WO2020168696A1 - 基于VRDS 4D医学影像的动脉与静脉Ai处理方法及产品 - Google Patents

基于VRDS 4D医学影像的动脉与静脉Ai处理方法及产品 Download PDF

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WO2020168696A1
WO2020168696A1 PCT/CN2019/101158 CN2019101158W WO2020168696A1 WO 2020168696 A1 WO2020168696 A1 WO 2020168696A1 CN 2019101158 W CN2019101158 W CN 2019101158W WO 2020168696 A1 WO2020168696 A1 WO 2020168696A1
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data
vein
target
medical image
artery
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PCT/CN2019/101158
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English (en)
French (fr)
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李斯图尔特平
李戴维伟
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未艾医疗技术(深圳)有限公司
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Priority to EP19915864.3A priority Critical patent/EP3929933A4/en
Priority to AU2019430854A priority patent/AU2019430854B2/en
Priority to US17/432,494 priority patent/US20220172823A1/en
Publication of WO2020168696A1 publication Critical patent/WO2020168696A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/40—Image enhancement or restoration using histogram techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/64—Analysis of geometric attributes of convexity or concavity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10076—4D tomography; Time-sequential 3D tomography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Definitions

  • This application relates to the technical field of medical imaging devices, and in particular to a method and product for processing arteries and veins Ai based on VRDS 4D medical images.
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • DTI Diffusion Tensor Imaging
  • PET Positron Emission Computed Tomography
  • the embodiments of the present application provide a method and product for processing arteries and veins Ai based on VRDS 4D medical imaging, in order to improve the accuracy and efficiency of arterial and vein imaging by medical imaging devices.
  • an embodiment of the present application provides a method for processing arteries and veins Ai based on VRDS 4D medical images, which is applied to a medical imaging device; the method includes:
  • the target medical image data includes at least a data set of blood vessels at the target site.
  • the blood vessel data set includes a data set of arteries and a data set of veins.
  • the first data in the data set of the artery and the second data in the data set of the vein are independent of each other, the first data is data associated with the intersection position of the artery and the vein, and the second data Is the data associated with the intersection position, the data set of the artery is the result of the transfer function of the cube space of the arterial surface and the tissue structure inside the artery, and the data set of the vein is the surface of the vein and the The transfer function result of the cubic space of the tissue structure inside the vein;
  • the embodiments of the present application provide an arterial and vein Ai processing device based on VRDS Ai 4D medical image, which is applied to a medical imaging device;
  • the arterial and vein Ai processing device based on VRDS Ai 4D medical image includes a processing unit and Communication unit, where
  • the processing unit is configured to determine a bitmap BMP data source according to multiple scanned images of the target part of the target user; and to generate target medical image data according to the BMP data source, and the target medical image data includes at least the The data set of the blood vessel of the target part, the data set of the blood vessel includes the data set of the artery and the data set of the vein, and the first data in the data set of the artery and the second data of the data set of the vein are mutually Independently, the first data is data associated with the intersection position of the artery and the vein, the second data is data associated with the intersection position, and the data set of the artery is the arterial surface and The transfer function result of the cube space of the tissue structure inside the artery, and the data set of the vein is the transfer function result of the cube space of the vein surface and the tissue structure inside the vein; and used to pass the communication unit Perform 4D medical imaging according to the target medical image data, wherein the intersection position of the artery and the vein presents an image effect of overall separation.
  • an embodiment of the present application provides a medical imaging device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured by the above Executed by a processor, the above-mentioned program includes instructions for executing steps in any method of the first aspect of the embodiments of the present application.
  • an embodiment of the present application provides a computer-readable storage medium, wherein the foregoing computer-readable storage medium stores a computer program for electronic data exchange, wherein the foregoing computer program enables a computer to execute In one aspect, some or all of the steps described in any method.
  • embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute For example, some or all of the steps described in any method of the first aspect.
  • the computer program product may be a software installation package.
  • the medical imaging device first determines the bitmap BMP data source based on the multiple scanned images of the target part of the target user, and secondly, generates the target medical image data based on the BMP data source, and finally, according to the target medical image
  • the data is subjected to 4D medical imaging, where the target medical image data includes at least a data set of blood vessels at the target site.
  • the blood vessel data set includes an artery data set and a vein data set, and the first data in the arterial data set and the vein
  • the second data of the data set are independent of each other.
  • the first data is the data associated with the intersection position of the artery and the vein, and the second data is the data associated with the intersection position.
  • the data set of the artery is the tissue structure of the artery surface and the inside of the artery.
  • the result of the transfer function of the cube space, the data set of the vein is the result of the cube space of the vein surface and the tissue structure inside the vein, and the intersection of the artery and the vein presents an overall separated image effect. It can be seen that the medical imaging device of this application can The 4D medical image display based on the original scanned image is helpful to improve the accuracy and efficiency of artery and vein imaging.
  • FIG. 1 is a schematic structural diagram of a VRDS Ai 4D medical image intelligent analysis and processing system provided by an embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a method for processing arteries and veins Ai based on VRDS 4D medical images according to an embodiment of the present application;
  • FIG. 3 is a schematic structural diagram of a medical imaging device provided by an embodiment of the present application.
  • FIG. 4 is a block diagram of the functional unit composition of an arterial and vein Ai processing device based on VRDS Ai 4D medical imaging according to an embodiment of the present application.
  • the medical imaging devices involved in the embodiments of this application refer to various instruments that use various media as information carriers to reproduce the internal structure of the human body as images.
  • the image information and the actual structure of the human body have spatial and temporal distributions.
  • DICOM data refers to the original image file data collected by medical equipment that reflects the internal structural characteristics of the human body. It can include electronic computed tomography CT, magnetic resonance MRI, diffusion tensor imaging DTI, and positron emission computed tomography PET-
  • image source refers to the Texture2D/3D image volume data generated by analyzing the original DICOM data.
  • VRDS refers to the Virtual Reality Doctor system (VRDS for short).
  • FIG. 1 is a schematic structural diagram of a VRDS Ai 4D medical image intelligent analysis and processing system 100 based on an embodiment of the present application.
  • the system 100 includes a medical imaging device 110 and a network database 120.
  • the medical imaging device 110 can Including the local medical imaging device 111 and/or the terminal medical imaging device 112.
  • the local medical imaging device 111 or the terminal medical imaging device 112 is used to connect the arteries based on the VRDS Ai 4D medical image based on the original DICOM data.
  • the four-dimensional separated body rendering of human arteries and veins is carried out to achieve four-dimensional three-dimensional effects (the four-dimensional medical image specifically refers to the medical image including the internal spatial structural characteristics and external spatial structural characteristics of the displayed tissue.
  • the internal spatial structure feature means that the slice data inside the tissue has not been lost, that is, the medical imaging device can present the internal structure of target organs, blood vessels and other tissues.
  • the external spatial structure feature refers to the environmental characteristics between tissues, including tissues and tissues.
  • the spatial location characteristics (including intersection, interval, fusion), etc., such as the edge structure characteristics of the intersection position between the kidney and the artery, etc.), the local medical imaging device 111 can also be used to compare images relative to the terminal medical imaging device 112
  • the source data is edited to form the transfer function result of the four-dimensional human body image.
  • the transfer function result can include the transfer function result of the surface of the internal organs and the tissue structure in the internal organs, and the transfer function result of the cube space, as required by the transfer function Cube edit box and arc edit array quantity, coordinates, color, transparency and other information.
  • the network database 120 may be, for example, a cloud server.
  • the network database 120 is used to store the image source generated by analyzing the original DICOM data and the transfer function result of the four-dimensional human body image edited by the local medical imaging device 111.
  • the image source may be from multiple sources.
  • a local medical imaging device 111 to realize interactive diagnosis of multiple doctors.
  • HMDS Head mounted Displays Set
  • the operating actions refer to the user’s actions through the medical imaging device.
  • An external intake device such as a mouse, keyboard, etc., controls the operation of the four-dimensional human body image to achieve human-computer interaction.
  • the operation action includes at least one of the following: (1) Change the color and/or of a specific organ/tissue Transparency, (2) positioning zoom view, (3) rotating view, realizing multi-view 360-degree observation of four-dimensional human body image, (4) "entering" human organs to observe internal structure, real-time clipping effect rendering, (5) moving up and down view.
  • FIG. 2 is a schematic flowchart of a method for processing arteries and veins Ai based on VRDS 4D medical imaging according to an embodiment of the present application, which is applied to the medical imaging device described in FIG. 1;
  • Artery and vein Ai processing methods based on VRDS 4D medical images include:
  • the medical imaging device determines a bitmap BMP data source according to multiple scanned images of the target part of the target user;
  • the target part may be a kidney, for example.
  • the scan image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • the medical imaging device determines the bitmap BMP data source according to multiple scanned images associated with the target organ of the target user, including: the medical imaging device acquires the internal body of the target user collected by medical equipment A plurality of scanned images with structural characteristics; at least one scanned image containing the target part is selected from the multiple scanned images, and the at least one scanned image is used as the medical digital imaging and communication DICOM data of the target user;
  • the DICOM data is parsed to generate the image source of the target user, the image source includes texture 2D/3D image volume data;
  • the second preset processing is performed on the image source to obtain the BMP data source, the second preset
  • the processing includes at least one of the following operations: VRDS limited contrast adaptive histogram equalization, hybrid partial differential denoising, and VRDS Ai elastic deformation processing.
