Image processing method, device and image processing equipment

Abstract

The present application discloses an image processing method, an apparatus and an image processing device, which relate to the field of image processing technology. After the image processing device obtains a mask image sequence and a contrast image sequence of a target object, it can first subtract the contrast image sequence from the mask image sequence to obtain a subtraction image sequence, and then obtain a volume image based on the subtraction image sequence. It can be seen that the image processing device provided by the present application only needs one reconstruction to obtain a volume image, thereby effectively improving the efficiency of obtaining the volume image.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to an image processing method, device and image processing equipment. Background Art

[0002] Digital subtraction angiography (DSA) technology can clearly show blood vessels in bones or dense soft tissues, so it is widely used in the diagnosis and treatment of vascular diseases. The main principle of DSA technology is to remove the background image overlapping with the vascular image to obtain a volume image so that doctors can make a diagnosis based on the volume image. Therefore, it is particularly important to obtain a volume image. Summary of the invention

[0003] The present application provides an image processing method, apparatus and image processing device, and the technical solution is as follows:

[0004] In one aspect, an image processing method is provided, the method comprising:

[0005] Acquire a mask image sequence and a contrast image sequence of a target area of ​​the target object;

[0006] Subtracting the contrast image sequence from the mask image sequence to obtain a subtraction image sequence;

[0007] The subtraction image sequence is reconstructed to obtain a volume image.

[0008] Optionally, subtracting the contrast image sequence from the mask image sequence to obtain a subtraction image sequence includes:

[0009] Performing registration processing on the mask image sequence and the angiography image sequence;

[0010] The contrast image sequence after the registration process is subtracted from the mask image sequence after the registration process to obtain a subtraction image sequence.

[0011] Optionally, the registering the mask image sequence and the contrast image sequence includes:

[0012] The mask image sequence and the angiography image sequence are registered by using an elastic registration method.

[0013] Optionally, reconstructing the subtraction image sequence to obtain a volume image includes:

[0014] performing artifact correction on the subtraction image sequence;

[0015] The artifact-corrected subtraction image sequence is reconstructed to obtain a volume image.

[0016] Optionally, the volume image is a volume image sequence, and after obtaining the volume image, the method further includes:

[0017] Acquire the grayscale average value, voxel volume and tissue weight of the target tissue in each image of the volume image sequence;

[0018] Determining the blood volume of the image based on the grayscale average, voxel volume and tissue weight of the target tissue in each image;

[0019] The target tissue is a tissue in a target area of ​​the target object.

[0020] Optionally, before determining the blood volume of the image based on the grayscale average value, voxel volume and tissue weight of the target tissue in each image, for each image in the volume image sequence, the method further includes:

[0021] Acquire the grayscale average value of the main blood vessels of the target tissue in the image;

[0022] Based on the grayscale average of the main blood vessels of the target tissue, the grayscale average of the target tissue is normalized.

[0023] Optionally, before acquiring the grayscale average value, voxel volume and tissue weight of the target tissue in each image in the volume image sequence, the method further includes:

[0024] Reconstructing the mask image sequence to obtain a mask volume image sequence;

[0025] identifying the location of interfering tissue in the mask volume image sequence;

[0026] The position of the interfering tissue is marked in the corresponding target volume image sequence.

[0027] Optionally, the method further comprises: color coding the blood volume of each image and displaying it in a corresponding image in the target volume image sequence.

[0028] In another aspect, an image processing device is provided, the device comprising:

[0029] An acquisition module, used for acquiring a mask image sequence and a contrast image sequence of a target area of ​​a target object;

[0030] A first generating module, used for subtracting the contrast image sequence from the mask image sequence to obtain a subtracted image sequence;

[0031] The second generating module is used to reconstruct the subtraction image sequence to obtain a volume image.

[0032] Optionally, the first generating module is used for:

[0033] Performing registration processing on the mask image sequence and the angiography image sequence;

[0034] The contrast image sequence after the registration process is subtracted from the mask image sequence after the registration process to obtain a subtraction image sequence.

