above device may further include:

[0120] An initial voxel point determination module, configured to determine a plurality of initial voxel points corresponding to a sampling point according to the positional relationship between the sampling point and each voxel point in the multi-organ.

[0121] A voxel weight determination module, configured to determine the voxel weights of each initial voxel point relative to the sampling point according to the coordinate positions of each initial voxel point and the coordinate position of the sampling point.

[0122] A drawing parameter determination module, configured to determine the drawing parameters corresponding to the sampling point based on the drawing parameters of each initial voxel point and each voxel weight.

[0123] In one embodiment, the drawing parameters may further include voxel data.

[0124] In this embodiment, the drawing parameter determination module may include:

[0125] A tissue label determination sub-module, configured to determine the tissue label corresponding to the sampling point based on the tissue labels of each initial voxel point.

[0126] An initial voxel data determination sub-module, configured to determine the initial voxel data corresponding to the sampling point according to the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight.

[0127] A tissue gradient and voxel data determination sub-module, configured to determine the tissue gradient and voxel data corresponding to the sampling point according to the initial voxel data of the sampling point.

[0128] In one embodiment, the initial voxel data determination sub-module may include:

[0129] A first initial voxel data unit, configured to determine the first initial voxel data corresponding to the sampling point according to the voxel data of each initial voxel point and the corresponding voxel weight.

[0130] A target voxel point determination unit, configured to determine the target voxel point corresponding to the sampling point according to the tissue label of the sampling point and the tissue labels of each initial voxel point.

[0131] A second initial voxel data determination unit, configured to determine the second initial voxel data corresponding to the sampling point according to the voxel data of each target voxel point and the corresponding voxel weight.

[0132] In this embodiment, the tissue gradient and voxel data determination sub-module is used to determine the tissue gradient and voxel data of corresponding sampling points according to the first initial voxel data and the second initial voxel data.

[0133] In one embodiment, the tissue gradient and voxel data determination sub-module may include:

[0134] The first tissue gradient determination unit is used to determine the corresponding first tissue gradient based on the first initial voxel data.

[0135] The second tissue gradient determination unit is used to determine the corresponding second tissue gradient based on the second initial voxel data.

[0136] The included angle determination unit is used to determine the corresponding gradient included angle according to the first tissue gradient and the second tissue gradient.

[0137] The judgment unit is used to judge whether the gradient included angle is less than a preset threshold; when the gradient included angle is less than the preset threshold, it is determined that the first initial voxel data is the voxel data of the corresponding sampling point, and the first tissue gradient is the tissue gradient of the corresponding sampling point; when the gradient included angle is greater than or equal to the preset threshold, it is determined that the second initial voxel data is the voxel data of the corresponding sampling point, and the second tissue gradient is the tissue gradient of the corresponding sampling point.

[0138] In one embodiment, the tissue label determination sub-module may include:

[0139] The proportion determination unit is used to determine the proportion of each tissue label according to the tissue labels of each initial voxel point.

[0140] The tissue label determination unit is used to determine that the tissue label with the largest proportion is the tissue label of the sampling point.

[0141] In one embodiment, the tissue drawing parameter may further include voxel data.

[0142] In this embodiment, the drawing module 300 may include:

[0143] The voxel point determination sub-module is used to determine the voxel points corresponding to at least two tissue bodies based on the tissue labels.

[0144] The drawing sub-module is used to draw the tissue bodies according to the voxel data and tissue gradients of the voxel points corresponding to at least two tissue bodies, and obtain the tissue body image.

[0145] For the specific limitations of the volume data rendering device, reference can be made to the limitations of the volume data rendering method in the foregoing text, which will not be elaborated here. Each module in the foregoing volume data rendering device can be implemented in whole or in part by software, hardware, and their combination. Each of the foregoing modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to each of the foregoing modules.

[0146] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 6 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as volume data, tissue rendering parameters, and multi-tissue volume images. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a volume data rendering method.

