Method, system, device and medium for rendering medical images

Abstract

The application discloses a medical image rendering method, system, device and medium, and the rendering method comprises the following steps: acquiring first rendering parameters of a plurality of first medical images at different gray values according to a preset color table; acquiring maximum rendering parameters and minimum rendering parameters corresponding to different preset gray value intervals based on the first rendering parameters, so as to construct a corresponding target mapping relationship; acquiring a plurality of voxel physical value intervals corresponding to a target medical image; calculating a target gray value interval corresponding to the voxel physical value interval; acquiring a target rendering parameter according to the target gray value interval and the target mapping relationship; and performing tracking rendering on the target medical image according to the target rendering parameter. The mapping relationship corresponding to the gray value interval and the maximum and minimum rendering parameters is obtained through precalculation, the target rendering parameter is rapidly obtained by querying the mapping relationship during rendering, the speed of acquiring the rendering parameter can be improved, and the efficiency of rendering is further improved.

Description

Technical Field

[0001] This invention relates to the field of medical image rendering technology, and in particular to a method, system, device and medium for rendering medical images. Background Technology

[0002] Volumetric data obtained from imaging equipment such as CT (Computed Tomography) scans are three-dimensional images composed of several two-dimensional slice images. Similar to how two-dimensional images are composed of pixels, three-dimensional images are composed of x*y*z voxels. To allow doctors to more intuitively visualize the three-dimensional structure of organs and tissues, an increasing number of modern medical post-processing workstations are using ray tracing technology to physically render volumetric data. Rendering can take global illumination into account, providing more realistic and detailed three-dimensional images. To allow users to adjust parameters in real time and instantly view the results, progressive physically rendered volumetric data has also gradually become the mainstream technology.

[0003] The principle of progressive physical rendering for volume data is as follows: random rays are repeatedly emitted from each pixel position in the image to perform ray tracing, and the radiance of each calculated ray is accumulated and averaged. As the number of ray tracing iterations increases, the resulting HDR (High Dynamic Range) image gradually approaches the true result of ray integration. Each ray tracing iteration is called an iteration. In each iteration: first, based on the position of each pixel in the image, a ray is randomly emitted from the camera in the corresponding direction to the volume data. When the ray enters the volume data, distance sampling is performed on the volume data based on the grayscale value and corresponding color table. When the opacity of the volume data meets a specific condition, the ray will scatter at that position. The direct illumination at the current position is calculated, and the direction sampling is determined based on the material characteristics of the volume data at that location, using either a BRDF (Bidirectional Reflectance Distribution Function) or a phase function. The contribution of subsequent light paths is used to determine whether to continue distance sampling along the sampling direction and repeat the above process, or to terminate the process. Furthermore, when the ray exits the volume data or the number of scattering iterations reaches its maximum, the ray will also stop moving forward. After the ray stops moving forward, the final radiance of that ray is output.

[0004] In distance sampling, to improve sampling efficiency, compared to traditional Delta Tracking, existing technologies such as Decomposition Tracking use local maximum and minimum attenuation coefficients for distance sampling or transmittance calculation, increasing sampling effectiveness and improving computational efficiency. However, for medical volume data, which typically stores real physical values, grayscale values ​​need to be calculated using window width and level, and then opacity, attenuation coefficients, and albedo mapped using a color table. Since the color table is a non-linear mapping, calculating the local maximum and minimum attenuation coefficients after volume data is segmented requires first transforming the segmented physical value range using window width and level to obtain a grayscale value range, and then traversing the color table based on this range to obtain the maximum and minimum attenuation coefficients. Therefore, the maximum and minimum attenuation coefficients are closely related to window width and level, and the color table. Adjusting the window width and level or switching the color table requires recalculating the maximum and minimum attenuation coefficients, a time-consuming process that makes it difficult to guarantee real-time rendering when adjusting window width and level or switching the color table. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art, which requires recalculation of the maximum and minimum attenuation coefficients of local volume data when adjusting window width, window level or color table, resulting in long rendering time and difficulty in real-time rendering. The present invention provides a rendering method, system, device and medium for medical images.

