Vascular imaging method, device, equipment and storage medium

Abstract

The embodiment of the present invention discloses a vascular imaging method, device, equipment and storage medium, wherein the method comprises: obtaining an original filling image and at least two original mask images of a target acquisition part of a target object; determining an original mask image in the same heart beating state as the original filling image from the at least two original mask images as a mask image to be selected; determining a target mask image corresponding to the original filling image according to the mask image to be selected; and determining a vascular subtraction image of the target acquisition part according to the original filling image and the target mask image. The technical solution of the embodiment of the present invention improves the imaging quality of the vascular subtraction image by determining a target mask image that highly matches the original filling image from multiple original mask images.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of digital subtraction angiography, and in particular to a vascular imaging method, device, equipment and storage medium. Background Art

[0002] Digital subtraction angiography (DSA) technology is now a relatively mature method for the diagnosis and treatment of cerebrovascular and cardiovascular diseases. DSA technology can intuitively see the detailed conditions of the blood vessels in the subject's area of ​​interest, which plays a vital role in surgery.

[0003] Normally, DSA subtraction technology first takes an X-ray of the area of ​​interest to the user, fixes a frame of the image without contrast agent as a mask, then adds contrast agent to obtain a real-time filling image, and finally uses the filling image to subtract the mask, theoretically obtaining an image of only blood vessels. However, because there is a time difference between the two images, and the user's body often moves during this period, whether it is spontaneous shaking of the body or involuntary movement inside the body, a large number of motion artifacts will be generated, affecting the subtraction effect.

[0004] Since the development of DSA technology, some methods have been developed to reduce motion artifacts, which have improved the display effect of blood vessels to a certain extent. However, due to the breathing and beating of the heart, the heart and abdomen of the body move more violently, and the related methods of reducing motion artifacts have a poor recovery effect. For example, in previous studies, elastic registration and other methods are often used for complex parts such as the heart. However, elastic registration and other methods are particularly computationally intensive, and the movement of the human body in three-dimensional space, the effect of calibration on a two-dimensional plane is still limited, and this problem has not been fundamentally solved. Summary of the invention

[0005] The embodiments of the present invention provide a blood vessel imaging method, apparatus, device and storage medium to achieve intelligent subtraction of blood vessel images.

[0006] In a first aspect, an embodiment of the present invention provides a blood vessel imaging method, comprising:

[0007] Acquire an original filling image and at least two original mask images of a target acquisition part of a target object;

[0008] Determine, from the at least two original mask images, an original mask image in the same heart beating state as the original filling image as a mask image to be selected;

[0009] Determine a target mask image corresponding to the original filling image according to the mask image to be selected;

[0010] A blood vessel subtraction image of the target acquisition site is determined according to the original filling image and the target mask image.

[0011] In a second aspect, an embodiment of the present invention further provides a vascular imaging device, the device comprising:

[0012] An image acquisition module is used to acquire an original filling image and at least two original mask images of a target acquisition part of a target object;

[0013] A mask image to be selected determining module, used to determine an original mask image in the same heart beating state as the original filling image from the at least two original mask images as the mask image to be selected;

[0014] A target mask image determination module, used to determine a target mask image corresponding to the original filling image according to the mask image to be selected;

[0015] The vascular subtraction image determination module is used to determine the vascular subtraction image of the target acquisition part according to the original filling image and the target mask image.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0017] one or more processors;

[0018] a storage device for storing one or more programs,

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the vascular imaging method provided by any embodiment of the present invention.

[0020] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the vascular imaging method provided by any embodiment of the present invention is implemented.

[0021] The technical solution of the embodiment of the present invention provides more original mask image options by acquiring the original filling image and at least two original mask images of the target acquisition part of the target object compared with the existing method; then, by using the original mask image in the at least two original mask images that is in the same heart beating state as the original filling image as the mask image to be selected, the influence of motion artifacts generated in the original mask image and the original filling image due to the heart beating on the subtraction effect is effectively reduced; the target mask image corresponding to the original filling image is determined according to the mask image to be selected, thereby ensuring a high degree of matching between the target mask image and the original filling image; the vascular subtraction image of the target acquisition part is determined according to the original filling image and the target mask image, with low computational cost and fast processing speed, and can effectively improve the imaging quality of the vascular subtraction image. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required to describe the embodiments. Obviously, the drawings introduced are only drawings of a part of the embodiments to be described in the present invention, rather than all the drawings. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of a flow chart of a vascular imaging method provided by an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a flow chart of a vascular imaging method provided by an embodiment of the present invention;

[0025] Figure 3 A schematic flow chart of an optional example of a vascular imaging method provided by an embodiment of the present invention;

[0026] Figure 4 A schematic diagram of the structure of a vascular imaging device provided by an embodiment of the present invention;

[0027] Figure 5 The present invention is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0029] It should also be noted that, for ease of description, only the part relevant to the present invention but not all content is shown in the accompanying drawings. It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processing or methods depicted as flow charts. Although the flow chart describes each operation (or step) as sequential processing, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. When its operation is completed, the processing can be terminated, but it can also have additional steps not included in the accompanying drawings. The processing can correspond to methods, functions, procedures, subroutines, subprograms, etc.

[0030] Figure 1 A flow chart of a vascular imaging method provided in an embodiment of the present invention is provided. This embodiment can be applied to a vascular visualization system in X-ray sequence imaging. In the case where artifacts are generated due to spontaneous shaking of the human body or involuntary movement inside the body, the method can be executed by a vascular imaging device. The device can be implemented by software and / or hardware and can be configured in a terminal and / or server to implement the vascular imaging method in the embodiment of the present invention.