[0035] Optionally, the first generating module is used for:

[0036] The mask image sequence and the angiography image sequence are registered by using an elastic registration method.

[0037] On the other hand, an image processing device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the image processing method as described in the above aspects is implemented.

[0038] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the image processing method described in the above aspects is implemented.

[0039] The beneficial effects of the technical solution provided by this application include at least:

[0040] The embodiment of the present application provides an image processing method, apparatus and image processing device. After the image processing device obtains the mask image sequence and the contrast image sequence of the target object, it can first subtract the contrast image sequence from the mask image sequence to obtain a subtraction image sequence, and then obtain a volume image based on the subtraction image sequence. It can be seen that the image processing device provided by the present application only needs one reconstruction to obtain a volume image, thereby effectively improving the efficiency of obtaining the volume image.

[0041] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of an image processing method provided by an embodiment of the present application;

[0043] Figure 2 is a flowchart of another image processing method provided by an embodiment of the present application;

[0044] Figure 3 is a structural schematic diagram of an image processing device provided in an embodiment of the present application;

[0045] Figure 4 is a structural schematic diagram of another image processing device provided in an embodiment of the present application;

[0046] Figure 5 It is a structural schematic diagram of an image processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0048] DSA technology can clearly show blood vessels in bones or dense soft tissues, so it is widely used in the diagnosis and treatment of vascular diseases. The main principle of DSA technology is to remove the background image overlapping with the vascular image to obtain a volume image so that doctors can make a diagnosis based on the volume image. Therefore, it is particularly important to obtain a volume image.

[0049] In the related art, an image processing device can obtain multiple mask images and multiple contrast images, and reconstruct the multiple mask images and multiple contrast images. Then, the image processing device can subtract the reconstructed mask image from the reconstructed contrast image to obtain a volume image.

[0050] However, since it usually takes a long time to reconstruct an image, the efficiency of obtaining volume images in the related art is low.

[0051] The present application embodiment provides an image processing method, which is applied to an image processing device, such as an image processing device in a DSA system. Figure 1 , the method comprising:

[0052] Step 101: Acquire a mask image sequence and a contrast image sequence of a target area of ​​a target object.

[0053] In an embodiment of the present application, the mask image sequence (i.e., a projection data sequence without contrast agent) and the contrast image sequence (i.e., a projection data sequence with contrast agent) may be pre-stored by an image processing device. Alternatively, the DSA system may include a DSA body connected to the image processing device. The DSA body may scan a target area of ​​the target object to obtain a mask image sequence and a contrast image sequence of the target area, and send the mask image sequence and the contrast image sequence to the image processing device. Accordingly, the image processing device may obtain the mask image sequence and the contrast image sequence.

[0054] The mask image sequence may include multiple mask image frames, each mask image frame is obtained by the DSA body scanning the target area of ​​the target object before the contrast agent is injected into the target area. The contrast image sequence may include: multiple contrast image frames corresponding to the multiple mask image frames one by one, each contrast image frame is obtained by the DSA body scanning the target area after a certain dose of contrast agent is injected into the target area by a contrast agent injector.

[0055] In the embodiment of the present application, the DSA body may include: a mechanical arm, and an X-ray emitter and an X-ray detector located at both ends of the mechanical arm, and the X-ray detector may be connected to an image processing device. The X-ray emitter may emit X-rays, and the X-ray detector may convert the X-rays into an image (the mask image or contrast image described above) after detecting the X-rays, and output the image to the image processing device.

[0056] Optionally, the robot arm may be a C-shaped arm. The target object may be a human body or a phantom. The target region may be a brain region, a neck region or a heart region. For example, the target region may be a brain region.

[0057] Step 102: subtract the contrast image sequence from the mask image sequence to obtain a subtraction image sequence.

[0058] In the embodiment of the present application, for each frame of angiography image in the angiography image sequence, the image processing device subtracts the angiography image from the corresponding mask image to obtain a subtraction image sequence, wherein the subtraction image sequence may include: multiple frames of subtraction images.