  • the medical imaging device will filter the scanned images to obtain DICOM data, and then analyze to obtain Texture 2D/3D image volume data, and finally process at least one of equalization, denoising, and elastic deformation, and finally obtain conformity BMP data source required by medical imaging.
  • the VRDS limited contrast adaptive histogram equalization includes: regional noise ratio limiting, global contrast limiting; dividing the local histogram of the image source into multiple partitions, and for each partition, according to the The slope of the cumulative histogram of the neighborhood of the partition determines the slope of the transformation function, and the degree of contrast magnification around the pixel value of the partition is determined according to the gradient of the transformation function, and then the limit cropping process is performed according to the degree of contrast magnification to produce effective
  • the distribution of the histogram also produces effective and usable neighborhood size values, and these cropped parts of the histogram are evenly distributed to other areas of the histogram.
  • the hybrid partial differential denoising includes: different from Gaussian low-pass filtering (indiscriminately weakening the high-frequency components of the image, denoising will also produce image edge blurring), the isoilluminance formed by objects in natural images
  • the line (including the edge) should be a smooth and smooth curve, that is, the absolute value of the curvature of these isoilluminance lines should be small enough.
  • the design is achieved through VRDS Ai curvature drive and VRDS Ai high-order hybrid denoising, which can protect the edge of the image and avoid the step effect in the smoothing process.
  • the hybrid partial differential denoising model is achieved through VRDS Ai curvature drive and VRDS Ai high-order hybrid denoising, which can protect the edge of the image and avoid the step effect in the smoothing process.
  • the VRDS Ai elastic deformation processing includes: superimposing positive and negative random distances on the original dot matrix to form a difference position matrix, and then the grayscale at each difference position forms a new dot matrix, which can realize the distortion of the image Deform, and then rotate, distort, and translate the image.
  • the medical imaging device obtains the BMP data source by processing the original scanned image data, which increases the amount of information of the original data, and increases the depth dimension information, and finally obtains data that meets the requirements of 4D medical image display.
  • the medical imaging device generates target medical image data according to the BMP data source, where the target medical image data includes at least a data set of blood vessels at the target site, and the blood vessel data set includes arterial data
  • the data set of the set and the vein, and the first data in the data set of the artery and the second data of the data set of the vein are independent of each other, and the first data is the intersection position with the artery and the vein Associated data, the second data is the data associated with the intersection position, the data set of the artery is the result of the transfer function of the cube space of the artery surface and the tissue structure inside the artery, the vein
  • the data set is the transfer function result of the cube space of the vein surface and the tissue structure inside the vein;
  • the medical imaging device generating target medical image data according to the BMP data source includes: the medical imaging device imports the BMP data source into a preset VRDS medical network model to obtain the first medical Image data, the first medical image data includes a data set of blood vessels at the target site, and the blood vessel data set includes fusion data of the intersection position, data of the artery except the intersection position, and all The data of the vein except the intersection position, the data set of the blood vessel is the result of the transfer function of the cube space of the tissue structure in the blood vessel surface and the blood vessel; importing the first medical image data into a preset The cross blood vessel network model is used to perform spatial division processing on the fusion data of the cross position through the cross blood vessel network model to obtain the first data and the second data; and the first data and the second data are integrated Data, data of the artery except the intersection position, and data of the vein except the intersection position, to obtain the target medical image data.
  • the medical imaging device imports the BMP data source into the preset VRDS medical network model to obtain the first medical image data, and then imports the first medical image data into the preset cross-vascular network model, through
  • the cross blood vessel network model performs spatial segmentation processing on the fusion data of the cross position to obtain the first data and the second data, and integrates the first data, the second data, and the division of the arteries
  • the data of the intersection position and the data of the vein excluding the intersection position to obtain the target medical image data is conducive to more accurate and comprehensive acquisition of the target medical image data and improve the accuracy of the target medical image data.
  • the medical imaging device synthesizes the first data, the second data, the data of the artery except the intersection position and the data of the vein except the intersection position to obtain
  • the target medical image data includes: the medical imaging device synthesizes the first data, the second data, the data of the artery except the intersection position and the data of the vein except the intersection position , Obtain second medical image data; perform first preset processing on the second medical image data to obtain target medical image data, and the first preset processing includes at least one of the following operations: 2D boundary optimization processing, 3D boundary optimization Processing and data enhancement processing, where the target medical image data includes a data set of the target organ, a data set of the artery, and a data set of the vein;
  • the 2D boundary optimization processing includes multiple sampling to obtain low-resolution information and high-resolution information, where the low-resolution information can provide the contextual semantic information of the segmented target in the entire image, that is, reflect the relationship between the target and the environment.
  • the low-resolution information can provide the contextual semantic information of the segmented target in the entire image, that is, reflect the relationship between the target and the environment.
  • features are used to determine the object category, and high-resolution information is used to provide more refined features for segmentation targets, such as gradients.
  • the 3D boundary optimization processing includes: 3D convolution, 3D max pooling, and 3D upward convolution layer.
  • the input data size is a1, a2, a3, the number of channels is c, and the filter size is f, that is, the filter
  • the dimension is f*f*f*c, the number of filters is n, the final output of the 3-dimensional convolution is:
  • each layer contains two 3*3*3 convolution kernels, each of which follows an activation function (Relu), and then in each dimension there is a 2*2*2 maximum pooling and merging two Steps.
  • each layer is composed of 2*2*2 upward convolutions, with a step size of 2 in each dimension, and then two 3*3*3 convolutions, and then Relu. Then in the analysis path, the shortcut connections of the equal resolution layers provide the basic high-resolution features of the synthesized path. In the last layer, 1*1*1 convolution reduces the number of output channels.
  • the data enhancement processing includes any one of the following: data enhancement based on arbitrary angle rotation, data enhancement based on histogram equalization, data enhancement based on white balance, data enhancement based on mirroring operation, data enhancement based on random cut And data enhancement based on simulating different lighting changes.
  • the medical imaging device synthesizes the first data, the second data, the data of the artery except the intersection position and the data of the vein except the intersection position to obtain the second medical
  • the first preset processing is performed on the second medical image data to obtain the target medical image data
  • further optimization processing is performed on the obtained medical image data, which is beneficial to improve the accuracy and efficiency of the target medical image data.
  • the medical imaging device imports the first medical image data into a preset cross blood vessel network model, and performs spatial division processing on the fusion data at the cross position through the cross blood vessel network model to obtain
  • the first data and the second data include: the medical imaging device partitions the cross position according to the cross complexity to obtain multiple cross partitions; and filters the fusion data of each cross partition to reduce The data volume of each partition; the filtered data of each partition is imported into the cross blood vessel network model to obtain the first partition data and the second partition data of each partition; and the multiple cross partitions are integrated To obtain the first data, and synthesize the multiple second partition data of the multiple cross-partitions to obtain the second data.
  • the cross complexity of the cross position can be calculated according to the curvature range of the cross region.
  • the divisions of the intersection position can be divided according to the radius of curvature. For example, the area within the radius of curvature a1 to a2 is the first intersection area, and the area within the radius of curvature a2 to a3 is the second intersection area.
  • the data filtering strategy of each cross partition can be performed by algorithms such as convolution operation or sparse processing, with the purpose of reducing the amount of data on the basis of ensuring the display effect to improve the calculation efficiency.
  • the intersecting blood vessel network model realizes the data separation of arteries and veins through the following operations: (1) extracting the fusion data of the intersection position; (2) separating the fusion data based on a preset data separation algorithm for each fusion data to obtain mutual Independent arterial boundary point data and vein boundary point data; (3) Integrate multiple arterial boundary point data obtained after processing into the first data, and integrate multiple venous boundary point data obtained after processing into the second data.
  • the medical imaging device can perform partitioning and sparse processing for the data at the intersection of the artery and the vein, so as to ensure the display effect while improving the calculation efficiency and real-time.
  • the medical imaging device performs 4D medical imaging according to the target medical image data, wherein the intersection of the artery and the vein presents an image effect of overall separation;
  • the 4D medical imaging refers to presenting a 4-dimensional medical image.
  • the medical imaging device performing 4D medical imaging according to the target medical image data includes: the medical imaging device selects enhancement data with a quality score greater than a preset score from the target medical image data as VRDS 4D imaging data; 4D medical imaging is performed according to the VRDS 4D imaging data.
  • the quality score can be comprehensively evaluated from the following dimensions: average gradient, information entropy, visual information fidelity, peak signal-to-noise ratio PSNR, structural similarity SSIM, mean square error MSE, etc., for details, please refer to the common image field The image quality scoring algorithm will not be repeated here.
  • the medical imaging device further performs data screening through quality scores to improve the imaging effect.
  • the medical imaging device first determines the bitmap BMP data source based on the multiple scanned images of the target part of the target user, and secondly, generates the target medical image data based on the BMP data source, and finally, according to the target medical image
  • the data is subjected to 4D medical imaging, where the target medical image data includes at least a data set of blood vessels at the target site.