[0031] like Figure 1 As shown, the method of this embodiment may specifically include:

[0032] S110: Acquire an original filling image and at least two original mask images of a target acquisition part of a target object.

[0033] The target object can be understood as the object to be imaged by vascular subtraction based on DSA technology. Typically, the target object can be a person or an animal, such as a subject or a patient. The target acquisition part can be the part to be imaged, or the part where the original filling image and the original mask image are to be acquired, which is generally a region of interest.

[0034] When the heart beats, the target object's blood vessels may also stretch and relax as the heart contracts and relaxes. Therefore, as an optional technical solution of the embodiment of the present invention, the target acquisition part may include but is not limited to the blood vessels in the heart part that are subtracted according to the heart beat, and the blood vessels in non-heart parts may also be subtracted according to the heart beat, such as the head or limbs.

[0035] The original filling image and the original mask image may be obtained by performing X-ray filming of the region of interest of the target object using digital subtraction angiography technology.

[0036] The original filling image can be a unique image left by X-ray after adding contrast agent and taking X-ray film in the area of ​​interest of the target object. Contrast agent is generally a chemical product injected (or taken) into tissues or organs to enhance the observation effect. The density of these products is higher or lower than that of the surrounding tissues, and the contrast formed is displayed by certain instruments. For example, X-ray is used to observe commonly used iodine preparations, barium sulfate, etc. Because X-rays cannot penetrate contrast agents, it is precisely by utilizing this characteristic that the image displayed by contrast agents under X-rays can be used to diagnose vascular lesions.

[0037] Exemplarily, a contrast agent is added to the target object by intravenous injection. For example, iodine is injected into the vein of the target object, and then an X-ray is taken of the area of ​​interest. After the iodine enters the blood vessel and passes through the X-ray, it will leave a unique image. The real-time filling image obtained at this time is used as the original filling image.

[0038] The original mask image can be understood as an image of a fixed frame when an X-ray is taken in the area of ​​interest of the patient without adding contrast agent. Considering that the target object may move to a certain extent during the X-ray filming, whether it is spontaneous shaking of the body or involuntary movement inside the body, motion artifacts may exist in the image, so that the subtraction effect of the mask image and the filling image is affected. Therefore, in this embodiment, two or more original mask images can be obtained to prepare for the subsequent determination of the original mask image corresponding to the original filling image.

[0039] Optionally, an X-ray is first taken of an area of ​​interest of the target object without adding a contrast agent to obtain two or more original mask images, and then a contrast agent is added into the target object, and an X-ray is taken of the area having the same pixels as the original mask, and the obtained real-time filling image is used as the original filling image.

[0040] For example, when it is necessary to view the details of the blood vessels in the heart area of ​​the target object, it is necessary to first perform an X-ray of the heart area of ​​the target object without adding contrast agent, and successfully obtain two or more original mask images; then add contrast agent into the body of the cardiovascular disease patient, and perform an X-ray of the heart area of ​​the cardiovascular disease patient. After the contrast agent is irradiated by X-rays, a unique image will be left, and a real-time filling image of the cardiovascular disease patient is obtained, that is, the original filling image of the heart area of ​​the cardiovascular disease patient is obtained.

[0041] Optionally, obtaining the original filling image and at least two original mask images of the target acquisition part of the target object may be performed by first obtaining multiple original mask images of the target acquisition part of the target object, and then obtaining the original filling image of the target acquisition part. Among them, obtaining multiple original mask images of the target acquisition part of the target object may be obtaining multiple original mask images within one cardiac cycle, or may be obtaining multiple original mask images within two or more cardiac cycles. Considering the individual differences of the target objects and the differences in equipment performance, the acquisition sequence of the original mask images and the original filling images may be determined according to actual needs, and no specific limitation is made here.

[0042] S120: Determine, from the at least two original mask images, an original mask image in the same heart beating state as the original filling image as a mask image to be selected.

[0043] Among them, the heart beating state can be understood as the state presented by the heart with its contraction movement and relaxation movement. In the embodiment of the present invention, it can be specifically that the heart movement information is divided into at least two heart beating states according to the contraction degree and relaxation degree of the heart in each cardiac cycle; or, the contraction period and diastole period of the heart in each cardiac cycle can be determined, and the heart movement information is divided into at least two heart beating states according to the total contraction time corresponding to the contraction period of the heart and the total relaxation time corresponding to the diastole period. For example, it can be specifically that the total contraction time and the total relaxation time are divided according to a preset fixed time to obtain a plurality of divided time periods, and the heart movement information in each of the divided time periods is used as a unified heart beating state. In other words, each time period can correspond to a heart beating state; or, optionally, at least two heart beating states are divided according to the amplitude fluctuation range of the heart beating waveform of each cardiac cycle.

[0044] The mask image to be selected may be an original mask image in the same heart beating state as the original filling image. It is understandable that in practical applications, affected by image acquisition parameters, the number of the mask images to be selected may be one, two or more.

[0045] Optionally, it is determined whether the original mask image and the original filling image are in the same heart beating state by using the acquired time information of the original mask image and the acquired time information of the original filling image.

[0046] Optionally, according to the image acquisition frequencies of the original filling image and the original mask image and the cardiac cycle of the target object, an original mask image in the same cardiac beating state as the original filling image is determined from at least two original mask images as the mask image to be selected.

[0047] Optionally, whether the original mask image and the original filling image are in the same heart beating state is determined by the similarity between the original mask image and the original filling image. The similarity between the original mask image and the original filling image can be determined by at least one of a Pearson correlation coefficient method, a cross-correlation coefficient method, and a maximum subtraction histogram energy method.