[0059] It is understandable that the multiple pixels in each frame of the contrast image correspond to the multiple pixels in each frame of the mask image. Subtracting the contrast image from the corresponding mask image means: determining the difference between the grayscale values ​​of the corresponding pixels in the contrast image and the corresponding mask image.

[0060] Step 103: reconstruct the subtraction image sequence to obtain a volume image.

[0061] After the image processing device obtains the silhouette image sequence, it can reconstruct the subtraction image sequence to obtain a volume image. The reconstruction method of the subtraction image sequence can adopt three-dimensional reconstruction. The volume image can be a tomographic image, a three-dimensional image, or a volume image sequence composed of multiple tomographic images. The tomographic image can also be called a slice image. The number of images included in the volume image sequence depends on the thickness of the tomographic image.

[0062] In summary, the embodiment of the present application provides an image processing method, after the image processing device obtains the mask image sequence and the contrast image sequence of the target object, it can first subtract the contrast image sequence from the mask image sequence to obtain a subtraction image sequence, and then obtain a volume image based on the subtraction image sequence. It can be seen that the image processing device provided in the embodiment of the present application only needs one reconstruction to obtain a volume image, thereby effectively improving the efficiency of obtaining the volume image.

[0063] Figure 2 is a flowchart of another image processing method provided by an embodiment of the present application, which can be applied to an image processing device. Figure 2 , the method may include:

[0064] Step 201: Acquire a mask image sequence and a contrast image sequence of a target area of ​​a target object.

[0065] In an embodiment of the present application, the mask image sequence and the contrast image sequence may be pre-stored by an image processing device. Alternatively, the DSA system may include a DSA body connected to the image processing device. The DSA body may scan the target area of ​​the target object to obtain the mask image sequence and the contrast image sequence of the target area, and send the mask image sequence and the contrast image sequence to the image processing device. Accordingly, the image processing device may obtain the mask image sequence and the contrast image sequence.

[0066] The mask image sequence may include multiple mask images, each of which is obtained by the DSA body scanning the target area at multiple different angles before contrast agent is injected into the target area of ​​the target object. The contrast image sequence may include: multiple contrast images corresponding to the multiple mask images, each of which is obtained by the DSA body scanning the target area at multiple different angles after a certain dose of contrast agent is injected into the target area by a contrast agent injector. Each contrast image may also be called a live image.

[0067] For example, the DSA body can obtain a perfusion scanning protocol and perform a first round scan on the target area based on the perfusion scanning protocol to obtain a mask image sequence. Then, after a certain dose of contrast agent is injected into the target area and reaches a steady state, the DSA body can perform a second round scan on the target area based on the perfusion scanning protocol to obtain a contrast image sequence.

[0068] In the embodiment of the present application, the DSA body may include: a mechanical arm, and an X-ray emitter and an X-ray detector located at both ends of the mechanical arm, and the X-ray detector may be connected to an image processing device. The X-ray emitter may emit X-rays, and the X-ray detector may convert the X-rays into an image (the mask image or contrast image described above) after detecting the X-rays, and output the image to the image processing device.

[0069] Optionally, the robot arm may be a C-shaped arm. The target object may be a human body or a phantom. The target region may be a brain region, a neck region or a heart region. For example, the target region may be a brain region.

[0070] Step 202: perform registration processing on the mask image sequence and the contrast image sequence.

[0071] Since the breathing or movement of the target object may cause the position of the target area relative to the DSA body to change, the image processing device can perform registration processing on the acquired mask image sequence and contrast image sequence, so that each mask image in the mask image sequence matches the feature point on the corresponding contrast image, so that the mask image sequence and the contrast image sequence are spatially aligned. In this way, a large number of motion artifacts can be avoided in the obtained subtraction image sequence, thereby ensuring that the generated volume image has a high contrast and good quality.