[0006] The present invention solves the above-mentioned technical problems through the following technical solution:

[0007] This invention provides a method for rendering medical images, the rendering method comprising:

[0008] Based on the preset color table, obtain the first rendering parameters of several first medical images at different grayscale values;

[0009] Based on the first rendering parameters, the maximum and minimum rendering parameters corresponding to different preset grayscale value ranges are obtained to construct the corresponding target mapping relationship;

[0010] Obtain several voxel physical value intervals corresponding to the target medical image, wherein the voxel physical value intervals are used to characterize the range of physical values ​​of volume data in a local part of the target medical image;

[0011] The target grayscale value range corresponding to the voxel physical value range is calculated;

[0012] Based on the target grayscale value range and the target mapping relationship, obtain the target rendering parameters;

[0013] The target medical image is tracked and rendered according to the target rendering parameters.

[0014] In this scheme, the mapping relationship between grayscale value range and maximum and minimum rendering parameters is obtained through pre-calculation. During rendering, the target rendering parameters can be quickly obtained by querying this mapping relationship, which can improve the speed of obtaining rendering parameters and thus improve rendering efficiency.

[0015] Preferably, the step of obtaining the voxel physical value range of the target medical image includes:

[0016] The volume data of the target medical image is divided into several individual data groups;

[0017] Obtain the physical value range of the volume data for each of the volume data groups.

[0018] In this scheme, by dividing the volume data into several individual data groups, we can avoid the problem of too large a difference between the maximum and minimum rendering parameters, which would lead to too many invalid positions being sampled in the distance sampling. This can improve the efficiency of distance sampling and reduce the time consumption.

[0019] Preferably, the rendering method further includes:

[0020] The voxels in the volume data group are divided into several voxel groups, and different voxel groups correspond to different preset color tables.

[0021] Specifically, each voxel representing the same preset object in the volume data group is assigned to the same voxel group.

[0022] In this solution, by dividing the volumetric data group into several voxel groups and using different color tables to obtain rendering parameters for each voxel group, different tissues and organs in medical images can be distinguished more clearly, thus optimizing the rendering effect of medical images.

[0023] Preferably, the step of calculating the target grayscale value range corresponding to the voxel physical value range includes:

[0024] Get the current window width and window level information;

[0025] The corresponding target grayscale value range is calculated based on the voxel physical value range and the window width and window level information.

[0026] In this solution, by adjusting the window width and window level, the parts of interest in the medical image can be highlighted as needed, thus optimizing the rendering effect of the medical image.

[0027] Preferably, the rendering method further includes:

[0028] The target mapping relationship is stored in the preset memory of the medical image processing device, and the preset memory can be accessed through a two-dimensional index;

[0029] And / or,

[0030] The voxel physical value range is stored in the memory of the medical image processing device in a preset partition structure.

[0031] In this approach, by storing the target mapping relationship and voxel physical value range in the CPU (Central Processing Unit) or GPU (Graphics Processing Unit) and querying them during rendering, the rendering efficiency of medical images can be improved.

[0032] Preferably, the step of obtaining the target rendering parameters based on the target grayscale value range and the target mapping relationship includes:

[0033] Based on the target grayscale value range, determine the corresponding preset grayscale value range;

[0034] Based on the preset grayscale value range, the target mapping relationship in the preset memory is obtained to query the maximum rendering parameter and the minimum rendering parameter.

[0035] In this solution, the rendering efficiency of medical images can be improved by querying the corresponding maximum and minimum rendering parameters through pre-calculated target mapping relationships.

[0036] The present invention also provides a rendering system for medical images, the rendering system comprising:

[0037] The first rendering parameter acquisition module is used to acquire first rendering parameters of several first medical images at different grayscale values ​​according to a preset color table.

[0038] The mapping relationship construction module is used to obtain the maximum and minimum rendering parameters corresponding to different preset grayscale value ranges based on the first rendering parameters, so as to construct the corresponding target mapping relationship;

[0039] The physical value acquisition module is used to acquire several voxel physical value intervals corresponding to the target medical image, wherein the voxel physical value intervals are used to characterize the range of physical values ​​of local volume data of the target medical image;

[0040] The grayscale value acquisition module is used to calculate the target grayscale value range corresponding to the voxel physical value range;

[0041] The target rendering parameter acquisition module is used to acquire target rendering parameters based on the target grayscale value range and the target mapping relationship;

[0042] The rendering control module is used to perform tracking rendering of the target medical image according to the target rendering parameters.