[0048] Optionally, when acquiring the original mask image and the original filling image, the physiological signal of the target object is acquired in real time, and then the physiological signal corresponding to the original mask image and the original filling image is used to determine whether the original mask image and the original filling image are in the same heart beating state. The physiological signal can be at least one of an electroencephalogram signal, an electrocardiogram signal, a pulse signal or a signal extracted from other modal images.

[0049] In addition, the original mask image in the same heart beating state as the original filling image can be determined by low-frequency information of one original mask image among the multiple original mask images and average high-frequency information of the high-frequency information of the multiple original mask images. Alternatively, the multiple original mask images are iteratively processed, for example, a preset proportion of image information in each original mask image is selected for processing to determine the original mask image in the same heart beating state as the original filling image.

[0050] Optionally, before determining the original mask image in the same heart beating state as the original filling image from at least two original mask images as the mask image to be selected, the original filling image and the at least two original mask images may be preprocessed, and then, the original mask image in the same heart beating state as the preprocessed original filling image may be determined from the at least two preprocessed original mask images as the mask image to be selected, in preparation for the subsequent determination of the target mask image. Before determining the original mask image in the same heart beating state as the original filling image from at least two original mask images as the mask image to be selected, the original filling image and the at least two original mask images may be preprocessed respectively, and the number of preprocessed images may be relatively large at this time; the advantage of such operation is that the screened original mask image may be obtained, which provides a more accurate selection range for the subsequent selection of the original mask image in the same heart beating state as the original filling image.

[0051] Optionally, after determining an original mask image in the same heart beat state as the original filling image from at least two original mask images as the mask image to be selected, the original filling image and the mask image to be selected are preprocessed, so as to subsequently determine the target mask image.

[0052] Of course, it is also possible to combine the above two preprocessing opportunities, and before determining the original mask image in the same heart beating state as the original filling image from at least two original mask images as the mask image to be selected, preprocess the original filling image and the at least two original mask images, and after determining the original mask image in the same heart beating state as the original filling image from at least two original mask images as the mask image to be selected, preprocess the original filling image and the mask image to be selected. After determining the original mask image in the same heart beating state as the original filling image from at least two original mask images as the mask image to be selected, preprocess the original mask image and the mask image to be selected, respectively. The advantage of such operation is that the number of preprocessed images is relatively small at this time, and a target mask image corresponding to the original filling image can be determined from the two or more mask images to be selected.

[0053] Among them, preprocessing can be understood as the operation of initializing the original mask image and the original filling image, the purpose of which is to make the original mask image and the original filling image better match. Exemplarily, the preprocessing method can be to perform logarithm (also known as Log transformation) and / or noise reduction on the image. Among them, the noise reduction method can be multi-scale noise reduction, etc.

[0054] Specifically, preprocessing the original filling image and the at least two original mask images respectively may include: performing logarithmic transformation processing on the original filling image and the at least two original mask images respectively, and filtering processing on the original filling image and the at least two original mask images after the logarithmic transformation processing respectively.

[0055] Because X-rays present an exponentially decaying energy distribution, the log transform can more clearly reflect the distribution information of each result; the second step is noise reduction. X-rays themselves have a lot of noise, including pulse noise, Gaussian noise, etc. The purpose of noise reduction is to make the original mask image and the original filled image match more accurately. For example, at least one of the filtering methods such as mean filtering, Gaussian filtering, bilateral filtering and trilateral filtering can be used.

[0056] Specifically, adaptive trilateral filtering can be used to reduce noise on the original mask image and the original filled image to better retain the image edge information. In the process of implementing the present invention, the inventor found that bilateral filtering takes into account the grayscale value of the image itself and a spatial distribution, that is, the closer the distance, the greater the weight. Although this noise removal effect is very good, it will also remove the corresponding spatial structure information. Because the technical solution of the embodiment of the present invention adds a third weight information, that is, the pulse weight. The definition of the pulse weight is to first determine whether the point is an image edge point. If it is an edge point and the signal of the edge point is a pulse signal, the bilateral filter coefficient can be increased at this time to achieve better denoising ability.

[0057] It should be noted that, the original filling image and the at least two original mask images may be filtered first, respectively, and then the filtered original filling image and the at least two original mask images may be logarithmically transformed.

[0058] S130: Determine a target mask image corresponding to the original filling image according to the mask image to be selected.

[0059] The target mask image can be understood as a mask image to be selected that is determined from the mask images to be selected and that is more matched with the original filling image. It can be understood that the original mask image determined from the at least two original mask images that is in the same heart beating state as the original filling image may be one, two or more, that is, the number of mask images to be selected may be one, two or more. At this time, the method of determining the target mask image may be, when the number of mask images to be selected is one, the mask image to be selected is used as the target mask image corresponding to the original filling image; when the number of mask images to be selected is two or more, the mask image to be selected corresponding to the original filling image is determined from two or more mask images to be selected.

[0060] Among them, determining the candidate mask image corresponding to the original filling image from two or more candidate mask images can be, optionally one image from the two or more candidate mask images is selected as the candidate mask image corresponding to the original filling image, or, an image with fewer image artifacts is selected from the two or more candidate mask images as the candidate mask image corresponding to the original filling image, or, according to the image acquisition time of each candidate mask image, the acquisition time of the original filling image and the cardiac cycle, determining the candidate mask image corresponding to the original filling image from two or more candidate mask images.