[0072] Optionally, the image processing device may use an elastic registration algorithm to process the mask image sequence and the contrast image sequence to register the mask image sequence and the contrast image sequence. Since the elastic registration algorithm is used to register the mask image sequence and the contrast image sequence at a faster speed, the efficiency of obtaining volume images can be further improved.

[0073] In the embodiment of the present application, for each mask image in the mask image sequence, the image processing device may perform feature detection on the mask image to obtain a plurality of feature points. Then, the image processing device may perform feature matching on the contrast image corresponding to the mask image based on the plurality of feature points, thereby registering the mask image with the corresponding contrast image.

[0074] Optionally, each feature point may be a key point of the target area, or may be a boundary point of a bone in the target area. The process in which the image processing device performs feature matching on the contrast image corresponding to the mask image based on the multiple feature points may include: the image processing device may select multiple (such as 2) sub-images near the feature points as template sub-images, and use a feature matching algorithm to process the template sub-image and the contrast image, so as to register the mask image with the corresponding contrast image.

[0075] It can be understood that, while ensuring the registration accuracy, reducing the size of the template sub-image can significantly reduce the time required for the image processing device to register the mask image sequence and the contrast image sequence.

[0076] Step 203: subtract the contrast image sequence after the registration processing from the mask image sequence after the registration processing to obtain a subtraction image sequence.

[0077] After the image processing device performs registration processing on the masked vascular image sequence and the angiography image sequence, for each frame of the angiography image in the registered angiography image sequence, the image processing device can subtract the angiography image from the corresponding mask image in the registered mask image sequence, thereby obtaining a subtraction image sequence of the target area of ​​the target object.

[0078] The subtraction image sequence includes multiple frames of subtraction images, and generally, each frame of the subtraction image should only contain blood vessels, that is, the subtraction image sequence is a relatively clear blood vessel image sequence of the target area.

[0079] Step 204: reconstruct the subtraction image sequence to obtain a volume image.

[0080] In the embodiment of the present application, the image processing device can perform artifact correction on the subtraction image sequence, and then reconstruct the artifact-corrected subtraction image sequence to obtain a volume image. In this way, artifacts can be effectively avoided in the generated volume image, and the quality of the reconstructed volume image can be further ensured to be good.

[0081] Optionally, the image processing device may perform artifact correction on the subtraction image sequence using an artifact correction algorithm. The artifact correction algorithm may be at least one of the following algorithms: a scattering artifact correction algorithm, a truncation artifact correction algorithm, a ring artifact correction algorithm, and a stripe artifact correction algorithm.

[0082] Among them, the scattering artifact correction algorithm can perform artifact correction based on Monte Carlo double Gaussian kernel simulation of scattering signals. The truncation artifact correction algorithm can perform artifact correction using a b-spline interpolation edge estimation method.

[0083] Step 205: Determine the blood volume based on the volume image.

[0084] In the embodiment of the present application, when the blood volume in the blood vessel is 100 milliliters (ml) / 100 grams (g), that is, when the blood vessel is full of blood, the grayscale average value of the target tissue image in the volume image is linearly related to the concentration of the contrast agent in the blood vessel included in the target tissue. Therefore, the image processing device can calculate the blood volume of the target tissue by using the ratio of the grayscale average value of the target tissue image in the volume image to the grayscale average value of the blood vessel image. The blood volume is positively correlated with the ratio. The target tissue is the tissue in the target area of ​​the target object. The target tissue image is a sub-image of the target tissue in the volume image, and the blood vessel image is a sub-image of the blood vessel in the volume image.

[0085] It can be understood that the volume image can be a volume image sequence. The process of the image processing device determining the blood volume based on the volume image can include: the image processing device obtains the grayscale average value, voxel volume and tissue weight of the brain tissue in each image in the volume image sequence, and then determines the blood volume of the image based on the grayscale average value, voxel volume and tissue weight of the brain tissue in each image. That is, the blood volume of the target tissue in the image is determined.