[0043] In this scheme, the mapping relationship between grayscale value range and maximum and minimum rendering parameters is obtained through pre-calculation. During rendering, the target rendering parameters can be quickly obtained by querying this mapping relationship, which can improve the speed of obtaining rendering parameters and thus improve rendering efficiency.

[0044] Preferably, the physical value acquisition module includes:

[0045] A volume data group acquisition unit is used to divide the volume data of the target medical image into several individual data groups;

[0046] The physical value acquisition unit is used to acquire the physical value range of the volume data of each volume data group.

[0047] In this scheme, by dividing the volume data into several individual data groups, we can avoid the problem of too large a difference between the maximum and minimum rendering parameters, which would lead to too many invalid positions being sampled in the distance sampling. This can improve the efficiency of distance sampling and reduce the time consumption.

[0048] Preferably, the rendering system further includes:

[0049] The voxel group acquisition module divides the voxels in the voxel data group into several voxel groups, and different voxel groups correspond to different preset color tables.

[0050] Specifically, each voxel representing the same preset object in the volume data group is assigned to the same voxel group.

[0051] In this solution, by dividing the volumetric data group into several voxel groups and using different color tables to obtain rendering parameters for each voxel group, different tissues and organs in medical images can be distinguished more clearly, thus optimizing the rendering effect of medical images.

[0052] Preferably, the grayscale value acquisition module includes:

[0053] The window width and window level acquisition unit is used to acquire the current window width and window level information;

[0054] The grayscale value acquisition unit is used to calculate the corresponding target grayscale value range based on the voxel physical value range and the window width and window level information.

[0055] In this solution, by adjusting the window width and window level, the parts of interest in the medical image can be highlighted as needed, thus optimizing the rendering effect of the medical image.

[0056] Preferably, the rendering system further includes:

[0057] The first storage module is used to store the target mapping relationship in a preset memory of the medical image processing device, and the preset memory can be accessed through a two-dimensional index;

[0058] And / or,

[0059] The second storage module is used to store the voxel physical value range in the memory of the medical image processing device in a preset partition structure.

[0060] In this approach, by storing the target mapping relationship and voxel physical value range in the CPU or GPU and querying them during rendering, the rendering efficiency of medical images can be improved.

[0061] Preferably, the target rendering parameter acquisition module includes:

[0062] A preset interval acquisition unit is used to determine the corresponding preset gray value interval based on the target gray value interval.

[0063] The rendering parameter acquisition unit is used to obtain the target mapping relationship in the preset memory according to the preset grayscale value range, so as to query the maximum rendering parameter and the minimum rendering parameter.

[0064] In this solution, the rendering efficiency of medical images can be improved by querying the corresponding maximum and minimum rendering parameters through pre-calculated target mapping relationships.

[0065] The present invention also provides a medical image processing device, which includes the above-mentioned medical image rendering system.

[0066] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and used to run on the processor, wherein the processor executes the computer program to implement the above-described method for rendering medical images.

[0067] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-described method for rendering medical images.

[0068] Based on common knowledge in the field, the preferred conditions can be combined arbitrarily to obtain various preferred embodiments of the present invention.

[0069] The positive and progressive effects of this invention are as follows: by pre-calculating the mapping relationship between grayscale value ranges and maximum and minimum rendering parameters, and storing it in the CPU or GPU in the form of a two-dimensional texture or buffer, the target rendering parameters can be quickly obtained by querying the mapping relationship during rendering, which can improve the speed of obtaining rendering parameters, effectively simplify the existing rendering process, and improve rendering efficiency. Attached Figure Description

[0070] Figure 1This is a first flowchart of the medical image rendering method according to Embodiment 1 of the present invention;

[0071] Figure 2 This is a second flowchart of the medical image rendering method according to Embodiment 1 of the present invention;

[0072] Figure 3 This is the third flowchart of the medical image rendering method according to Embodiment 1 of the present invention;

[0073] Figure 4 This is the fourth flowchart of the medical image rendering method according to Embodiment 1 of the present invention;

[0074] Figure 5 This is a schematic diagram of the first module of the medical image rendering system according to Embodiment 1 of the present invention;

[0075] Figure 6 This is a schematic diagram of the second module of the medical image rendering system according to Embodiment 1 of the present invention;

[0076] Figure 7 This is a schematic diagram of the structure of the electronic device according to Embodiment 4 of the present invention. Detailed Implementation

[0077] The present invention will be further illustrated by way of embodiments below, but the present invention is not limited to the scope of the embodiments described herein.