[0147] Those skilled in the art can understand that Figure 6 the structure shown in

[0148] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0149] In one of the embodiments, when the processor executes the computer program, it is implemented to determine the rendering parameters of each sampling point according to the rendering parameters of the voxel points corresponding to each sampling point, and the following steps can also be implemented: determining a plurality of initial voxel points corresponding to the sampling point according to the positional relationship between the sampling point and the voxel points in the volume data to be rendered; determining the voxel weights of each initial voxel point relative to the sampling point according to the coordinate positions of each initial voxel point and the coordinate position of the sampling point; and determining the rendering parameters of the corresponding sampling point based on the rendering parameters of each initial voxel point and each voxel weight.

[0150] In one embodiment, the rendering parameter may further include voxel data.

[0151] In this embodiment, when the processor executes the computer program, to determine the rendering parameter of the corresponding sampling point based on the rendering parameters of each initial voxel point and each voxel weight, it may include: determining the tissue label of the corresponding sampling point based on the tissue labels of each initial voxel point; determining the initial voxel data of the corresponding sampling point according to the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight; and determining the tissue gradient and voxel data of the corresponding sampling point according to the initial voxel data of the sampling point.

[0152] In one embodiment, when the processor executes the computer program, to determine the voxel data of the corresponding sampling point according to the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight, it may include: determining the first initial voxel data of the corresponding sampling point according to the voxel data of each initial voxel point and the corresponding voxel weight; determining the target voxel point of the corresponding sampling point according to the tissue label of the sampling point and the tissue labels of each initial voxel point; and determining the second initial voxel data of the corresponding sampling point according to the voxel data of each target voxel point and the corresponding voxel weight.

[0153] In this embodiment, when the processor executes the computer program, to determine the tissue gradient and voxel data of the corresponding sampling point according to the initial voxel data of the sampling point, it may include: determining the tissue gradient and voxel data of the corresponding sampling point according to the first initial voxel data and the second initial voxel data.

[0154] In one embodiment, when the processor executes the computer program, to determine the tissue gradient and voxel data of the corresponding sampling point according to the first initial voxel data and the second initial voxel data, it may include: determining the corresponding first tissue gradient based on the first initial voxel data; determining the corresponding second tissue gradient based on the second initial voxel data; determining the corresponding gradient angle according to the first tissue gradient and the second tissue gradient; determining whether the gradient angle is less than a preset threshold; when the gradient angle is less than the preset threshold, determining the first initial voxel data as the voxel data of the corresponding sampling point and determining the first tissue gradient as the tissue gradient of the corresponding sampling point; and when the gradient angle is greater than or equal to the preset threshold, determining the second initial voxel data as the voxel data of the corresponding sampling point and determining the second tissue gradient as the tissue gradient of the corresponding sampling point.

[0155] In one embodiment, when the processor executes the computer program, to determine the tissue label of the corresponding sampling point based on the tissue labels of each initial voxel point, it may include: determining the proportion of each tissue label based on the tissue labels of each initial voxel point; and determining the tissue label with the largest proportion as the tissue label of the sampling point.

[0156] In one embodiment, the rendering parameters may further include voxel data.

[0157] In this embodiment, when the processor executes the computer program, it realizes rendering of the tissue body according to the rendering parameters of each sampling point and each voxel point, and obtains a tissue body image, which may include: determining voxel points corresponding to at least two tissue bodies based on tissue labels; performing rendering of the tissue body according to the voxel data and tissue gradients of the voxel points corresponding to at least two tissue bodies to obtain a tissue body image.

[0158] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are realized: obtaining volume data to be rendered, where the volume data includes volume data of at least two tissue bodies; determining sampling points; performing rendering of the tissue body according to the rendering parameters of each sampling point to obtain a tissue body image, and the rendering parameters include tissue labels and tissue gradients.

[0159] In one of the embodiments, when the computer program is executed by the processor, the following steps may further be realized: determining a plurality of initial voxel points corresponding to the sampling points according to the positional relationship between the sampling points and the voxel points in the volume data to be rendered; determining the voxel weights of each initial voxel point relative to the sampling point according to the coordinate positions of each initial voxel point and the coordinate position of the sampling point; determining the rendering parameters corresponding to the sampling points based on the rendering parameters of each initial voxel point and each voxel weight.