[0086] Furthermore, since the premise for the image processing device to calculate CBV is that the CBV of the main blood vessel of the target tissue is 100 ml / 100 g, that is, the CBV of the blood vessel = 1, the image processing device needs to use the grayscale average value of the blood vessel measured based on the volume image sequence to normalize the grayscale average value of the target tissue in the volume image sequence. Specifically, before determining the blood volume of the image based on the grayscale average value, voxel volume and tissue weight of the target tissue in each image, for each image in the volume image sequence, the image processing device can also obtain the grayscale average value of the main blood vessel of the target tissue in the image, and normalize the grayscale average value of the target tissue based on the grayscale average value of the blood vessel.

[0087] It can be understood that if the target area is the brain area, the target tissue is the brain tissue, and the main blood vessel is the large blood vessel of the brain tissue. The large blood vessel is the blood vessel connected to the collateral blood vessel and is also the blood vessel into which the contrast agent first flows. Correspondingly, the blood volume is the cerebral blood volume (CBV). For example, the CBV may satisfy:

[0088] CBV=(brain tissue GL / large blood vessel GL×voxel volume) / tissue weight×100.

[0089] The unit of CBV is ml / 100g, which means how many milliliters of blood pass through every 100 grams of brain tissue. Brain tissue GL refers to the grayscale average value of brain tissue in the volume image sequence. Large blood vessel GL refers to the grayscale average value of large blood vessels in the brain tissue in the volume image sequence.

[0090] The unit of the voxel volume is ml, and the voxel volume is positively correlated with the area of ​​the pixel in the volume image sequence. Tissue weight refers to the weight of brain tissue, which can be equal to the product of the pixel volume of brain tissue and tissue density. The unit of the pixel volume is cubic centimeter (cm 3 ), and the pixel volume can be equal to the product of the area of ​​the pixel in the volume image sequence and the layer thickness. The unit of tissue density is grams per cubic centimeter (g / cm 3 ), the tissue density refers to the mass per unit volume of tissue. Different tissues have different tissue densities. For example, the tissue density of brain tissue is about 1.05g / cm 3 .

[0091] In the embodiment of the present application, after the image processing device obtains the volume image sequence, it can first pre-process the volume image sequence, and then determine the blood volume based on the pre-processed volume image sequence. The process of pre-processing the volume image sequence may include: performing noise reduction processing on the volume image sequence, and performing image segmentation processing on the volume image sequence after the noise reduction processing to avoid the bones in the target tissue and the like affecting the calculation accuracy of the blood volume.

[0092] Optionally, the image processing device may perform smoothing processing on the volume image sequence to achieve noise reduction processing on the volume image sequence.

[0093] In the embodiment of the present application, since there may be differences in the grayscale values ​​of the air area and the bone area of ​​the mask image sequence and the contrast image sequence, there may be residual air area and bone area in the acquired subtraction image sequence, thereby affecting the accuracy of blood volume determination. Based on this, the image processing device can also perform the following operations before obtaining the grayscale average value, voxel volume and tissue weight of the brain tissue of each image in the volume image sequence based on the volume image sequence: reconstruct the mask image sequence to obtain the mask volume image sequence; identify the position of the interfering tissue in the mask volume image sequence; mark the position of the interfering tissue in the corresponding target volume image sequence. Among them, the interfering tissue may include: air area and bone area. In this way, the image processing device can use a threshold-based segmentation method combined with the position mark of the interfering tissue to perform image segmentation processing on the volume image sequence. Or in the blood volume calculation, the interfering tissue position mark area is not counted, thereby improving the accuracy of blood volume determination.

[0094] It is understandable that the image processing device can identify the position of the interfering tissue in each mask volume image in the mask volume image sequence, and mark the position of the interfering tissue in the volume image sequence and in the tomographic image corresponding to the mask volume image.

[0095] It can also be understood that the image processing device can also perform pseudo-color processing on the volume image sequence to obtain a pseudo-color image. And the image processing device can also include: a display screen, and the image processing device can display the pseudo-color image on the display screen to assist doctors in diagnosis. Among them, different colors in the pseudo-color image represent different blood flow distribution conditions. Specifically, the image processing device can color-code the blood volume of each image and display it in the corresponding image in the target volume image sequence.