[0078] Example 1

[0079] This embodiment provides a method for rendering medical images, such as... Figure 1 As shown, the rendering method includes:

[0080] S1. Based on the preset color table, obtain the first rendering parameters of several first medical images at different grayscale values;

[0081] The color table uses a user-defined piecewise function to define rendering parameters such as opacity for grayscale values ​​within the range of 0.0-1.0. Grayscale values ​​are floating-point numbers between 0.0 and 1.0 for easy interpolation. Specifically, for grayscale values ​​without set opacity, interpolation is performed based on the nearest nearest set opacity. Step S1 obtains the rendering parameters corresponding to different grayscale values.

[0082] S2. Based on the first rendering parameters, obtain the maximum and minimum rendering parameters corresponding to different preset grayscale value ranges to construct the corresponding target mapping relationship;

[0083] Step S2 is the key point of this invention. It involves pre-calculating the maximum and minimum rendering parameters for different preset grayscale value ranges to construct a target mapping relationship. Taking a 256*256 resolution as an example, the grayscale value range is a two-dimensional quantity (a / 255, b / 255), where a and b are both in the range of 0-255. For example, for the grayscale value range (3 / 255, 33 / 255), the rendering parameters corresponding to each grayscale value in the range can be obtained by traversing the color table, and then the maximum rendering parameter m and the minimum rendering parameter n can be obtained. The resulting target mapping relationship is a two-dimensional to two-dimensional mapping relationship from (a / 255, b / 255) to (m, n). The higher the resolution in the target mapping relationship, the more mapping values ​​are stored, and the more accurate the result.

[0084] S3. Obtain several voxel physical value intervals corresponding to the target medical image, wherein the voxel physical value interval is used to characterize the range of physical values ​​of the volume data of the local part of the target medical image.

[0085] If the volumetric data of a medical image is not very uniform, the overall maximum and minimum rendering parameters may differ significantly. Therefore, the volumetric data is usually divided into several blocks. For example, a 512*512*512 volumetric data can be divided into 32*32*32 blocks, each corresponding to 16*16*16 voxels. The voxel physical value range represents the range of physical values ​​corresponding to the local volumetric data of that 16*16*16 block.

[0086] S4. Calculate the target grayscale value range corresponding to the voxel physical value range;

[0087] Based on the window width and window level, the physical values ​​of the volume data are converted into grayscale values ​​of 0.0-1.0. For physical values ​​outside the window width, the boundary values ​​of 0.0 or 1.0 are taken.

[0088] S5. Obtain the target rendering parameters based on the target grayscale value range and the target mapping relationship;

[0089] For example, at a resolution of 256, for the grayscale value range of (0.01-0.02), 0.01*255 rounded down is 2, and 0.02*255 rounded up is 5. By querying the value corresponding to (2 / 255, 5 / 255) in the mapping relationship, the maximum and minimum rendering parameters can be obtained.

[0090] S6. Based on the target rendering parameters, perform tracking rendering of the target medical image.

[0091] For example, the rendering parameter is opacity A, which characterizes the proportion of light reduction over a certain distance, and the attenuation coefficient μ is calculated based on the opacity. t The relationship between the two is μ t= -ln(1-Α) / S, where S is a preset distance constant. Finally, rendering calculations are performed based on the attenuation coefficient.

[0092] In this scheme, the mapping relationship between grayscale value range and maximum and minimum rendering parameters is obtained through pre-calculation. During rendering, the target rendering parameters can be quickly obtained by querying this mapping relationship, which can improve the speed of obtaining rendering parameters and thus improve rendering efficiency.

[0093] In a feasible solution, such as Figure 2 As shown, step S3 includes:

[0094] S31. Divide the volumetric data of the target medical image into several individual data groups;

[0095] S32. Obtain the physical value range of the volume data for each volume data group.

[0096] For example, a volumetric dataset of 512*512*512 can be divided into 32*32*32 individual data groups, with each data group corresponding to 16*16*16 voxels. The voxel physical value range represents the range of physical values ​​corresponding to the local volumetric dataset of that 16*16*16 voxel.