  • the blood vessel data set includes an artery data set and a vein data set, and the first data in the arterial data set and the vein
  • the second data of the data set are independent of each other.
  • the first data is the data associated with the intersection position of the artery and the vein, and the second data is the data associated with the intersection position.
  • the data set of the artery is the tissue structure of the artery surface and the inside of the artery.
  • the result of the transfer function of the cube space, the data set of the vein is the result of the cube space of the vein surface and the tissue structure inside the vein, and the intersection of the artery and the vein presents an overall separated image effect. It can be seen that the medical imaging device of this application can The 4D medical image display based on the original scanned image is helpful to improve the accuracy and efficiency of artery and vein imaging.
  • FIG. 3 is a schematic structural diagram of a medical imaging apparatus 300 provided by an embodiment of the present application.
  • the medical imaging apparatus 300 includes a processor 310, a memory 320, a communication interface 330, and one or more programs 321, wherein the one or more programs 321 are stored in the above-mentioned memory 320 and are configured to be executed by the above-mentioned processor 310, and the one or more The program 321 includes instructions for performing the following steps;
  • the target medical image data includes at least a data set of blood vessels at the target site.
  • the blood vessel data set includes a data set of arteries and a data set of veins.
  • the first data in the data set of the artery and the second data in the data set of the vein are independent of each other, the first data is data associated with the intersection position of the artery and the vein, and the second data Is the data associated with the intersection position, the data set of the artery is the result of the transfer function of the cube space of the arterial surface and the tissue structure inside the artery, and the data set of the vein is the surface of the vein and the The transfer function result of the cubic space of the tissue structure inside the vein;
  • the medical imaging device first determines the bitmap BMP data source based on the multiple scanned images of the target part of the target user, and secondly, generates the target medical image data based on the BMP data source, and finally, according to the target medical image
  • the data is subjected to 4D medical imaging, where the target medical image data includes at least a data set of blood vessels at the target site.
  • the blood vessel data set includes an artery data set and a vein data set, and the first data in the arterial data set and the vein
  • the second data of the data set are independent of each other.
  • the first data is the data associated with the intersection position of the artery and the vein, and the second data is the data associated with the intersection position.
  • the data set of the artery is the tissue structure of the artery surface and the inside of the artery.
  • the result of the transfer function of the cube space, the data set of the vein is the result of the cube space of the vein surface and the tissue structure inside the vein, and the intersection of the artery and the vein presents an overall separated image effect. It can be seen that the medical imaging device of this application can The 4D medical image display based on the original scanned image is helpful to improve the accuracy and efficiency of artery and vein imaging.
  • the target medical image data is generated according to the BMP data source
  • the program further includes instructions for performing the following operations: import the BMP data source into a preset VRDS medical network model to obtain The first medical image data
  • the first medical image data includes a data set of the blood vessel of the target part
  • the data set of the blood vessel includes the fusion data of the intersection position, the fusion data of the artery except the intersection position
  • the data and the data of the vein except for the intersection position, the data set of the blood vessel is the result of the transfer function of the cube space of the tissue structure in the blood vessel surface and the blood vessel
  • the first medical image data Import a preset cross blood vessel network model, and perform spatial segmentation processing on the fusion data at the cross position through the cross blood vessel network model to obtain the first data and the second data
  • the second data, the data of the artery except the intersection position, and the data of the vein except the intersection position obtain the target medical image data.
  • the instructions in the program are specifically used to perform the following operations: synthesize the first data, the second data, the data of the artery except the intersection position, and the vein
  • the data of the intersection position obtains second medical image data
  • the first preset processing is performed on the second medical image data to obtain target medical image data
  • the first preset processing includes at least one of the following operations: 2D boundary optimization Processing, 3D boundary optimization processing, and data enhancement processing.
  • the target medical image data includes a data set of the target organ, a data set of the artery, and a data set of the vein.
  • the first medical image data is imported into a preset cross blood vessel network model, and the fusion data at the cross position is spatially divided through the cross blood vessel network model to obtain the first First data and the second data
  • the program further includes instructions for performing the following operations: partition the cross position according to the cross complexity to obtain multiple cross partitions; filter the fusion data of each cross partition In order to reduce the data volume of each partition; import the filtered data of each partition into the cross blood vessel network model to obtain the first partition data and the second partition data of each partition; Multiple first partition data of a cross partition to obtain the first data, and multiple second partition data of the multiple cross partitions are synthesized to obtain the second data.
  • the 4D medical imaging is performed according to the target medical image data
  • the program further includes instructions for performing the following operations: filtering the target medical image data with a quality score greater than a preset score
  • the enhanced data is used as VRDS 4D imaging data; 4D medical imaging is performed according to the VRDS 4D imaging data.
  • the bitmap BMP data source is determined based on the multiple scanned images associated with the target organ of the target user, and the program further includes instructions for performing the following operations: Obtain the reflected target user collected by the medical device Multiple scanned images of the internal structural features of the human body; at least one scanned image containing the target part is selected from the multiple scanned images, and the at least one scanned image is used as the target user for medical digital imaging and communication DICOM data; parse the DICOM data to generate the image source of the target user, the image source includes Texture 2D/3D image volume data; perform the second preset processing on the image source to obtain the BMP data source, the first The second preset processing includes at least one of the following operations: VRDS limited contrast adaptive histogram equalization, hybrid partial differential denoising, and VRDS Ai elastic deformation processing.
  • the VRDS limited contrast adaptive histogram equalization command is specifically used to perform the following operations: regional noise ratio limiting, global contrast limiting; dividing the local histogram of the image source into multiple partitions, For each partition, determine the slope of the transformation function according to the slope of the cumulative histogram of the neighborhood of the partition, determine the degree of contrast magnification around the pixel value of the partition according to the gradient of the transformation function, and then magnify the degree according to the contrast Perform limited cropping processing to generate the distribution of the effective histogram, and at the same time generate the effective and usable neighborhood size values, and evenly distribute the cropped part of the histogram to other areas of the histogram; the hybrid partial differential denoising
  • the instructions are specifically used to perform the following operations:: Through VRDS Ai curvature drive and VRDS Ai high-level hybrid denoising, the curvature of the image edge is less than the preset curvature, which can protect the image edge and avoid the step effect in the smoothing process.
  • the VRDS Ai elastic deformation processing instruction is specifically used to perform the following operations: superimpose positive and negative random distances on the image lattice to form a difference position matrix, and then place the gray on each difference position It can form a new dot matrix, which can realize the distortion and deformation inside the image, and then rotate, distort, and translate the image.
  • the medical imaging apparatus includes hardware structures and/or software modules corresponding to each function.
  • this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software-driven hardware depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.
  • the embodiment of the present application may divide the medical imaging device into functional units according to the foregoing method examples.
  • each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and there may be other division methods in actual implementation.
  • FIG. 4 is a block diagram of the functional unit composition of the arterial and vein Ai processing device 400 based on VRDS Ai 4D medical imaging involved in an embodiment of the present application.
  • the VRDS Ai 4D medical image-based arterial and vein Ai processing device 400 is applied to a medical imaging device.
  • the VRDS Ai 4D medical image-based arterial and vein Ai processing device 400 includes a processing unit 401 and a communication unit 402, wherein,
  • the processing unit 401 is configured to determine a bitmap BMP data source according to multiple scanned images of the target part of the target user; and to generate target medical image data according to the BMP data source, the target medical image data including at least all
  • the data set of the blood vessel of the target part, the data set of the blood vessel includes the data set of the artery and the data set of the vein, and the first data in the data set of the artery and the second data of the data set of the vein Independent of each other, the first data is data associated with the intersection position of the artery and the vein, the second data is data associated with the intersection position, and the data set of the artery is the arterial surface And the transfer function result of the cube space of the tissue structure inside the artery, the data set of the vein is the transfer function result of the cube space of the vein surface and the tissue structure inside the vein; and for communicating through the communication
  • the unit 402 performs 4D medical imaging according to the target medical image data, wherein the intersection of the artery and the vein presents an image effect of overall separation.
  • the device 400 further includes a storage unit 403, the processing unit 401 may be a processor, the communication unit 402 may be a communication interface, and the storage unit 403 may be a memory.
  • the medical imaging device first determines the bitmap BMP data source based on the multiple scanned images of the target part of the target user, and secondly, generates the target medical image data based on the BMP data source, and finally, according to the target medical image
  • the data is subjected to 4D medical imaging, where the target medical image data includes at least a data set of blood vessels at the target site.
  • the blood vessel data set includes an artery data set and a vein data set, and the first data in the arterial data set and the vein
  • the second data of the data set are independent of each other.
  • the first data is the data associated with the intersection position of the artery and the vein, and the second data is the data associated with the intersection position.
  • the data set of the artery is the tissue structure of the artery surface and the inside of the artery.
  • the result of the transfer function of the cube space, the data set of the vein is the result of the cube space of the vein surface and the tissue structure inside the vein, and the intersection of the artery and the vein presents an overall separated image effect. It can be seen that the medical imaging device of this application can The 4D medical image display based on the original scanned image is helpful to improve the accuracy and efficiency of artery and vein imaging.