[0096] It is also understandable that the order of the steps of the image processing method provided in the embodiment of the present application can be appropriately adjusted, and the steps can be increased or decreased accordingly according to the situation. For example, step 202 can be deleted according to the situation; or, step 205 can also be deleted according to the situation. Any technician familiar with the technical field can easily think of a method of change within the technical scope disclosed in this application, which should be included in the protection scope of this application, so it will not be repeated.

[0097] In summary, the embodiment of the present application provides an image processing method, after the image processing device obtains the mask image sequence and the contrast image sequence of the target object, it can first subtract the contrast image sequence from the mask image sequence to obtain a subtraction image sequence, and then obtain a volume image based on the subtraction image sequence. It can be seen that the image processing device provided in the embodiment of the present application only needs one reconstruction to obtain a volume image, thereby effectively improving the efficiency of obtaining the volume image.

[0098] The present application embodiment provides an image processing device, which can execute the image processing method provided by the above method embodiment. Figure 3 , the image processing device 300 comprises:

[0099] The acquisition module 301 is used to acquire a mask image sequence and a contrast image sequence of a target area of ​​a target object.

[0100] The first generating module 302 is used to subtract the contrast image sequence from the mask image sequence to obtain a subtracted image sequence.

[0101] The second generating module 303 is used to reconstruct the subtraction image sequence to obtain a volume image.

[0102] Optionally, the first generating module 302 may be used to:

[0103] Performing registration processing on the mask image sequence and the contrast image sequence;

[0104] The contrast image sequence after the registration process is subtracted from the mask image sequence after the registration process to obtain a subtraction image sequence.

[0105] Optionally, the first generating module 302 may be used to:

[0106] The mask image sequence and the contrast image sequence are registered using the elastic registration method.

[0107] Optionally, the second generating module 303 may be used to:

[0108] Performing artifact correction on subtracted image sequences;

[0109] The artifact-corrected subtraction image sequence is reconstructed to obtain a volume image.

[0110] Optional, see Figure 4 The image processing device may further include: a determination module 304. The determination module 304 is used to determine the blood volume based on the volume image.

[0111] Optionally, the determination module 304 may be used to:

[0112] Obtain the grayscale mean, voxel volume, and tissue weight of the target tissue for each image in the volume image sequence;

[0113] The blood volume of the image was determined based on the grayscale mean, voxel volume, and tissue weight of the target tissue in each image;

[0114] The target tissue is the tissue in the target area of ​​the target object.

[0115] Optionally, in some embodiments, the device may further include a normalization module. The normalization module is used to: for each image in the volume image sequence, obtain the grayscale average value of the main blood vessels of the target tissue in the image; and perform normalization processing on the grayscale average value of the target tissue based on the grayscale average value of the main blood vessels of the target tissue.

[0116] Optionally, in some embodiments, the device may further include a marking module. The marking module is used to: reconstruct the mask image sequence to obtain a mask volume image sequence; identify the position of the interfering tissue in the mask volume image sequence; and mark the position of the interfering tissue in the corresponding target volume image sequence.

[0117] Optionally, in some embodiments, the device may further include a coding module, which is used to color code the blood volume of each image and display it in a corresponding image in the target volume image sequence.

[0118] In summary, the embodiment of the present application provides an image processing device, which, after obtaining a mask image sequence and a contrast image sequence of a target object, can first subtract the contrast image sequence from the mask image sequence to obtain a subtraction image sequence, and then obtain a volume image based on the subtraction image sequence. It can be seen that the image processing device provided in the embodiment of the present application only needs one reconstruction to obtain a volume image, thereby effectively improving the efficiency of obtaining a volume image.

[0119] The present application embodiment provides an image processing device, see Figure 5 , the image processing device 400 includes: a processor 401 and a memory 403. The processor 401 and the memory 403 are connected, such as through a bus 402. Optionally, the controller 400 may also include a transceiver 404. It should be noted that in actual applications, the transceiver 404 is not limited to one, and the structure of the controller 400 does not constitute a limitation on the embodiments of the present application.