[0097] In this scheme, by dividing the volume data into several individual data groups, we can avoid the problem of too large a difference between the maximum and minimum rendering parameters, which would lead to too many invalid positions being sampled in the distance sampling. This can improve the efficiency of distance sampling and reduce the time consumption.

[0098] In one feasible solution, the rendering method also includes:

[0099] The voxels in the volume data group are divided into several voxel groups, and different voxel groups correspond to different preset color tables.

[0100] In this process, each voxel representing the same preset object in the volume data group is grouped into the same voxel group.

[0101] For certain special organs and tissues, such as blood vessels and the heart, the scanned CT values ​​are similar to those of adjacent soft tissues, resulting in similar colors and opacities that make them indistinguishable. Therefore, additional information is needed to classify them. This scheme divides voxels into several voxel groups and uses different color tables for different voxel groups to differentiate them. Voxels of the same tissue or organ are grouped into the same voxel group.

[0102] During rendering, the maximum and minimum rendering parameters are queried for each voxel group, and then the maximum and minimum rendering parameters of the entire voxel data group are obtained.

[0103] In this solution, by dividing the volumetric data group into several voxel groups and using different color tables to obtain rendering parameters for each voxel group, different tissues and organs in medical images can be distinguished more clearly, thus optimizing the rendering effect of medical images.

[0104] In a feasible solution, such as Figure 3 As shown, step S4 includes:

[0105] S41. Obtain the current window width and window level information;

[0106] S42. Calculate the corresponding target grayscale value range based on the voxel physical value range and window width and window level information.

[0107] Adjusting the window width and level increases the opacity of the region of interest in a medical image and decreases the opacity of the region of no interest, thus displaying only the region of interest. Based on the window width and level, the physical values ​​of voxels are converted into grayscale values ​​between 0.0 and 1.0. Physical values ​​smaller than the selected window width and level are set to a grayscale value of 0.0, and physical values ​​larger than the selected window width and level are set to a grayscale value of 1.0.

[0108] In this solution, by adjusting the window width and window level, the parts of interest in the medical image can be highlighted as needed, thus optimizing the rendering effect of the medical image.

[0109] In one feasible solution, the rendering method also includes:

[0110] The target mapping relationship is stored in the preset memory of the medical image processing device, which can be accessed through a two-dimensional index;

[0111] And / or,

[0112] The voxel physical value range is stored in the memory of the medical image processing device in a preset partition structure.

[0113] Specifically, the target mapping relationship can be stored in the CPU or GPU as a two-dimensional texture or buffer. The maximum and minimum physical value information of the voxel physical value range can be stored in the CPU or GPU in the form of a block-like partitioned structure.

[0114] In this approach, by storing the target mapping relationship and voxel physical value range in the CPU or GPU and querying them during rendering, the rendering efficiency of medical images can be improved.

[0115] In a feasible solution, such as Figure 4 As shown, step S5 includes:

[0116] S51. Determine the corresponding preset grayscale value range based on the target grayscale value range;

[0117] S52. Based on the preset grayscale value range, obtain the target mapping relationship in the preset memory to query the maximum and minimum rendering parameters.

[0118] For example, for the target grayscale value range (0.01-0.02), 0.01*255 rounded down is 2, and 0.02*255 rounded up is 5. The corresponding preset grayscale value range is (2 / 255, 5 / 255). By querying the value corresponding to (2 / 255, 5 / 255) in the target mapping relationship, the maximum and minimum rendering parameters can be obtained. Of course, the higher the resolution of the target mapping relationship, the closer the target grayscale value range will be to the preset grayscale value range, and the more accurate the result will be.

[0119] In this solution, the rendering efficiency of medical images can be improved by querying the corresponding maximum and minimum rendering parameters through pre-calculated target mapping relationships.

[0120] The implementation process of the medical image rendering method provided in this embodiment is described below using a specific implementation method:

[0121] (1) Pre-calculate the color table or read the pre-calculation results from the file, use the gray value range as two-dimensional coordinates, use the maximum and minimum attenuation coefficients within the gray value range as values, and store them in the form of two-dimensional textures or buffers in the CPU or GPU.