  • the target medical image data is generated according to the BMP data source
  • the processing unit 401 is specifically configured to: import the BMP data source into a preset VRDS medical network model to obtain the first medical image Data
  • the first medical image data includes a data set of blood vessels at the target site
  • the blood vessel data set includes fusion data of the intersection position, data of the artery except the intersection position, and the The data of the vein in addition to the intersection position, the data set of the blood vessel is the result of the transfer function of the cube space of the blood vessel surface and the tissue structure in the blood vessel
  • the first data and the second data are integrated , The data of the crossing position of the artery and the data of the crossing position of the vein to obtain the target medical image data.
  • the processing unit 401 is specifically configured to integrate the first data, the second data, the data of the artery except the intersection position and the data of the vein except the intersection position.
  • Data to obtain second medical image data perform first preset processing on the second medical image data to obtain target medical image data, and the first preset processing includes at least one of the following operations: 2D boundary optimization processing, 3D boundary Optimization processing and data enhancement processing.
  • the target medical image data includes a data set of the target organ, a data set of the artery, and a data set of the vein.
  • the first medical image data is imported into a preset cross blood vessel network model, and the fusion data at the cross position is spatially divided through the cross blood vessel network model to obtain the first The first data and the second data
  • the processing unit 401 is specifically configured to: partition the cross position according to the cross complexity to obtain multiple cross partitions; filter the fused data of each cross partition to reduce the The amount of data in each partition; the filtered data of each partition is imported into the cross blood vessel network model to obtain the first partition data and the second partition data of each partition; the integration of the multiple cross partitions A plurality of first partition data is obtained to obtain the first data, and a plurality of second partition data of the plurality of cross partitions are synthesized to obtain the second data.
  • the 4D medical imaging is performed according to the target medical image data
  • the processing unit 401 is specifically configured to: filter the enhanced data with a quality score greater than a preset score from the target medical image data as a VRDS 4D imaging data; 4D medical imaging is performed according to the VRDS 4D imaging data.
  • the bitmap BMP data source is determined according to the multiple scanned images associated with the target organ of the target user, and the processing unit 401 is specifically configured to: obtain the internal structure of the human body of the target user collected by medical equipment Multiple scanned images with characteristics; filter out at least one scanned image containing the target part from the multiple scanned images, and use the at least one scanned image as the medical digital imaging and communication DICOM data of the target user; parsing
  • the DICOM data generates the image source of the target user, the image source includes Texture 2D/3D image volume data;
  • the second preset processing is performed on the image source to obtain the BMP data source, the second preset processing Including at least one of the following operations: VRDS limited contrast adaptive histogram equalization, hybrid partial differential denoising, VRDS Ai elastic deformation processing.
  • the VRDS limited contrast adaptive histogram equalization and the processing unit 401 is specifically configured to: limit the regional noise ratio and limit the global contrast; divide the local histogram of the image source into multiple partitions For each partition, determine the slope of the transformation function according to the slope of the cumulative histogram of the neighborhood of the partition, determine the degree of contrast amplification around the pixel value of the partition according to the slope of the transformation function, and then enlarge according to the contrast Perform limited cropping processing to the extent that the distribution of the effective histogram is generated, and at the same time, the effective and usable neighborhood size value is generated, and the cropped part of the histogram is evenly distributed to other areas of the histogram; the mixed partial differential Noise, the processing unit 401 is specifically used for: VRDS Ai curvature drive and VRDS Ai high-order hybrid denoising, so that the curvature of the image edge is less than the preset curvature, which can protect the image edge and avoid steps in the smoothing process.
  • the processing unit 401 is specifically used to: superimpose positive and negative random distances on the image lattice to form a difference position matrix, and then set each difference value
  • the gray scale of the position forms a new dot matrix, which can realize the distortion and deformation inside the image, and then rotate, distort, and translate the image.
  • An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method as recorded in the above method embodiment ,
  • the aforementioned computer includes a medical imaging device.
  • the embodiments of the present application also provide a computer program product.
  • the above-mentioned computer program product includes a non-transitory computer-readable storage medium storing a computer program. Part or all of the steps of the method.