[0120] Processor 401 may be a CPU (Central Processing Unit), a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 401 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0121] The bus 402 may include a path to transmit information between the above components. The bus 402 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 402 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0122] The memory 403 is used to store a computer program corresponding to the image processing method of the above embodiment of the present application, and the computer program is controlled and executed by the processor 401. The processor 401 is used to execute the computer program stored in the memory 403 to implement the content shown in the above method embodiment.

[0123] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the image processing method provided in the above method embodiment is implemented. For example, Figure 1 or Figure 2 The method shown.

[0124] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0125] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0126] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0127] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0128] In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0129] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. An image processing method, characterized in that: The method comprises: Acquire a mask image sequence and a contrast image sequence of a target area of ​​the target object; Subtracting the contrast image sequence from the mask image sequence to obtain a subtraction image sequence; Reconstructing the subtraction image sequence to obtain a volume image, wherein the volume image is a volume image sequence; Acquire the grayscale average value, voxel volume and tissue weight of the target tissue of each image in the preprocessed volume image sequence, wherein the target tissue is the tissue in the target area of ​​the target object, and the preprocessing includes: noise reduction processing and image segmentation processing; Determining the blood volume of the image based on the grayscale average, voxel volume and tissue weight of the target tissue in each image; Before acquiring the grayscale average value, voxel volume and tissue weight of the target tissue in each image in the volume image sequence, the method further includes: Reconstructing the mask image sequence to obtain a mask volume image sequence; identifying the location of interfering tissue in the mask volume image sequence; The position of the interfering tissue is marked in the corresponding volume image sequence.

2. The method according to claim 1, characterized in that Subtracting the contrast image sequence from the mask image sequence to obtain a subtraction image sequence, comprising: Performing registration processing on the mask image sequence and the angiography image sequence; The contrast image sequence after the registration process is subtracted from the mask image sequence after the registration process to obtain a subtraction image sequence.

3. The method according to claim 2, characterized in that The registering process of the mask image sequence and the contrast image sequence comprises: The mask image sequence and the angiography image sequence are registered by using an elastic registration method.

4. The method according to any one of claims 1 to 3, characterized in that: Reconstructing the subtraction image sequence to obtain a volume image includes: performing artifact correction on the subtraction image sequence; The artifact-corrected subtraction image sequence is reconstructed to obtain a volume image.

5. The method according to any one of claims 1 to 3, characterized in that: Before determining the blood volume of the image based on the grayscale average value, voxel volume and tissue weight of the target tissue in each image, for each image in the volume image sequence, the method further includes: Acquire the grayscale average value of the main blood vessels of the target tissue in the image; Based on the grayscale average of the main blood vessels of the target tissue, the grayscale average of the target tissue is normalized.

6. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: The blood volume for each image is color coded and displayed in the corresponding image in the volumetric image sequence.

7. An image processing device, characterized in that: The device comprises: An acquisition module, used for acquiring a mask image sequence and a contrast image sequence of a target area of ​​a target object; A first generating module, used for subtracting the contrast image sequence from the mask image sequence to obtain a subtracted image sequence; A second generating module is used to reconstruct the subtraction image sequence to obtain a volume image, wherein the volume image is a volume image sequence; a determination module, configured to obtain the grayscale average value, voxel volume and tissue weight of the target tissue of each image in the preprocessed volume image sequence, wherein the target tissue is the tissue of the target area of ​​the target object, and the preprocessing includes: noise reduction processing and image segmentation processing; and based on the grayscale average value, voxel volume and tissue weight of the target tissue of each image, determine the blood volume of the image; A marking module is used to reconstruct the mask image sequence to obtain a mask volume image sequence before obtaining the grayscale average value, voxel volume and tissue weight of the target tissue in each image in the volume image sequence; identify the position of the interfering tissue in the mask volume image sequence; and mark the position of the interfering tissue in the corresponding volume image sequence.

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

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