[0061] S140: Determine a blood vessel subtraction image of the target acquisition site according to the original filling image and the target mask image.

[0062] Among them, the vascular subtraction image can be obtained by subtracting the target mask image from the original filling image. After the two images are subtracted, a vascular subtraction image with only blood vessels can be obtained;

[0063] Specifically, the overlapping parts of the original filling image and the target mask image are identified, and the overlapping parts can be understood as all the information in the target mask image; and the parts in the original filling image corresponding to the target mask image are subtracted, so that the blood vessels to which the contrast agent has been added in the original filling image can be avoided, and then a vascular subtraction image of only the blood vessels is obtained.

[0064] After the vascular subtraction image of the target acquisition part is determined according to the original filling image and the target mask image, the method further includes: performing image post-processing on the vascular subtraction image to obtain a target vascular image. There are multiple ways to post-process the image, which may include but are not limited to at least one of the following processes: noise removal, motion artifact reduction, stretching, shrinking, and enhancement of the vascular subtraction image. Exemplarily, an S-curve may be used to stretch or shrink different grayscale ranges of the vascular subtraction image. The advantage of such an operation is that the contrast of the blood vessels in the vascular subtraction image can be improved by performing image post-processing on the vascular subtraction image, thereby optimizing the subtraction effect of the vascular subtraction image.

[0065] Optionally, the blood vessel subtraction image is stretched based on a color lookup table curve to obtain a target blood vessel image.

[0066] Among them, the color lookup table curve is also called the LUT curve, and the LUT curve can be used to stretch the image to highlight the vascular image. The LUT can be a mapping table of pixel grayscale values. Specifically, the pixel grayscale value of the vascular subtraction image is transformed by certain transformations, such as threshold, inversion, binarization, contrast adjustment and linear transformation, to obtain another grayscale value corresponding to the pixel grayscale value of the vascular subtraction image. The above operation can highlight the useful information in the vascular subtraction image and enhance the light contrast of the vascular subtraction image.

[0067] Optionally, after stretching the vascular subtraction image based on the color lookup table curve, the method may further include: performing image enhancement processing on the stretched vascular subtraction image, and then using the vascular subtraction image after image enhancement processing as the target vascular image.

[0068] Image enhancement can be understood as a method of using image pyramids to interpret images at multiple resolutions. Image pyramids include Gaussian pyramids and Laplacian pyramids, etc. Image pyramids can be used to improve the display quality of images to facilitate information extraction and recognition. For example, unnecessary or interfering information can be removed and the required information can be highlighted, which is conducive to image analysis and interpretation or further processing.

[0069] The technical solution of this embodiment provides more original mask image options by acquiring an original filling image and at least two original mask images of a target acquisition part of a target object compared to the existing method; then, by using an original mask image in the same heart beating state as the original filling image among the at least two original mask images as a mask image to be selected, the influence of motion artifacts generated in the original mask image and the original filling image due to heart beating on the subtraction effect is effectively reduced; a target mask image corresponding to the original filling image is determined according to the mask image to be selected, thereby ensuring a high degree of matching between the target mask image and the original filling image; a vascular subtraction image of the target acquisition part is determined according to the original filling image and the target mask image, with low computational cost and fast processing speed, and the imaging quality of the vascular subtraction image can be effectively improved.

[0070] Figure 2 A flow chart of another vascular imaging method provided by an embodiment of the present invention. Based on any optional technical solution in the embodiment of the present invention, optionally, determining an original mask image in the same heart beating state as the original filling image from the at least two original mask images as the mask image to be selected includes: determining the image acquisition frequency of the original filling image and each of the original mask images and the cardiac cycle of the target object; and determining, according to the cardiac cycle and the image acquisition frequency, an original mask image in the same heart beating state as the original filling image from the at least two original mask images as the mask image to be selected.

[0071] On the basis of any optional technical solution of the present invention, optionally, determining the target mask image corresponding to the original filling image according to the mask image to be selected includes: if there are two or more mask images to be selected, determining the target mask image corresponding to the original filling image from the two or more mask images to be selected based on a preset discrimination method;

[0072] On the basis of any optional technical solution of the present invention, optionally, determining the vascular subtraction image of the target acquisition part according to the original filling image and the target mask image includes: rigidly registering the original filling image and the target mask image, and subtracting the registered original filling image from the target mask image to obtain the vascular subtraction image of the target acquisition part.

[0073] Among them, the technical terms or technical features that are the same as those in the previous embodiments are not repeated in this embodiment.

[0074] like Figure 2 As shown, the method of this embodiment may specifically include:

[0075] S210: Acquire an original filling image and at least two original mask images of a target acquisition part of a target object.

[0076] S220: Determine the image acquisition frequency of the original filling image and the original mask image and the cardiac cycle of the target object.

[0077] The image acquisition frequency can be understood as the number of images that can be acquired per unit time. The image acquisition frequency of the original filling image and the image acquisition frequency of the original mask image can be the same or different. In the embodiment of the present invention, the image acquisition frequency can be set according to actual needs and is not specifically limited here. Generally, the image acquisition frequency is set before acquiring the original filling image and the original mask image.

[0078] Wherein, the cardiac cycle can be understood as the process experienced by the cardiovascular system from the start of one heartbeat to the start of the next heartbeat. Specifically, each contraction and relaxation of the heart constitutes a cardiac cycle. Specifically, the changes in ventricular pressure, ventricular volume, blood flow and valve activity in each phase of the cardiac cycle, such as the changes in ventricular contraction and relaxation activity of the ventricle as the center, the entire cardiac cycle is carried out in eight phases. The eight phases can be isovolumetric contraction, rapid ejection phase, slow ejection phase, pre-diastole, isovolumetric relaxation phase, rapid filling phase, slow filling phase and atrial contraction phase.