  • the computer program product may be a software installation package, and the computer includes a medical imaging device.
  • the disclosed device may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the above-mentioned units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or integrated. To another system, or some features can be ignored, or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical or other forms.
  • the units described above as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable memory.
  • the technical solution of the present application essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, A number of instructions are included to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the foregoing methods of the various embodiments of the present application.
  • the aforementioned memory includes: U disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.
  • the program can be stored in a computer-readable memory, and the memory can include: a flash disk , Read-only memory (English: Read-Only Memory, abbreviation: ROM), random access device (English: Random Access Memory, abbreviation: RAM), magnetic disk or optical disc, etc.

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Abstract

本申请实施例公开了一种基于VRDS 4D医学影像的动脉与静脉Ai处理方法及产品，应用于医学成像装置；方法包括：医学成像装置首先根据目标用户的目标部位的多张扫描图像确定位图BMP数据源，其次，根据BMP数据源生成目标医学影像数据，最后，根据目标医学影像数据进行4D医学成像，其中，目标医学影像数据至少包括目标部位的血管的数据集合，静脉的数据集合为静脉表面和静脉内部的组织结构的立方体空间的传递函数结果，且动脉和静脉的交叉位置呈现整体分离的影像效果。可见，本申请医学成像装置能够基于原始扫描图像进行4D医学影像显示，有利于提高动脉和静脉成像的准确度和效率。

Description

基于VRDS 4D医学影像的动脉与静脉Ai处理方法及产品 技术领域
本申请涉及医学成像装置技术领域，具体涉及一种基于VRDS 4D医学影像的动脉与静脉Ai处理方法及产品。
背景技术
目前，医生通过电子计算机断层扫描(Computed Tomography，CT)、磁共振成像(Magnetic Resonance Imaging，MRI)、弥散张量成像(Diffusion Tensor Imaging，DTI)、正电子发射型计算机断层显像(Positron Emission Computed Tomography，PET)等技术获取人体内部组织的形态、位置、拓扑结构等信息。但二维切片数据形成的图像中血管影像模糊不清，随着医学成像技术的飞速发展，人们对血管的医学成像提出了新的需求。
发明内容
本申请实施例提供了一种基于VRDS 4D医学影像的动脉与静脉Ai处理方法及产品，以期提高医学成像装置进行动脉和静脉成像的准确度和效率。
第一方面，本申请实施例提供一种基于VRDS 4D医学影像的动脉与静脉Ai处理方法，应用于医学成像装置；所述方法包括：
根据目标用户的目标部位的多张扫描图像确定位图BMP数据源；
根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标部位的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述动脉和所述静脉的交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据，所述动脉的数据集合为所述动脉表面和所述动脉内部的组织结构的立方体空间的传递函数结果，所述静脉的数据集合为所述静脉表面和所述静脉内部的组织结构的立方体空间的传递函数结果；
根据所述目标医学影像数据进行4D医学成像，其中，所述动脉和所述静脉的交叉位置呈现整体分离的影像效果。
第二方面，本申请实施例提供一种基于VRDS Ai 4D医学影像的动脉与静脉Ai处理装置，应用于医学成像装置；所述基于VRDS Ai 4D医学影像的动脉与静脉Ai处理装置包括处理单元和通信单元，其中，
所述处理单元，用于根据目标用户的目标部位的多张扫描图像确定位图BMP数据源；以及用于根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标部位的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述动脉和所述静脉的交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据，所述动脉的数据集合为所述动脉表面和所述动脉内部的组织结构的立方体空间的传递函数结果，所述静脉的数据集合为所述静脉表面和所述静脉内部的组织结构的立方体空间的传递函数结果；以及用于通过所述通信单元根据所述目标医学影像数据进行4D医学成像，其中，所述动脉和所述静脉的交叉位置呈现整体分离的影像效果。
第三方面，本申请实施例提供一种医学成像装置，包括处理器、存储器、通信接口以及一个或多个程序，其中，上述一个或多个程序被存储在上述存储器中，并且被配置由上 述处理器执行，上述程序包括用于执行本申请实施例第一方面任一方法中的步骤的指令。
第四方面，本申请实施例提供了一种计算机可读存储介质，其中，上述计算机可读存储介质存储用于电子数据交换的计算机程序，其中，上述计算机程序使得计算机执行如本申请实施例第一方面任一方法中所描述的部分或全部步骤。
第五方面，本申请实施例提供了一种计算机程序产品，其中，上述计算机程序产品包括存储了计算机程序的非瞬时性计算机可读存储介质，上述计算机程序可操作来使计算机执行如本申请实施例第一方面任一方法中所描述的部分或全部步骤。该计算机程序产品可以为一个软件安装包。
可以看出，本申请实施例中，医学成像装置首先根据目标用户的目标部位的多张扫描图像确定位图BMP数据源，其次，根据BMP数据源生成目标医学影像数据，最后，根据目标医学影像数据进行4D医学成像，其中，目标医学影像数据至少包括目标部位的血管的数据集合，血管的数据集合中包括动脉的数据集合和静脉的数据集合，且动脉的数据集合中的第一数据和静脉的数据集合的第二数据相互独立，第一数据为与动脉和静脉的交叉位置关联的数据，第二数据为与交叉位置关联的数据，动脉的数据集合为动脉表面和动脉内部的组织结构的立方体空间的传递函数结果，静脉的数据集合为静脉表面和静脉内部的组织结构的立方体空间的传递函数结果，且动脉和静脉的交叉位置呈现整体分离的影像效果，可见，本申请医学成像装置能够基于原始扫描图像进行4D医学影像显示，有利于提高动脉和静脉成像的准确度和效率。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种基于VRDS Ai 4D医学影像智能分析处理***的结构示意图；
图2是本申请实施例提供的一种基于VRDS 4D医学影像的动脉与静脉Ai处理方法的流程示意图；
图3是本申请实施例提供的一种医学成像装置的结构示意图；
图4是本申请实施例提供的一种基于VRDS Ai 4D医学影像的动脉与静脉Ai处理装置的功能单元组成框图。
具体实施方式
为了使本技术领域的人员更好地理解本申请方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别不同对象，而不是用于描述特定顺序。此外，术语“包括”和“具有”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、***、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其他步骤或单元。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在 本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
本申请实施例所涉及到的医学成像装置是指利用各种不同媒介作为信息载体，将人体内部的结构重现为影像的各种仪器，其影像信息与人体实际结构有着空间和时间分布上的对应关系。“DICOM数据”是指通过医疗设备采集的反映人体内部结构特征的原始图像文件数据，可以包括电子计算机断层扫描CT、核磁共振MRI、弥散张量成像DTI、正电子发射型计算机断层显像PET-CT等信息，“图源”是指解析原始DICOM数据生成的Texture2D/3D图像体数据。“VRDS”是指虚拟现实医用***(Virtual Reality Doctor system，简称为VRDS)。
请参阅图1，图1是本申请实施例提供了一种基于VRDS Ai 4D医学影像智能分析处理***100的结构示意图，该***100包括医学成像装置110和网络数据库120，其中医学成像装置110可以包括本地医学成像装置111和/或终端医学成像装置112，本地医学成像装置111或终端医学成像装置112用于基于原始DICOM数据，以本申请实施例所呈现的基于VRDS Ai 4D医学影像的动脉与静脉的分析处理算法为基础，进行人体动脉和静脉的四维分离体绘制，实现四维立体效果(该4维医学影像具体是指医学影像包括所显示组织的内部空间结构特征及外部空间结构特征，所述内部空间结构特征是指组织内部的切片数据未丢失，即医学成像装置可以呈现目标器官、血管等组织的内部构造，外部空间结构特性是指组织与组织之间的环境特征，包括组织与组织之间的空间位置特性(包括交叉、间隔、融合)等，如肾脏与动脉之间的交叉位置的边缘结构特性等)，本地医学成像装置111相对于终端医学成像装置112还可以用于对图源数据进行编辑，形成四维人体图像的传递函数结果，该传递函数结果可以包括人体内脏器官表面和人体内脏器官内的组织结构的传递函数结果，以及立方体空间的传递函数结果，如传递函数所需的立方编辑框与弧线编辑的数组数量、坐标、颜色、透明度等信息。网络数据库120例如可以是云服务器等，该网络数据库120用于存储解析原始DICOM数据生成的图源，以及本地医学成像装置111编辑得到的四维人体图像的传递函数结果，图源可以是来自于多个本地医学成像装置111以实现多个医生的交互诊断。
用户通过上述医学成像装置110进行具体的图像显示时，可以选择显示器或者虚拟现实VR的头戴式显示器(Head mounted Displays Set，HMDS)结合操作动作进行显示，操作动作是指用户通过医学成像装置的外部摄入设备，如鼠标、键盘等，对四维人体图像进行的操作控制，以实现人机交互，该操作动作包括以下至少一种：(1)改变某个具体器官/组织的颜色和/或透明度，(2)定位缩放视图，(3)旋转视图，实现四维人体图像的多视角360度观察，(4)“进入”人体器官内部观察内部构造，实时剪切效果渲染，(5)上下移动视图。
下面对本申请实施例涉及到的基于VRDS 4D医学影像的动脉与静脉Ai处理方法进行详细介绍。
请参阅图2，图2是本申请实施例提供了一种基于VRDS 4D医学影像的动脉与静脉Ai处理方法的流程示意图，应用于如图1所述的医学成像装置；如图所示，本基于VRDS 4D医学影像的动脉与静脉Ai处理方法包括：
S201，医学成像装置根据目标用户的目标部位的多张扫描图像确定位图BMP数据源；
其中，所述目标部分例如可以是肾脏。所述扫描图像包括以下任意一种：CT图像、MRI图像、DTI图像、PET-CT图像。
在一个可能的示例中，所述医学成像装置根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，包括：所述医学成像装置获取通过医疗设备采集的反映目标用户的人体内部结构特征的多张扫描图像；从所述多张扫描图像中筛选出包含所述目标部位的至少一张扫描图像，将所述至少一张扫描图像作为目标用户的医学数字成像和通信DICOM 数据；解析所述DICOM数据生成目标用户的图源，所述图源包括纹理Texture 2D/3D图像体数据；针对所述图源执行第二预设处理得到所述BMP数据源，所述第二预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理。