[0122] (2) The maximum and minimum physical values ​​of the local statistical data are stored in the CPU or GPU in the form of a block-like partitioned structure;

[0123] (3) Convert the maximum and minimum physical values ​​of the local volume data to gray values ​​through window width and window level, and query the local volume data by nearest neighbor interpolation or by obtaining the buffer index based on the maximum and minimum gray values ​​in the two-dimensional texture to obtain the maximum and minimum attenuation coefficients of the local volume data.

[0124] Depending on performance and storage requirements, the attenuation coefficient can be pre-calculated in the CPU or GPU, stored in the partition structure, and then queried and used in the main rendering process to adjust the window width and window level before updating; or the window width and window level can be transformed in real time in the ray tracing process and the maximum and minimum attenuation coefficients can be obtained from the texture or buffer.

[0125] (4) Decomposition Tracking is performed based on the local maximum and minimum attenuation coefficients.

[0126] The medical image rendering method provided in this embodiment obtains the mapping relationship between grayscale value range and maximum and minimum rendering parameters through pre-calculation, and stores it in the CPU or GPU in the form of two-dimensional texture or buffer. During rendering, the target rendering parameters can be quickly obtained by querying the mapping relationship, which can improve the speed of obtaining rendering parameters and thus improve rendering efficiency.

[0127] Example 2

[0128] This embodiment provides a medical image rendering system, such as... Figure 5 As shown, the rendering system includes:

[0129] The first rendering parameter acquisition module 1 is used to acquire first rendering parameters of several first medical images at different grayscale values ​​according to a preset color table.

[0130] The mapping relationship construction module 2 is used to obtain the maximum and minimum rendering parameters corresponding to different preset grayscale value ranges based on the first rendering parameters, so as to construct the corresponding target mapping relationship;

[0131] The physical value acquisition module 3 is used to acquire several voxel physical value intervals corresponding to the target medical image, wherein the voxel physical value interval is used to characterize the range of physical values ​​of local volume data of the target medical image;

[0132] Gray value acquisition module 4 is used to calculate the target gray value range corresponding to the voxel physical value range;

[0133] The target rendering parameter acquisition module 5 is used to acquire target rendering parameters based on the target grayscale value range and the target mapping relationship;

[0134] Rendering control module 6 is used to perform tracking rendering of the target medical image based on the target rendering parameters.

[0135] In a feasible solution, such as Figure 6 As shown, the physical value acquisition module 3 includes:

[0136] The volume data acquisition unit 301 is used to divide the volume data of the target medical image into several individual data groups;

[0137] The physical value acquisition unit 302 is used to acquire the physical value range of volume data for each volume data group.

[0138] In one feasible embodiment, the rendering system also includes:

[0139] The voxel group acquisition module 7 divides the voxels in the voxel data group into several voxel groups, and different voxel groups correspond to different preset color tables.

[0140] In this process, each voxel representing the same preset object in the volume data group is grouped into the same voxel group.

[0141] In one feasible solution, the grayscale value acquisition module 4 includes:

[0142] Window width and window position acquisition unit 41 is used to acquire the current window width and window position information;

[0143] The grayscale value acquisition unit 42 is used to calculate the corresponding target grayscale value range based on the voxel physical value range and window width and window level information.

[0144] In one feasible embodiment, the rendering system also includes:

[0145] The first storage module 8 is used to store the target mapping relationship into the preset memory of the medical image processing device. The preset memory can be accessed through a two-dimensional index.

[0146] And / or,

[0147] The second storage module 9 is used to store the voxel physical value range in the memory of the medical image processing device in a preset partition structure.

[0148] In one feasible solution, the target rendering parameter acquisition module 5 includes:

[0149] The preset range acquisition unit 51 is used to determine the corresponding preset gray value range based on the target gray value range.

[0150] The rendering parameter acquisition unit 52 is used to obtain the target mapping relationship in the preset memory according to the preset grayscale value range, so as to query the maximum rendering parameter and the minimum rendering parameter.

[0151] Since the medical image rendering system provided in this embodiment is based on the same principle as the medical image rendering method provided in Embodiment 1, it will not be described again here.

[0152] The medical image rendering system provided in this embodiment obtains the mapping relationship between grayscale value range and maximum and minimum rendering parameters through pre-calculation, and stores it in the CPU or GPU in the form of two-dimensional texture or buffer. During rendering, the target rendering parameters can be quickly obtained by querying the mapping relationship, which can improve the speed of obtaining rendering parameters and thus improve rendering efficiency.