[0079] In the disclosed embodiment, each phase in each cardiac cycle can be regarded as a heart beating state, that is, eight heart beating states can be divided in each cardiac cycle. Of course, two adjacent phases can also be regarded as a unified beating state. Furthermore, by determining whether the original mask image and the original filling image are in the same phase, it is determined whether the original mask image and the original filling image are in the same heart beating state.

[0080] The time it takes for a cardiac cycle to complete is determined by the heart rate. The heart rate is an important indicator of the health of the cardiovascular function of the human body. For example, if the heart rate is 75 beats / minute, the time it takes to complete a cardiac cycle is 0.8 seconds.

[0081] Specifically, the heart rate value can be obtained through detection technologies such as Weibo radar's heart beat recognition technology, image capture's heart beat recognition technology, and visualized image's heart beat recognition technology.

[0082] Among them, the heart beat recognition technology of the microwave radar can be to use the microwave radar to collect heart beat signals, divide the periodic signal sequence into discrete frames; and then use signal processing and other technologies to extract the heart beat to obtain the heart rate value.

[0083] Among them, the image captured heart beat recognition technology can be understood as a heart rate measurement method based on image capture, which obtains the characteristic vector T of the detection space and the threshold ε for conversion to a binary image; obtains a video of heart rate detection; extracts each frame image F1, F2, ..., Fx from the video; obtains a grayscale image Gx of Fx in the detection space; obtains a grayscale image Ry of the difference with G1 from Gx; extracts the change area My of each blood vessel image; extracts the value Ky of the change area in the image; extracts the period Tr of the change characteristic value obtained from the change Ky; and obtains the heart rate value HR.

[0084] The heart beat recognition technology of the visual image can be understood as a non-contact real-time heart rate measurement system based on LabVIEW. The face image is captured by an ordinary camera, and the three primary colors of each frame are separated to generate three-channel images of R (red), G (green), and B (blue). The face is recognized by the skin color model, the ROI area of ​​the face is determined, and the mean of the G channel is extracted. The baseline drift of the signal is eliminated and the peak of the signal is detected by combining wavelet transform, and finally the measured heart rate value is obtained.

[0085] It should be noted that in a short period of time and without strenuous exercise, the cardiac cycle is a relatively stable periodic change. Combined with the image acquisition frequency of obtaining the original filling image and each original mask image, the image acquisition frequency of the original filling image and each original mask image and the cardiac cycle of the target object can be determined.

[0086] S230: According to the cardiac cycle and the image acquisition frequency, determine, from the at least two original mask images, an original mask image in the same heart beating state as the original filling image as a mask image to be selected.

[0087] As mentioned above, different heart beating states can be divided in each cardiac cycle, and the image acquisition times of the original filling image and each original mask image can be determined respectively according to the image acquisition frequencies of the original filling image and the original mask image. Furthermore, the heart beating state corresponding to the original filling image can be determined according to the image acquisition time of the original filling image and the time range corresponding to each heart beating state in each cardiac cycle. Furthermore, the heart beating state corresponding to each original filling image can be determined respectively according to the image acquisition time of each original mask image and the time range corresponding to each heart beating state in each cardiac cycle. Furthermore, the original mask image in the same heart beating state as the original filling image can be determined.

[0088] It should be noted that if the original mask image in the same cardiac state as the original filling image is not determined based on the pre-set rules, the original mask image whose image acquisition time is closest to the time period corresponding to the cardiac state in which the original filling image is located can be used as the mask image to be selected. Specifically, the target time period corresponding to the cardiac state that is the same as the cardiac state in which the original filling image is located in each cardiac cycle can be determined respectively; the absolute value of the time difference between the time endpoints of the image acquisition time of each original mask image in the cardiac cycle to which it belongs and the image acquisition time of the adjacent original mask image in the target time period corresponding to the cardiac cycle is calculated, and the original mask image corresponding to the smallest absolute value among the calculated absolute values ​​is used as the original mask image in the same cardiac state as the original filling image, that is, as the mask image to be selected.

[0089] S240: If the number of the determined mask images to be selected is two or more, determine a target mask image corresponding to the original filling image from the two or more mask images to be selected based on a preset discrimination method.

[0090] Wherein, the preset discrimination method includes at least one of a Pearson correlation coefficient method, a mutual correlation coefficient method and a maximum subtraction histogram energy method.

[0091] Among them, the Pearson correlation coefficient method can be understood as a method for calculating linear correlation. The Pearson correlation coefficient can reflect the degree of linear correlation between two random variables, so the Pearson correlation coefficient can be used to judge the degree of Pearson correlation between two variables; wherein, the Pearson correlation is also called product difference correlation or matrix correlation. The Pearson correlation coefficient can be implemented through Matlab. Specifically, when the variables in the two images are linearly correlated, the Pearson correlation coefficient can show the degree of linear correlation between the two images, that is, the absolute value of the Pearson correlation coefficient is large, indicating a strong correlation; the absolute value of the Pearson correlation coefficient is small, indicating a weak correlation.