可见，本示例中，医学成像装置会对扫描图像进行筛选得到DICOM数据，然后解析得到Texture 2D/3D图像体数据，最后通过均衡、去噪、弹性变形中的至少一种进行处理，最终获得符合医学影像要求的BMP数据源。
在本可能的示例中，所述VRDS限制对比度自适应直方图均衡包括：区域噪音比度限幅、全局对比度限幅；将图源的局部直方图划分多个分区，针对每个分区，根据该分区的邻域的累积直方图的斜度确定变换函数的斜度，根据该变换函数的斜度确定该分区的像素值周边的对比度放大程度，然后根据该对比度放大程度进行限度裁剪处理，产生有效直方图的分布，同时也产生有效可用的邻域大小的取值，将这些裁剪掉的部分直方图均匀的分布到直方图的其他区域。
所述混合偏微分去噪包括：不同于高斯低通滤波(对图像的高频成分不加区别的减弱，去噪的同时会产生图像边缘模糊化)，自然图像中的物体所形成的等照度线(包括边缘)应该是足够光滑顺畅的曲线，即这些等照度线的曲率的绝对值应该足够小，当图像受到噪声污染后，图像的局部灰度值会发生随机起伏，导致等照度线的不规则震荡，形成局部曲率很大的等照度线，根据这一原理，设计通过VRDS Ai曲率驱动和VRDS Ai高阶混合去噪，实现即可保护图像边缘、又可以避免平滑过程中出现阶梯效应的混合偏微分去噪模型。
所述VRDS Ai弹性变形处理包括：在原有点阵上，叠加正负向随机距离形成差值位置矩阵，然后在每个差值位置上的灰度，形成新的点阵，可以实现图像内部的扭曲变形，另外再对图像进行旋转、扭曲、平移等操作。
可见，本示例中，医学成像装置通过对原始扫描图像数据的处理，得到BMP数据源，提高了原始数据的信息量，且增加了深度维度信息，最终得到符合4D医学影像显示需求的数据。
S202，所述医学成像装置根据所述BMP数据源生成目标医学影像数据，其中，所述目标医学影像数据至少包括所述目标部位的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述动脉和所述静脉的交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据，所述动脉的数据集合为所述动脉表面和所述动脉内部的组织结构的立方体空间的传递函数结果，所述静脉的数据集合为所述静脉表面和所述静脉内部的组织结构的立方体空间的传递函数结果；
在一个可能的示例中，所述医学成像装置根据所述BMP数据源生成目标医学影像数据，包括：所述医学成像装置将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标部位的血管的数据集合，所述血管的数据集合中包括所述交叉位置的融合数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，所述血管的数据集合为所述血管表面和所述血管内部中的组织结构的立方体空间的传递函数结果；将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据；综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据。
可见，本示例中，医学成像装置将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，再将所述第一医学影像数据导入预设的交叉血管网络模型，通过 所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据，综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据，有利于更加准确、全面的获取目标医学影像数据，提高目标医学影像数据的精准度。
在本可能的示例中，所述医学成像装置综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据，包括：所述医学成像装置综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到第二医学影像数据；针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合；
其中，所述2D边界优化处理包括多次采样获取低分辨率信息和高分辨率信息，其中，低分辨率信息能够提供分割目标在整个图像中上下文语义信息，即反映目标与环境之间关系的特征，这些特征用于物体类别判断，高分辨率信息用于为分割目标提供更加精细的特征，如梯度等。
其中，所述3D边界优化处理包括：3D卷积、3D最大池化和3D向上卷积层，输入数据的大小为a1、a2、a3，通道数为c，过滤器大小为f，即过滤器维度为f*f*f*c，过滤器数量为n，则3维卷积最终输出为：
(a1-f+1)*(a2-f+1)*(a3-f+1)*n
具有分析路径和合成路径。在分析路径中，每一层包含两个3*3*3的卷积核，每一个都跟随一个激活函数(Relu)，然后在每个维度上有2*2*2的最大池化合并两个步长。在合成路径中，每个层由2*2*2的向上卷积组成，每个维度上步长都为2，接着，两个3*3*3的卷积，然后Relu。然后在分析路径中从相等分辨率层的shortcut连接提供了合成路径的基本高分辨特征。在最后一层中，1*1*1卷积减少了输出通道的数量。
其中，所述数据增强处理包括以下任意一种：基于任意角度旋转的数据增强、基于直方图均衡的数据增强、基于白平衡的数据增强、基于镜像操作的数据增强、基于随机剪切的数据增强和基于模拟不同光照变化的数据增强。
可见，本示例中，医学成像装置综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到第二医学影像数据，针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，对得到的医学影像数据进行进一步的优化处理，有利于提高目标医学影像数据的精准性和高效性。
在本可能的示例中，所述医学成像装置将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据，包括：所述医学成像装置对所述交叉位置按照交叉复杂度进行分区，得到多个交叉分区；对每个交叉分区的融合数据进行筛选以降低所述每个分区的数据量；将所述每个分区的筛选后数据导入所述交叉血管网络模型，得到所述每个分区的第一分区数据和第二分区数据；综合所述多个交叉分区的多个第一分区数据，得到所述第一数据，综合所述多个交叉分区的多个第二分区数据，得到所述第二数据。
具体实现中，交叉位置的交叉复杂度可以根据交叉区域的曲率范围计算得到，曲率范围越大，说明动脉与静脉的接触情况越紧密复杂，对应交叉复杂度越高。交叉位置的分区具体可以按照曲率半径进行分区，如曲率半径a1到a2范围内的区域为第一交叉分区，曲率半径a2到a3范围内的区域为第二交叉分区。每个交叉分区的数据的筛选策略可以是通过卷积运算或者稀疏处理等算法执行，目的是保证显示效果的基础上减少数据量以提高计算效 率。
其中，所述交叉血管网络模型通过以下操作实现动脉和静脉的数据分离：(1)提取交叉位置的融合数据；(2)针对每个融合数据基于预设数据分离算法分离该融合数据，得到相互独立的动脉边界点数据和静脉边界点数据；(3)将处理后得到的多个动脉边界点数据整合为第一数据，将处理后得到的多个静脉边界点数据整合为第二数据。
可见，本示例中，医学成像装置能够针对动脉和静脉的交叉位置的数据进行分区和稀疏处理，保证显示效果的同时提高计算效率，提高实时性。
S203，所述医学成像装置根据所述目标医学影像数据进行4D医学成像，其中，所述动脉和所述静脉的交叉位置呈现整体分离的影像效果；
其中，所述4D医学成像是指呈现4维医学影像。
在一个可能的示例中，所述医学成像装置根据所述目标医学影像数据进行4D医学成像，包括：所述医学成像装置从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据；根据所述VRDS 4D成像数据进行4D医学成像。
其中，所述质量评分可以从以下维度进行综合评价，平均梯度、信息熵、视觉信息保真度、峰值信噪比PSNR、结构相似性SSIM、均方误差MSE等，具体可以参考图像领域的常见图像质量评分算法，此处不再赘述。
可见，本示例中，医学成像装置通过质量评分进一步进行数据筛选，提高成像效果。
可以看出，本申请实施例中，医学成像装置首先根据目标用户的目标部位的多张扫描图像确定位图BMP数据源，其次，根据BMP数据源生成目标医学影像数据，最后，根据目标医学影像数据进行4D医学成像，其中，目标医学影像数据至少包括目标部位的血管的数据集合，血管的数据集合中包括动脉的数据集合和静脉的数据集合，且动脉的数据集合中的第一数据和静脉的数据集合的第二数据相互独立，第一数据为与动脉和静脉的交叉位置关联的数据，第二数据为与交叉位置关联的数据，动脉的数据集合为动脉表面和动脉内部的组织结构的立方体空间的传递函数结果，静脉的数据集合为静脉表面和静脉内部的组织结构的立方体空间的传递函数结果，且动脉和静脉的交叉位置呈现整体分离的影像效果，可见，本申请医学成像装置能够基于原始扫描图像进行4D医学影像显示，有利于提高动脉和静脉成像的准确度和效率。
与上述图2所示的实施例一致的，请参阅图3，图3是本申请实施例提供的一种医学成像装置300的结构示意图，如图所示，所述医学成像装置300包括处理器310、存储器320、通信接口330以及一个或多个程序321，其中，所述一个或多个程序321被存储在上述存储器320中，并且被配置由上述处理器310执行，所述一个或多个程序321包括用于执行以下步骤的指令；
根据目标用户的目标部位的多张扫描图像确定位图BMP数据源；
根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标部位的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述动脉和所述静脉的交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据，所述动脉的数据集合为所述动脉表面和所述动脉内部的组织结构的立方体空间的传递函数结果，所述静脉的数据集合为所述静脉表面和所述静脉内部的组织结构的立方体空间的传递函数结果；
根据所述目标医学影像数据进行4D医学成像，其中，所述动脉和所述静脉的交叉位置呈现整体分离的影像效果。
可以看出，本申请实施例中，医学成像装置首先根据目标用户的目标部位的多张扫描 图像确定位图BMP数据源，其次，根据BMP数据源生成目标医学影像数据，最后，根据目标医学影像数据进行4D医学成像，其中，目标医学影像数据至少包括目标部位的血管的数据集合，血管的数据集合中包括动脉的数据集合和静脉的数据集合，且动脉的数据集合中的第一数据和静脉的数据集合的第二数据相互独立，第一数据为与动脉和静脉的交叉位置关联的数据，第二数据为与交叉位置关联的数据，动脉的数据集合为动脉表面和动脉内部的组织结构的立方体空间的传递函数结果，静脉的数据集合为静脉表面和静脉内部的组织结构的立方体空间的传递函数结果，且动脉和静脉的交叉位置呈现整体分离的影像效果，可见，本申请医学成像装置能够基于原始扫描图像进行4D医学影像显示，有利于提高动脉和静脉成像的准确度和效率。
在一个可能的示例中，所述根据所述BMP数据源生成目标医学影像数据，所述程序还包括用于执行以下操作的指令：将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标部位的血管的数据集合，所述血管的数据集合中包括所述交叉位置的融合数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，所述血管的数据集合为所述血管表面和所述血管内部中的组织结构的立方体空间的传递函数结果；将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据；综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据。
在一个可能的示例中，在所述综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据方面，所述程序中的指令具体用于执行以下操作：综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到第二医学影像数据；针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合。
在一个可能的示例中，所述将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据，所述程序还包括用于执行以下操作的指令：对所述交叉位置按照交叉复杂度进行分区，得到多个交叉分区；对每个交叉分区的融合数据进行筛选以降低所述每个分区的数据量；将所述每个分区的筛选后数据导入所述交叉血管网络模型，得到所述每个分区的第一分区数据和第二分区数据；综合所述多个交叉分区的多个第一分区数据，得到所述第一数据，综合所述多个交叉分区的多个第二分区数据，得到所述第二数据。
在一个可能的示例中，所述根据所述目标医学影像数据进行4D医学成像，所述程序还包括用于执行以下操作的指令：从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据；根据所述VRDS 4D成像数据进行4D医学成像。