[0153] Example 3

[0154] This embodiment provides a medical image processing device, which includes the aforementioned medical image rendering system.

[0155] The medical image processing device provided in this embodiment, by integrating the aforementioned medical image rendering system, improves the speed of medical image processing and has better product performance.

[0156] Example 4

[0157] This embodiment provides an electronic device. Figure 7 This is a schematic diagram of the electronic device. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the medical image rendering method of Embodiment 1. Figure 7 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0158] like Figure 7 As shown, the electronic device 30 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0159] Bus 33 includes a data bus, an address bus, and a control bus.

[0160] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0161] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0162] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32, such as the medical image rendering method of Embodiment 1 of this disclosure.

[0163] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, the model-generating device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. Figure 7As shown, network adapter 36 communicates with other modules of the model-generated device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0164] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0165] Example 5

[0166] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the medical image rendering method of Embodiment 1.

[0167] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0168] In a possible implementation, the present invention can also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to execute the medical image rendering method of Embodiment 1.

[0169] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0170] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A method for rendering medical images, characterized in that, The rendering method includes: Based on the preset color table, obtain the first rendering parameters of several first medical images at different grayscale values; Based on the first rendering parameters, the maximum and minimum rendering parameters corresponding to different preset grayscale value ranges are obtained to construct the corresponding target mapping relationship; and the target mapping relationship is stored in the preset memory of the medical image processing device. Obtain several voxel physical value intervals corresponding to the target medical image, wherein the voxel physical value intervals are used to characterize the range of physical values ​​of volume data in a local part of the target medical image; Get the current window width and window level information; Based on the voxel physical value range and the window width and window level information, the corresponding target gray value range is calculated; The target rendering parameters are obtained by querying the target mapping relationship in the preset memory according to the target grayscale value range; The target medical image is tracked and rendered according to the target rendering parameters.

2. The medical image rendering method as described in claim 1, characterized in that, The step of obtaining the voxel physical value range of the target medical image includes: The volume data of the target medical image is divided into several individual data groups; Obtain the physical value range of the volume data for each of the volume data groups.

3. The medical image rendering method as described in claim 2, characterized in that, The rendering method also includes: The voxels in the volume data group are divided into several voxel groups, and different voxel groups correspond to different preset color tables. Specifically, each voxel representing the same preset object in the volume data group is assigned to the same voxel group.

4. The medical image rendering method as described in claim 1, characterized in that, The rendering method also includes: The preset memory can be accessed through a two-dimensional index; And / or, The voxel physical value range is stored in the memory of the medical image processing device in a preset partition structure.

5. The medical image rendering method as described in claim 4, characterized in that, The step of querying the target mapping relationship in the preset memory based on the target grayscale value range to obtain the target rendering parameters includes: Based on the target grayscale value range, determine the corresponding preset grayscale value range; Based on the preset grayscale value range, the target mapping relationship in the preset memory is obtained to query the maximum rendering parameter and the minimum rendering parameter.

6. A medical image rendering system, characterized in that, The rendering system includes: The first rendering parameter acquisition module is used to acquire first rendering parameters of several first medical images at different grayscale values ​​according to a preset color table. The mapping relationship construction module is used to obtain the maximum and minimum rendering parameters corresponding to different preset grayscale value ranges based on the first rendering parameters, so as to construct the corresponding target mapping relationship; and store the target mapping relationship in the preset memory of the medical image processing device. The physical value acquisition module is used to acquire several voxel physical value intervals corresponding to the target medical image, wherein the voxel physical value intervals are used to characterize the range of physical values ​​of local volume data of the target medical image; The grayscale value acquisition module is used to acquire the current window width and window level information; and to calculate the corresponding target grayscale value range based on the voxel physical value range and the window width and window level information. The target rendering parameter acquisition module is used to query the target mapping relationship in the preset memory according to the target grayscale value range to obtain the target rendering parameters; The rendering control module is used to perform tracking rendering of the target medical image according to the target rendering parameters.

7. A medical image processing device, characterized in that, The medical image processing device includes the medical image rendering system as described in claim 6.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the medical image rendering method according to any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the medical image rendering method according to any one of claims 1-5.

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