[0092] Exemplarily, when there are two or more mask images to be selected, a target mask image corresponding to the original filling image can be determined from the two or more mask images to be selected based on the Pearson correlation coefficient method; specifically, the Pearson correlation coefficient method is used to calculate the absolute value of the Pearson correlation coefficient of the variables in the original filling image and each mask image to be selected, so as to determine the correlation between the mask image to be selected and the original filling image, and the mask image to be selected with the strongest correlation with the original filling image is used as the target mask image. In order to ensure accurate determination, further, a scatter plot of the original filling image and the mask image to be selected can be drawn, that is, the absolute value of the Pearson correlation coefficient and the scatter plot are used together to analyze the degree of correlation between the original filling image and the mask image to be selected.

[0093] Among them, the mutual correlation coefficient method can be a method for studying the degree of linear correlation between variables. The mutual correlation coefficient method is based on Gaussian distribution, and the mutual correlation coefficient or mutual information can be used as the basis for analyzing the degree of linear correlation between variables. At this time, the mutual correlation coefficient and mutual information are equivalent, and there is a conversion formula between the mutual correlation coefficient and mutual information. Specifically, when the mutual correlation coefficient between two variables is zero, it means that the two variables are independent of each other and the mutual information is 0; when the mutual correlation coefficient is ±1\pm1±1, it means that the two variables are completely correlated and the mutual information is infinite.

[0094] Exemplarily, when there are two or more mask images to be selected, a target mask image corresponding to the original filling image can be determined from the two or more mask images to be selected based on the cross-correlation coefficient method. Specifically, the cross-correlation coefficient method is used to calculate the cross-correlation coefficients of the variables in the original filling image and each mask image to be selected, and then the mask image to be selected that is most correlated with the original filling image is determined as the target mask image according to the cross-correlation coefficient.

[0095] Among them, the maximum subtraction histogram energy method is also called grayscale histogram comparison, which is a simple and practical method. Specifically, the grayscale histogram can be understood as an image that statistically analyzes the grayscale distribution in an image through a grayscale distribution function; by viewing the distribution of the image grayscale histogram, the frequency of a certain grayscale in the image can be intuitively understood. Specifically, obtaining the grayscale histogram of an image can be achieved by any one of Matlab, Opencv and Python.

[0096] Exemplarily, when the number of mask images to be selected is two or more, a target mask image corresponding to the original filling image can be determined from the two or more mask images to be selected based on the maximum subtraction histogram energy method. Specifically, the maximum subtraction histogram of the original filling image and each mask image to be selected is determined respectively, and the histogram overlap of the original filling image and each mask image to be selected is calculated respectively, and then the target mask image corresponding to the original filling image is determined according to the histogram overlap.

[0097] Since the original filling image and the mask image to be selected are in a relatively close cardiac state, except that the original filling image has more vascular images than the mask image to be selected, when converted into a histogram, the original filling image and the mask image to be selected are extremely close, except that the original filling image has more vascular information than the mask image to be selected. The above method can be used to identify the correlation between the original filling image and the mask image to be selected.

[0098] It should be noted that the target mask image corresponding to the original filling image can be determined by one of the following methods: the Pearson correlation coefficient method, the mutual correlation coefficient method, and the maximum subtraction histogram energy method. Alternatively, two or more methods may be used in combination to determine the effect of the target mask image corresponding to the original filling image.

[0099] S250, rigidly registering the original filling image with the target mask image, and subtracting the registered original filling image from the target mask image to obtain a vascular subtraction image of the target acquisition site.

[0100] Rigid registration can be understood as a process in which the physical properties of the two images do not change, but only their spatial positions and postures change, so as to align the two images. Specifically, in the process of rigid registration, a set of optimal rotation and translation matrices can be calculated, and the original filling image and the target mask image can be aligned.

[0101] Optionally, the rigid registration of the original filling image and the target mask image is achieved by pixel shifting.

[0102] Optionally, rigid registration can also be achieved through a registration algorithm, which mainly includes the following: ICP (iterative nearest point matching algorithm) and its variants, NDT (normal distribution transformation), Super4CS, Deep Learning (a method based on deep learning), Deep Closest Point, and Deep ICP.

[0103] Exemplarily, first, feature extraction is performed on the original filling image and the target mask image to obtain the feature points of each of the original filling image and the target mask image; by performing similarity measurement on the two images, matching feature point pairs are found; then, spatial coordinate transformation parameters of the original filling image and the target mask image are obtained through the matching feature point pairs; finally, rigid alignment of the images is performed based on the coordinate transformation parameters of the original filling image and the target mask image.

[0104] Among them, the original filling image and the target mask image are rigidly registered, which has a very important application value in clinical medicine. Specifically, some organs in the human body basically do not deform within a certain image acquisition time interval, such as the brain; some organs will have autonomous movement and internal distortion within the corresponding time interval, such as the lungs and the heart, which makes the filling images and mask images collected at different times have differences in shape, size, etc., which leads to differences in the selection of registration methods for medical images of different organs. Therefore, when registering images collected from different parts of the human body, a method that conforms to the characteristics of the part is used, which can be a rigid registration method or a non-rigid registration method, which is not limited here.

[0105] The technical solution of this embodiment determines, from at least two original mask images, an original mask image in the same heart beating state as the original filling image as a to-be-selected mask image, according to the image acquisition frequency of the original filling image and each original mask image and the cardiac cycle of the target object, fully considering the periodic characteristics of the heart beating, and preliminarily screening out the original mask image corresponding to the original filling image; further, when there are two or more to-be-selected mask images determined, the to-be-selected mask images are screened again based on a preset discrimination method to determine a target mask image corresponding to the original filling image, thereby achieving accurate screening of the original mask image and effectively ensuring the matching degree of the target mask image with the original filling image; moreover, after the original filling image and the target mask image are rigidly registered, the original filling image and the target mask image are subtracted to obtain a vascular subtraction image of the target acquisition site, thereby achieving a technical effect of further improving the image subtraction quality.