在一个可能的示例中，所述根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，所述程序还包括用于执行以下操作的指令：获取通过医疗设备采集的反映目标用户的人体内部结构特征的多张扫描图像；从所述多张扫描图像中筛选出包含所述目标部位的至少一张扫描图像，将所述至少一张扫描图像作为目标用户的医学数字成像和通信DICOM数据；解析所述DICOM数据生成目标用户的图源，所述图源包括纹理Texture 2D/3D图像体数据；针对所述图源执行第二预设处理得到所述BMP数据源，所述第二预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai 弹性变形处理。
在一个可能的示例中，所述VRDS限制对比度自适应直方图均衡指令具体用于执行以下操作：：区域噪音比度限幅、全局对比度限幅；将图源的局部直方图划分多个分区，针对每个分区，根据该分区的邻域的累积直方图的斜度确定变换函数的斜度，根据该变换函数的斜度确定该分区的像素值周边的对比度放大程度，然后根据该对比度放大程度进行限度裁剪处理，产生有效直方图的分布，同时也产生有效可用的邻域大小的取值，将这些裁剪掉的部分直方图均匀的分布到直方图的其他区域；所述混合偏微分去噪指令具体用于执行以下操作：：通过VRDS Ai曲率驱动和VRDS Ai高阶混合去噪，使得图像边缘的曲率小于预设曲率，实现即可保护图像边缘、又可以避免平滑过程中出现阶梯效应的混合偏微分去噪模型；所述VRDS Ai弹性变形处理指令具体用于执行以下操作：在图像点阵上，叠加正负向随机距离形成差值位置矩阵，然后在每个差值位置上的灰度，形成新的点阵，可以实现图像内部的扭曲变形，再对图像进行旋转、扭曲、平移操作。
上述主要从方法侧执行过程的角度对本申请实施例的方案进行了介绍。可以理解的是，医学成像装置为了实现上述功能，其包含了执行各个功能相应的硬件结构和/或软件模块。本领域技术人员应该很容易意识到，结合本文中所提供的实施例描述的各示例的单元及算法步骤，本申请能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机软件驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
本申请实施例可以根据上述方法示例对医学成像装置进行功能单元的划分，例如，可以对应各个功能划分各个功能单元，也可以将两个或两个以上的功能集成在一个处理单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。需要说明的是，本申请实施例中对单元的划分是示意性的，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
图4是本申请实施例中所涉及的基于VRDS Ai 4D医学影像的动脉与静脉Ai处理装置400的功能单元组成框图。该基于VRDS Ai 4D医学影像的动脉与静脉Ai处理装置400应用于医学成像装置，该基于VRDS Ai 4D医学影像的动脉与静脉Ai处理装置400包括处理单元401和通信单元402，其中，
所述处理单元401，用于根据目标用户的目标部位的多张扫描图像确定位图BMP数据源；以及用于根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标部位的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述动脉和所述静脉的交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据，所述动脉的数据集合为所述动脉表面和所述动脉内部的组织结构的立方体空间的传递函数结果，所述静脉的数据集合为所述静脉表面和所述静脉内部的组织结构的立方体空间的传递函数结果；以及用于通过所述通信单元402根据所述目标医学影像数据进行4D医学成像，其中，所述动脉和所述静脉的交叉位置呈现整体分离的影像效果。
其中，所述装置400还包括存储单元403，所述处理单元401可以是处理器，所述通信单元402可以是通信接口，所述存储单元403可以是存储器。
可以看出，本申请实施例中，医学成像装置首先根据目标用户的目标部位的多张扫描图像确定位图BMP数据源，其次，根据BMP数据源生成目标医学影像数据，最后，根据目标医学影像数据进行4D医学成像，其中，目标医学影像数据至少包括目标部位的血管的数据集合，血管的数据集合中包括动脉的数据集合和静脉的数据集合，且动脉的数据集合中 的第一数据和静脉的数据集合的第二数据相互独立，第一数据为与动脉和静脉的交叉位置关联的数据，第二数据为与交叉位置关联的数据，动脉的数据集合为动脉表面和动脉内部的组织结构的立方体空间的传递函数结果，静脉的数据集合为静脉表面和静脉内部的组织结构的立方体空间的传递函数结果，且动脉和静脉的交叉位置呈现整体分离的影像效果，可见，本申请医学成像装置能够基于原始扫描图像进行4D医学影像显示，有利于提高动脉和静脉成像的准确度和效率。
在一个可能的示例中，所述根据所述BMP数据源生成目标医学影像数据，所述处理单元401具体用于：将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标部位的血管的数据集合，所述血管的数据集合中包括所述交叉位置的融合数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，所述血管的数据集合为所述血管表面和所述血管内部中的组织结构的立方体空间的传递函数结果；将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据；综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据。
在一个可能的示例中，在所述综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据方面：所述处理单元401具体用于：综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到第二医学影像数据；针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合。
在一个可能的示例中，所述将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据，所述处理单元401具体用于：对所述交叉位置按照交叉复杂度进行分区，得到多个交叉分区；对每个交叉分区的融合数据进行筛选以降低所述每个分区的数据量；将所述每个分区的筛选后数据导入所述交叉血管网络模型，得到所述每个分区的第一分区数据和第二分区数据；综合所述多个交叉分区的多个第一分区数据，得到所述第一数据，综合所述多个交叉分区的多个第二分区数据，得到所述第二数据。
在一个可能的示例中，所述根据所述目标医学影像数据进行4D医学成像，所述处理单元401具体用于：从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据；根据所述VRDS 4D成像数据进行4D医学成像。
在一个可能的示例中，所述根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，所述处理单元401具体用于：获取通过医疗设备采集的反映目标用户的人体内部结构特征的多张扫描图像；从所述多张扫描图像中筛选出包含所述目标部位的至少一张扫描图像，将所述至少一张扫描图像作为目标用户的医学数字成像和通信DICOM数据；解析所述DICOM数据生成目标用户的图源，所述图源包括纹理Texture 2D/3D图像体数据；针对所述图源执行第二预设处理得到所述BMP数据源，所述第二预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理。
在一个可能的示例中，所述VRDS限制对比度自适应直方图均衡，所述处理单元401具体用于：区域噪音比度限幅、全局对比度限幅；将图源的局部直方图划分多个分区，针对每个分区，根据该分区的邻域的累积直方图的斜度确定变换函数的斜度，根据该变换函数的斜度确定该分区的像素值周边的对比度放大程度，然后根据该对比度放大程度进行限 度裁剪处理，产生有效直方图的分布，同时也产生有效可用的邻域大小的取值，将这些裁剪掉的部分直方图均匀的分布到直方图的其他区域；所述混合偏微分去噪，所述处理单元401具体用于：通过VRDS Ai曲率驱动和VRDS Ai高阶混合去噪，使得图像边缘的曲率小于预设曲率，实现即可保护图像边缘、又可以避免平滑过程中出现阶梯效应的混合偏微分去噪模型；所述VRDS Ai弹性变形处理，所述处理单元401具体用于：在图像点阵上，叠加正负向随机距离形成差值位置矩阵，然后在每个差值位置上的灰度，形成新的点阵，可以实现图像内部的扭曲变形，再对图像进行旋转、扭曲、平移操作。
本申请实施例还提供一种计算机存储介质，其中，该计算机存储介质存储用于电子数据交换的计算机程序，该计算机程序使得计算机执行如上述方法实施例中记载的任一方法的部分或全部步骤，上述计算机包括医学成像装置。
本申请实施例还提供一种计算机程序产品，上述计算机程序产品包括存储了计算机程序的非瞬时性计算机可读存储介质，上述计算机程序可操作来使计算机执行如上述方法实施例中记载的任一方法的部分或全部步骤。该计算机程序产品可以为一个软件安装包，上述计算机包括医学成像装置。
需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本申请并不受所描述的动作顺序的限制，因为依据本申请，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定是本申请所必须的。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置，可通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如上述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个***，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性或其它的形式。
上述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
上述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储器中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储器中，包括若干指令用以使得一台计算机设备(可为个人计算机、服务器或者网络设备等)执行本申请各个实施例上述方法的全部或部分步骤。而前述的存储器包括：U盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、移动硬盘、磁碟或者光盘等各种可以存储程序代码的介质。
本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储器中，存储器可以 包括：闪存盘、只读存储器(英文：Read-Only Memory，简称：ROM)、随机存取器(英文：Random Access Memory，简称：RAM)、磁盘或光盘等。
以上对本申请实施例进行了详细介绍，本文中应用了具体个例对本申请的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的一般技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。

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  1. 一种基于VRDS 4D医学影像的动脉与静脉Ai处理方法，其特征在于，应用于医学成像装置；所述方法包括：
    根据目标用户的目标部位的多张扫描图像确定位图BMP数据源；
    根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标部位的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述动脉和所述静脉的交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据，所述动脉的数据集合为所述动脉表面和所述动脉内部的组织结构的立方体空间的传递函数结果，所述静脉的数据集合为所述静脉表面和所述静脉内部的组织结构的立方体空间的传递函数结果；
    根据所述目标医学影像数据进行4D医学成像，其中，所述动脉和所述静脉的交叉位置呈现整体分离的影像效果。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述BMP数据源生成目标医学影像数据，包括：
    将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标部位的血管的数据集合，所述血管的数据集合中包括所述交叉位置的融合数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，所述血管的数据集合为所述血管表面和所述血管内部中的组织结构的立方体空间的传递函数结果；
    将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据；
    综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据。
  3. 根据权利要求1所述的方法，其特征在于，所述综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据，包括：