[0160] In one embodiment, the rendering parameters may further include voxel data.

[0161] In this embodiment, when the computer program is executed by the processor, realizing determining the rendering parameters corresponding to the sampling points based on the rendering parameters of each initial voxel point and each voxel weight may include: determining the tissue label corresponding to the sampling point based on the tissue labels of each initial voxel point; determining the initial voxel data corresponding to the sampling point according to the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight; determining the tissue gradient and voxel data corresponding to the sampling point according to the initial voxel data of the sampling point.

[0162] In one embodiment, when the computer program is executed by the processor, realizing determining the voxel data corresponding to the sampling point according to the tissue label of the sampling point, the voxel data of each initial voxel point, and each voxel weight may include: determining the first initial voxel data corresponding to the sampling point according to the voxel data of each initial voxel point and the corresponding voxel weight; determining the target voxel points corresponding to the sampling point according to the tissue label of the sampling point and the tissue labels of each initial voxel point; determining the second initial voxel data corresponding to the sampling point according to the voxel data of each target voxel point and the corresponding voxel weight.

[0163] In this embodiment, when the computer program is executed by a processor, it realizes determining the tissue gradient and voxel data corresponding to a sampling point according to the initial voxel data of the sampling point, which may include: determining the tissue gradient and voxel data corresponding to the sampling point according to the first initial voxel data and the second initial voxel data.

[0164] In one embodiment, when the computer program is executed by a processor, it realizes determining the tissue gradient and voxel data corresponding to a sampling point according to the first initial voxel data and the second initial voxel data, which may include: determining the corresponding first tissue gradient based on the first initial voxel data; determining the corresponding second tissue gradient based on the second initial voxel data; determining the corresponding gradient angle according to the first tissue gradient and the second tissue gradient; judging whether the gradient angle is less than a preset threshold; when the gradient angle is less than the preset threshold, determining the first initial voxel data as the voxel data corresponding to the sampling point and determining the first tissue gradient as the tissue gradient corresponding to the sampling point; when the gradient angle is greater than or equal to the preset threshold, determining the second initial voxel data as the voxel data corresponding to the sampling point and determining the second tissue gradient as the tissue gradient corresponding to the sampling point.

[0165] In one embodiment, when the computer program is executed by a processor, it realizes determining the tissue label corresponding to a sampling point based on the tissue labels of each initial voxel point, which may include: determining the proportion of each tissue label according to the tissue labels of each initial voxel point; determining the tissue label with the largest proportion as the tissue label of the sampling point.

[0166] In one embodiment, the rendering parameter may further include voxel data.

[0167] In this embodiment, when the computer program is executed by a processor, it realizes performing volume rendering of tissues according to the tissue rendering parameters of each sampling point and each voxel point to obtain a tissue volume image, which may include: determining voxel points corresponding to at least two tissue volumes based on the tissue labels; performing rendering of the tissue volumes according to the voxel data and tissue gradients of the voxel points corresponding to the at least two tissue volumes to obtain a tissue volume image.

[0168] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0169] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0170] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for volume data rendering, characterized in that, The method includes: Obtaining volume data to be rendered, where the volume data includes volume data of at least two tissue bodies; Determining sampling points; Determining a plurality of initial voxel points corresponding to the sampling points according to the positional relationship between the sampling points and the voxel points in the volume data to be rendered; Determining the voxel weights of each of the initial voxel points relative to the sampling point according to the coordinate positions of each of the initial voxel points and the coordinate position of the sampling point; Determining the rendering parameters corresponding to the sampling points based on the rendering parameters of each of the initial voxel points and the voxel weights; Rendering the tissue bodies according to the rendering parameters of each of the sampling points to obtain a tissue body image, where the rendering parameters include tissue labels and tissue gradients.

2. The method according to claim 1, wherein The rendering parameters further include voxel data; The rendering the tissue bodies according to the rendering parameters of each of the sampling points to obtain a tissue body image includes: Determining the sampling points corresponding to the at least two tissue bodies based on the tissue labels; Rendering the tissue bodies according to the voxel data and tissue gradients of the sampling points corresponding to the at least two tissue bodies to obtain a tissue body image.