[0106] Figure 3 This is a flow chart of a vascular imaging method in an application scenario provided by an embodiment of the present invention. In order to make those skilled in the art further understand the technical solution of the embodiment of the present invention, a specific application scenario example is given below, taking the example of researchers taking X-rays of the heart area of ​​a subject to obtain a vascular subtraction image thereof as an example.

[0107] The method of the embodiment of the present invention may include the following steps:

[0108] First, multiple original mask images of the target object are collected by DSA technology, and then, after contrast agent is used on the target object, the original filling image of the target acquisition part is collected. In order to improve the image quality and ensure the accuracy of matching, the collected original mask image and original filling image can be preprocessed by image preprocessing to obtain the preprocessed original mask image and original filling image. Among them, the image preprocessing method can be Log transformation and multi-scale noise reduction processing.

[0109] Then, for the preprocessed original mask image and original filling image, the heart beat recognition technology is used to intelligently identify the original mask image in the same heart beat state as the original filling image as a candidate mask image, and then, based on the candidate mask image, the candidate mask image that best matches the original filling image is further determined as the target mask image. In other words, the optimal original mask image is determined for the original mask image as the target mask image. At this time, the target mask image and the original filling image are already relatively similar, which lays a foundation for improving the image subtraction quality.

[0110] Furthermore, the target mask image can be corrected by using the original filling image as a reference and image correction techniques such as pixel shift to align the target mask image with the original filling image. Then, the original filling image and the corrected target mask image are subtracted to obtain a vascular subtraction image of the target acquisition site.

[0111] Finally, the vascular subtraction image can also be post-processed by a preset image post-processing method. For example, the image is first stretched using the LUT curve to highlight the vascular image. The second step is to enhance the image. Specifically, Gaussian and Laplace pyramids can be used for image enhancement, thereby further improving the display effect of the blood vessels in the vascular subtraction image.

[0112] The technical solution of this embodiment can accurately obtain the original mask image that is the best match for the original filling image by combining the heart beat recognition technology with the DSA technology, and then combine the image preprocessing, pixel shifting and image post-processing methods to achieve high-quality image subtraction effects, and comprehensively improve the imaging quality of vascular subtraction images from multiple angles.

[0113] Figure 4 A schematic diagram of the structure of a vascular imaging device provided in an embodiment of the present invention, the device can execute the vascular imaging method described in the above embodiment, and the device may include: an image acquisition module 410, a to-be-selected mask image determination module 420, a target mask image determination module 430 and a vascular subtraction image determination module 440.

[0114] Among them, the image acquisition module 410 is used to acquire the original filling image and at least two original mask images of the target acquisition part of the target object; the selected mask image determination module 420 is used to determine the original mask image in the same heart beating state as the original filling image from the at least two original mask images as the selected mask image; the target mask image determination module 430 is used to determine the target mask image corresponding to the original filling image according to the selected mask image; the vascular subtraction image determination module 440 is used to determine the vascular subtraction image of the target acquisition part according to the original filling image and the target mask image.

[0115] The technical solution of the embodiment of the present invention provides more original mask image options by acquiring the original filling image and at least two original mask images of the target acquisition part of the target object compared with the existing method; then, by using the original mask image in the at least two original mask images that is in the same heart beating state as the original filling image as the mask image to be selected, the influence of motion artifacts generated in the original mask image and the original filling image due to the heart beating on the subtraction effect is effectively reduced; the target mask image corresponding to the original filling image is determined according to the mask image to be selected, thereby ensuring a high degree of matching between the target mask image and the original filling image; the vascular subtraction image of the target acquisition part is determined according to the original filling image and the target mask image, with low computational cost and fast processing speed, and can effectively improve the imaging quality of the vascular subtraction image.

[0116] Based on any optional technical solution of the embodiment of the present invention, optionally, the module for determining the mask image to be selected is used to:

[0117] Determining the image acquisition frequency of the original filling image and each of the original mask images and the cardiac cycle of the target object;

[0118] According to the cardiac cycle and the image acquisition frequency, an original mask image in the same heart beating state as the original filling image is determined from the at least two original mask images as a mask image to be selected.

[0119] Based on any optional technical solution of the embodiment of the present invention, optionally, the target mask image determination module is used to:

[0120] If the number of the mask images to be selected is two or more, a target mask image corresponding to the original filling image is determined from the two or more mask images to be selected based on a preset discrimination method.

[0121] Based on any optional technical solution of the embodiment of the present invention, optionally, the vascular imaging device further includes: an image preprocessing module and an image updating module.

[0122] Among them, the image preprocessing module is used to preprocess the original filling image and the at least two original mask images respectively before determining the original mask image in the same heart beating state as the original filling image from the at least two original mask images as the mask image to be selected; the image updating module is used to update the original filling image and the at least two original mask images respectively according to the preprocessing results.

[0123] Based on any optional technical solution of the embodiment of the present invention, optionally, the image preprocessing module is used to:

[0124] The original mask image and the at least two original filling images are logarithmically transformed, and the original mask image and the at least two original filling images after the logarithmic transformation are filtered.

[0125] Based on any optional technical solution of the embodiment of the present invention, optionally, the vascular subtraction image determination module is used to:

[0126] The original filling image and the target mask image are rigidly registered, and the registered original filling image and the target mask image are subtracted to obtain a blood vessel subtraction image of the target acquisition site.