    综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到第二医学影像数据；
    针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合。
  4. 根据权利要求3所述的方法，其特征在于，所述2D边界优化处理包括：多次采样获取低分辨率信息和高分辨率信息；所述3D边界优化处理包括：3D卷积、3D最大池化和3D向上卷积层。
  5. 根据权利要求2或3所述的方法，其特征在于，所述将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据，包括：
    对所述交叉位置按照交叉复杂度进行分区，得到多个交叉分区；
    对每个交叉分区的融合数据进行筛选以降低所述每个分区的数据量；
    将所述每个分区的筛选后数据导入所述交叉血管网络模型，得到所述每个分区的第一分区数据和第二分区数据；
    综合所述多个交叉分区的多个第一分区数据，得到所述第一数据，综合所述多个交叉分区的多个第二分区数据，得到所述第二数据。
  6. 根据权利要求5所述的方法，其特征在于，所述对所述交叉位置按照交叉复杂度进行分区，得到多个交叉分区，包括：
    获取所述交叉位置的曲率范围；
    根据所述曲率范围得到曲率半径；
    根据所述曲率半径进行分区，得到多个交叉分区。
  7. 根据权利要求1-6任一项所述的方法，其特征在于，所述根据所述目标医学影像数据进行4D医学成像，包括：
    从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据；
    根据所述VRDS 4D成像数据进行4D医学成像。
  8. 根据权利要求1-7任一项所述的方法，其特征在于，所述根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，包括：
    获取通过医疗设备采集的反映目标用户的人体内部结构特征的多张扫描图像；
    从所述多张扫描图像中筛选出包含所述目标部位的至少一张扫描图像，将所述至少一张扫描图像作为目标用户的医学数字成像和通信DICOM数据；
    解析所述DICOM数据生成目标用户的图源，所述图源包括纹理Texture 2D/3D图像体数据；
    针对所述图源执行第二预设处理得到所述BMP数据源，所述第二预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理。
  9. 根据权利要求8所述的方法，其特征在于，所述VRDS限制对比度自适应直方图均衡包括以下步骤：区域噪音比度限幅、全局对比度限幅；将图源的局部直方图划分多个分区，针对每个分区，根据该分区的邻域的累积直方图的斜度确定变换函数的斜度，根据该变换函数的斜度确定该分区的像素值周边的对比度放大程度，然后根据该对比度放大程度进行限度裁剪处理，产生有效直方图的分布，同时也产生有效可用的邻域大小的取值，将这些裁剪掉的部分直方图均匀的分布到直方图的其他区域；
    所述混合偏微分去噪包括以下步骤：通过VRDS Ai曲率驱动和VRDS Ai高阶混合去噪，使得图像边缘的曲率小于预设曲率，实现既可以保护图像边缘、又可以避免平滑过程中出现阶梯效应的混合偏微分去噪模型；
    所述VRDS Ai弹性变形处理包括以下步骤：在图像点阵上，叠加正负向随机距离形成差值位置矩阵，然后在每个差值位置上的灰度，形成新的点阵，可以实现图像内部的扭曲变形，再对图像进行旋转、扭曲、平移操作。
  10. 一种基于VRDS Ai 4D医学影像的动脉与静脉Ai处理装置，其特征在于，应用于医学成像装置；所述基于VRDS Ai 4D医学影像的动脉与静脉Ai处理装置包括处理单元和通信单元，其中，
    所述处理单元，用于根据目标用户的目标部位的多张扫描图像确定位图BMP数据源；以及用于根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标部位的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述动脉和所述静脉的交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据，所述动脉的数据集合为所述动脉表面和所述动脉内部的组织结构的立 方体空间的传递函数结果，所述静脉的数据集合为所述静脉表面和所述静脉内部的组织结构的立方体空间的传递函数结果；以及用于通过所述通信单元根据所述目标医学影像数据进行4D医学成像，其中，所述动脉和所述静脉的交叉位置呈现整体分离的影像效果。
  11. 根据权利要求10所述的装置，其特征在于，在所述根据所述BMP数据源生成目标医学影像数据方面，所述处理单元具体用于：将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标部位的血管的数据集合，所述血管的数据集合中包括所述交叉位置的融合数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，所述血管的数据集合为所述血管表面和所述血管内部中的组织结构的立方体空间的传递函数结果；将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据；综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据。
  12. 根据权利要求10所述的装置，其特征在于，在所述综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到所述目标医学影像数据方面，所述处理单元具体用于：综合所述第一数据、所述第二数据、所述动脉的除所述交叉位置的数据以及所述静脉的除所述交叉位置的数据，得到第二医学影像数据；针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合。
  13. 根据权利要求12所述的装置，其特征在于，所述2D边界优化处理包括：多次采样获取低分辨率信息和高分辨率信息；所述3D边界优化处理包括：3D卷积、3D最大池化和3D向上卷积层。
  14. 根据权利要求11或12所述的装置，其特征在于，在所述将所述第一医学影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的融合数据进行空间分割处理，得到所述第一数据和所述第二数据方面，所述处理单元具体用于：对所述交叉位置按照交叉复杂度进行分区，得到多个交叉分区；对每个交叉分区的融合数据进行筛选以降低所述每个分区的数据量；将所述每个分区的筛选后数据导入所述交叉血管网络模型，得到所述每个分区的第一分区数据和第二分区数据；综合所述多个交叉分区的多个第一分区数据，得到所述第一数据，综合所述多个交叉分区的多个第二分区数据，得到所述第二数据。
  15. 根据权利要求14所述的装置，其特征在于，在所述对所述交叉位置按照交叉复杂度进行分区，得到多个交叉分区方面，所述处理单元具体用于：获取所述交叉位置的曲率范围；根据所述曲率范围得到曲率半径；根据所述曲率半径进行分区，得到多个交叉分区。
  16. 根据权利要求10-15任一项所述的装置，其特征在于，在所述根据所述目标医学影像数据进行4D医学成像方面，所述通信单元具体用于：从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据；根据所述VRDS 4D成像数据进行4D医学成像。
  17. 根据权利要求10-16任一项所述的装置，其特征在于，在所述根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源方面，所述处理单元具体用于：获取通过医疗设备采集的反映目标用户的人体内部结构特征的多张扫描图像；从所述多张扫描图像中筛选出包含所述目标部位的至少一张扫描图像，将所述至少一张扫描图像作为目标用户的 医学数字成像和通信DICOM数据；解析所述DICOM数据生成目标用户的图源，所述图源包括纹理Texture 2D/3D图像体数据；针对所述图源执行第二预设处理得到所述BMP数据源，所述第二预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理。
  18. 根据权利要求17所述的装置，其特征在于，所述VRDS限制对比度自适应直方图均衡包括以下步骤：区域噪音比度限幅、全局对比度限幅；将图源的局部直方图划分多个分区，针对每个分区，根据该分区的邻域的累积直方图的斜度确定变换函数的斜度，根据该变换函数的斜度确定该分区的像素值周边的对比度放大程度，然后根据该对比度放大程度进行限度裁剪处理，产生有效直方图的分布，同时也产生有效可用的邻域大小的取值，将这些裁剪掉的部分直方图均匀的分布到直方图的其他区域；所述混合偏微分去噪包括以下步骤：通过VRDS Ai曲率驱动和VRDS Ai高阶混合去噪，使得图像边缘的曲率小于预设曲率，实现既可以保护图像边缘、又可以避免平滑过程中出现阶梯效应的混合偏微分去噪模型；所述VRDS Ai弹性变形处理包括以下步骤：在图像点阵上，叠加正负向随机距离形成差值位置矩阵，然后在每个差值位置上的灰度，形成新的点阵，可以实现图像内部的扭曲变形，再对图像进行旋转、扭曲、平移操作。
  19. 一种医学成像装置，其特征在于，包括处理器、存储器、通信接口，以及一个或多个程序，所述一个或多个程序被存储在所述存储器中，并且被配置由所述处理器执行，所述程序包括用于执行如权利要求1-9任一项所述的方法中的步骤的指令。
  20. 一种计算机可读存储介质，其特征在于，存储用于电子数据交换的计算机程序，其中，所述计算机程序使得计算机执行如权利要求1-9任一项所述的方法。
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102543044A (zh) * 2011-11-28 2012-07-04 中国人民解放军第三军医大学第二附属医院 使冠状动脉显示更精细的方法及***
WO2018222181A1 (en) * 2017-05-31 2018-12-06 Proximie Inc. Systems and methods for determining three dimensional measurements in telemedicine application
CN109157284A (zh) * 2018-09-28 2019-01-08 广州狄卡视觉科技有限公司 一种脑肿瘤医学影像三维重建显示交互方法及***
CN109192308A (zh) * 2018-10-26 2019-01-11 西安医学院第附属医院 一种周围动脉硬化的早期筛查***
CN109300531A (zh) * 2018-08-24 2019-02-01 深圳大学 一种脑疾病早期诊断方法和装置

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB0708136D0 (en) * 2007-04-26 2007-06-06 Cancer Res Inst Royal Measure of contrast agent
CN102895031A (zh) * 2012-09-19 2013-01-30 深圳市旭东数字医学影像技术有限公司 肾脏虚拟手术方法及其***
CN105095487A (zh) * 2015-08-17 2015-11-25 北京京东尚科信息技术有限公司 电子商务网点信息管控方法
EP3380008B1 (en) * 2015-11-29 2020-09-09 Arterys Inc. Medical imaging and efficient sharing of medical imaging information
US10002428B2 (en) * 2015-12-01 2018-06-19 Ottawa Hospital Research Institute Method and system for identifying bleeding
CN106991712A (zh) * 2016-11-25 2017-07-28 斯图尔特平李 一种基于hmds的医学成像***

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102543044A (zh) * 2011-11-28 2012-07-04 中国人民解放军第三军医大学第二附属医院 使冠状动脉显示更精细的方法及***
WO2018222181A1 (en) * 2017-05-31 2018-12-06 Proximie Inc. Systems and methods for determining three dimensional measurements in telemedicine application
CN109300531A (zh) * 2018-08-24 2019-02-01 深圳大学 一种脑疾病早期诊断方法和装置
CN109157284A (zh) * 2018-09-28 2019-01-08 广州狄卡视觉科技有限公司 一种脑肿瘤医学影像三维重建显示交互方法及***
CN109192308A (zh) * 2018-10-26 2019-01-11 西安医学院第附属医院 一种周围动脉硬化的早期筛查***

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