3. The method according to claim 1, characterized in that, The rendering parameters further include voxel data; The determining the rendering parameters corresponding to the sampling points based on the rendering parameters of each of the initial voxel points and the voxel weights includes: Determining the tissue label corresponding to the sampling point based on the tissue labels of each of the initial voxel points; Determining the initial voxel data corresponding to the sampling point according to the tissue label of the sampling point, the voxel data of each of the initial voxel points, and the voxel weights; Determining the tissue gradient and voxel data corresponding to the sampling point according to the initial voxel data of the sampling point.

4. The method according to claim 3, characterized in that The determining the initial voxel data corresponding to the sampling point according to the tissue label of the sampling point, the voxel data of each of the initial voxel points, and the voxel weights includes: Determining the first initial voxel data corresponding to the sampling point according to the voxel data of each of the initial voxel points and the corresponding voxel weights; Determining the target voxel points corresponding to the sampling point according to the tissue label of the sampling point and the tissue labels of each of the initial voxel points; Determining the second initial voxel data corresponding to the sampling point according to the voxel data of each of the target voxel points and the corresponding voxel weights; The determining the tissue gradient and voxel data corresponding to the sampling point according to the initial voxel data of the sampling point includes: Determining the tissue gradient and voxel data corresponding to the sampling point according to the first initial voxel data and the second initial voxel data.

5. The method according to claim 4, wherein The determining the tissue gradient and voxel data corresponding to the sampling point according to the first initial voxel data and the second initial voxel data includes: Determining the corresponding first tissue gradient based on the first initial voxel data; Determining the corresponding second tissue gradient based on the second initial voxel data; Determining the corresponding gradient angle according to the first tissue gradient and the second tissue gradient; Judging whether the gradient angle is less than a preset threshold; When the gradient angle is less than the preset threshold, determine the first initial voxel data as the voxel data corresponding to the sampling point, and determine the first tissue gradient as the tissue gradient corresponding to the sampling point; When the gradient angle is greater than or equal to the preset threshold, determine the second initial voxel data as the voxel data corresponding to the sampling point, and determine the second tissue gradient as the tissue gradient corresponding to the sampling point.

6. The method according to claim 3, characterized in that, The determining the tissue label corresponding to the sampling point based on the tissue labels of the respective initial voxel points includes: Based on the tissue labels of the respective initial voxel points, determine the proportion of each of the tissue labels; Determine the tissue label with the largest proportion as the tissue label of the sampling point.

7. A volume data rendering device, characterized in that, The apparatus includes: A volume data acquisition module, configured to acquire volume data to be rendered, where the volume data includes volume data of at least two tissue volumes; A sampling point determination module, configured to determine sampling points; An initial voxel point determination module, configured to determine a plurality of initial voxel points corresponding to the sampling point according to the positional relationship between the sampling point and the voxel points in the volume data to be rendered; A voxel weight determination module, configured to determine the respective voxel weights of the respective initial voxel points relative to the sampling point according to the coordinate positions of the respective initial voxel points and the coordinate position of the sampling point; A rendering parameter determination module, configured to determine the rendering parameters corresponding to the sampling point based on the rendering parameters of the respective initial voxel points and the respective voxel weights; A rendering module, configured to perform rendering of the tissue volume according to the rendering parameters of the respective sampling points to obtain a tissue volume image, where the rendering parameters include tissue labels and tissue gradients.

8. The device according to claim 7, characterized in that, The rendering parameters further include voxel data; The rendering parameter determination module includes: A tissue label determination sub-module, configured to determine the tissue label corresponding to the sampling point based on the tissue labels of the respective initial voxel points; An initial voxel data determination sub-module, configured to determine the initial voxel data corresponding to the sampling point according to the tissue label of the sampling point, the voxel data of the respective initial voxel points, and the respective voxel weights; A tissue gradient and voxel data determination sub-module, configured to determine the tissue gradient and voxel data corresponding to the sampling point according to the initial voxel data of the sampling point.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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