[0127] Based on any optional technical solution of the embodiment of the present invention, optionally, the vascular imaging device further includes:

[0128] The image enhancement processing module is used to, after determining the vascular subtraction image of the target acquisition part according to the original filling image and the target mask image, stretch the vascular subtraction image based on a color lookup table curve, and perform image enhancement processing on the stretched vascular subtraction image to obtain a target vascular image.

[0129] The above-mentioned vascular imaging device can execute the vascular imaging method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the executed vascular imaging method.

[0130] Figure 5 The present invention is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 5 A block diagram of an exemplary electronic device 12 suitable for use in implementing embodiments of the present invention is shown. Figure 5 The electronic device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0131] like Figure 5As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0132] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0133] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0134] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, usually called a "hard drive"). Although Figure 5 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0135] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0136] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 20. Figure 5 As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although Figure 5 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0137] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing a blood vessel imaging method provided by the embodiment of the present invention.

[0138] An embodiment of the present invention also provides a storage medium containing computer executable instructions, which, when executed by a computer processor, are used to execute a vascular imaging method, the method comprising: acquiring an original filling image and at least two original mask images of a target acquisition part of a target object; determining, from the at least two original mask images, an original mask image in the same heart beating state as the original filling image as a to-be-selected mask image; determining a target mask image corresponding to the original filling image based on the to-be-selected mask image; and determining a vascular subtraction image of the target acquisition part based on the original filling image and the target mask image.

[0139] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0140] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0141] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0142] Computer program code for performing the operation of embodiments of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0143] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A vascular imaging method, characterized in that: include: Acquire an original filling image and at least two original mask images of a target acquisition part of a target object; Determine, from the at least two original mask images, an original mask image in the same heart beating state as the original filling image as a mask image to be selected; Determine a target mask image corresponding to the original filling image according to the mask image to be selected; Determine a vascular subtraction image of the target acquisition site according to the original filling image and the target mask image; The step of determining, from the at least two original mask images, an original mask image in the same heart beating state as the original filling image as a mask image to be selected includes: Determining the image acquisition frequency of the original filling image and the original mask image and the cardiac cycle of the target object; According to the cardiac cycle and the image acquisition frequency, determining, from the at least two original mask images, an original mask image in the same heart beating state as the original filling image as a mask image to be selected; The step of determining, according to the cardiac cycle and the image acquisition frequency, from the at least two original mask images, an original mask image in the same heart beating state as the original filling image as a mask image to be selected, comprises: respectively determining a target time period corresponding to a heart beating state that is the same as the heart beating state of the original filling image in each cardiac cycle; Determine the cardiac cycle to which the image acquisition time of each original mask image belongs, and calculate the absolute value of the time difference between the time endpoints of the image acquisition time of adjacent original mask images in the target time period corresponding to the cardiac cycle; The original mask image corresponding to the smallest absolute value among the absolute values ​​is used as a mask image to be selected in the same heart beating state as the original filling image.

2. The method according to claim 1, characterized in that The step of determining a target mask image corresponding to the original filling image according to the mask image to be selected includes: If the number of the mask images to be selected is two or more, a target mask image corresponding to the original filling image is determined from the two or more mask images to be selected based on a preset discrimination method.

3. The method according to claim 1, characterized in that Before determining, from the at least two original mask images, an original mask image in the same heart beating state as the original filling image as the mask image to be selected, the method further includes: The original filling image and the at least two original mask images are preprocessed respectively, and the original filling image and the at least two original mask images are updated respectively according to the preprocessing results.

4. The method according to claim 3, characterized in that The preprocessing of the original mask image and the at least two original filling images respectively includes: The original filling image and the at least two original mask images are logarithmically transformed, and the original filling image and the at least two original mask images after the logarithmic transformation are filtered.

5. The method according to claim 1, characterized in that The step of determining the vascular subtraction image of the target acquisition part according to the original filling image and the target mask image includes: The original filling image and the target mask image are rigidly registered, and the registered original filling image and the target mask image are subtracted to obtain a blood vessel subtraction image of the target acquisition site.

6. The method according to claim 1, characterized in that After determining the vascular subtraction image of the target acquisition part according to the original filling image and the target mask image, the method further includes: The blood vessel subtraction image is stretched based on the color lookup table curve to obtain a target blood vessel image.

7. A vascular imaging device, characterized in that: include: An image acquisition module is used to acquire an original filling image and at least two original mask images of a target acquisition part of a target object; A mask image to be selected determining module, used to determine an original mask image in the same heart beating state as the original filling image from the at least two original mask images as the mask image to be selected; A target mask image determination module, used to determine a target mask image corresponding to the original filling image according to the mask image to be selected; A vascular subtraction image determination module, used to determine the vascular subtraction image of the target acquisition part according to the original filling image and the target mask image; The module for determining the mask image to be selected is used to: determine the image acquisition frequencies of the original filling image and the original mask image and the cardiac cycle of the target object; determine, according to the cardiac cycle and the image acquisition frequency, from the at least two original mask images, an original mask image in the same cardiac beating state as the original filling image as the mask image to be selected; respectively determine the target time period corresponding to the same cardiac beating state as the cardiac beating state of the original filling image in each cardiac cycle; determine the cardiac cycle to which the image acquisition time of each original mask image belongs, and calculate the absolute value of the time difference between the time endpoints of the image acquisition times of adjacent original mask images in the target time period corresponding to the cardiac cycle; and use the original mask image corresponding to the smallest absolute value among the absolute values ​​as the mask image to be selected in the same cardiac beating state as the original filling image.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the blood vessel imaging method according to any one of claims 1 to 6.

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

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