Vessel recognition monitoring method and system based on static ct enhanced scan

Abstract

This invention discloses a method and system for vascular identification and monitoring based on static CT enhanced scanning. The method includes the following steps: acquiring subtraction images of the entire region of the patient's vessel to be monitored; processing the subtraction images to divide each image into multiple regions of preset size, with each region corresponding to a specific location; calculating the mean and standard deviation for each region of each subtraction image and determining the p-value; arranging the p-values ​​of the same region in each subtraction image in chronological order and plotting a p-value change curve for each region; and plotting a contrast agent concentration change curve based on the p-value change curve to determine the peak contrast agent time. This method can accurately determine the location and trend of the monitored vessel, avoiding situations where the ROI (Region of Interest) area circled by the doctor deviates from the original vessel, resulting in inaccurate CT values ​​and missing the peak contrast agent time.

Description

Technical Field

[0001] This invention relates to a method for vascular identification and monitoring based on static CT enhanced scanning, and also to a vascular identification and monitoring system for implementing this method, belonging to the field of medical imaging technology. Background Technology

[0002] Contrast-enhanced scanning increases the density difference between diseased tissue and adjacent normal tissue, thereby increasing the likelihood of lesion detection. However, individual differences exist among patients during contrast-enhanced scanning, leading to inconsistencies in the time to peak concentration of the intravascular contrast agent. If physicians directly use empirical values ​​to set the peak concentration time, they may miss the optimal time for the three-phase scan. Therefore, a low-dose contrast-enhanced scanning mode is necessary to accurately determine the peak concentration time of the contrast agent.

[0003] The CT scan procedure for low-dose contrast-enhanced vascular monitoring begins with a tomographic scan. The surgeon delineates the region of interest (ROI) of the vessel to be monitored on the tomographic image. A small dose of contrast agent is injected into the patient initially, and the contrast agent concentration over time is monitored to determine the peak concentration time. Based on the peak concentration time from the first low-dose scan, the surgeon sets the timing for the three-phase scans, administering a second small dose of contrast agent to complete the subsequent three-phase scans. If the ROI delineated by the surgeon deviates from the original vessel, the changes in contrast agent concentration within the monitored vessel will be inaccurate, and the resulting curve will not accurately reflect the change in contrast agent concentration over time.

[0004] Chinese invention application No. 201910782808.2 discloses a localization method and system based on DSA images. The localization method includes the following steps: normalizing a two-dimensional DSA sequence image to be processed to obtain a preprocessed two-dimensional DSA sequence image; determining the optimal frame in the preprocessed two-dimensional DSA sequence image based on a first model; and segmenting the target region from the two-dimensional DSA images belonging to the optimal frame based on a second model to obtain the localized region of the target region in the two-dimensional DSA sequence image to be processed. This localization method, by locating the target region in the two-dimensional DSA sequence image to be processed, enables the direct display of the target region in the two-dimensional DSA image, reducing the time required for human observation, thought, and judgment. Summary of the Invention

[0005] The primary technical problem to be solved by this invention is to provide a method for vascular identification and monitoring based on static CT enhanced scanning.

[0006] Another technical problem to be solved by the present invention is to provide a blood vessel identification and monitoring system based on static CT enhanced scanning.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] According to a first aspect of the present invention, a method for blood vessel identification and monitoring based on static CT enhanced scanning is provided, comprising the following steps:

[0009] Obtain subtraction images of the entire area of ​​the patient's blood vessels to be monitored;

[0010] The subtraction images are processed to divide each subtraction image into multiple regions of a preset size, and the positions of the regions of each subtraction image correspond one-to-one.

[0011] For each region block in each of the subtraction images, calculate the mean and standard deviation, and then calculate the P-value of the region block;

[0012] Arrange the P values ​​of the same region block in each of the subtraction images in chronological order, and plot the P value variation curve of the region block;

[0013] Based on the P-value change curve of the region block, a contrast agent concentration change curve of the region block is plotted to determine the peak time of the contrast agent.

[0014] Preferably, acquiring the subtraction image of the entire region of the blood vessel to be monitored in the patient specifically includes:

[0015] While the patient is receiving a small dose of contrast agent, the entire area of ​​the blood vessel to be monitored in the patient is projected.

[0016] Within a preset time period, an image of the entire region is acquired at preset intervals.

[0017] Subtract the first image from the second image of the entire region to obtain the first silhouette image; subtract the first image from the third image of the entire region to obtain the second silhouette image; subtract the first image from the fourth image of the entire region to obtain the third silhouette image; and so on, until the last image is obtained.

[0018] The first silhouette image to the last silhouette image together constitute the subtraction image.

[0019] Preferably, the preset size of the region block is 16*16 pixels to match the diameter of the blood vessel to be monitored.

[0020] Preferably, the step of calculating the mean and standard deviation of each region block in each subtraction image and calculating the P-value of the region block specifically includes:

[0021] The mean μ of the region block is calculated based on the following formula;

[0022]

[0023] The standard deviation б of the region block is calculated based on the following formula;

[0024]

[0025] The P-value of the region block is calculated based on the mean μ and standard deviation б using the following formula;

[0026]

[0027] Where M and N represent the number of pixels in the image; H i,j Let (i, j) represent the value of pixel (i, j); μ0 represents the mean at time t0, and σ0 represents the standard deviation at time t0, i.e., the mean and standard deviation of the first image; μ n Indicates t n The mean at time n is the mean of the (n-1)th image.

[0028] Preferably, the step of plotting the contrast agent concentration change curve of the region based on the P-value change curve of the region to determine the peak time of the contrast agent specifically includes:

[0029] The average value change of the region is monitored based on the P-value change curve of the region to obtain the concentration change trend of the contrast agent;

[0030] A concentration change curve of the contrast agent was plotted based on the concentration change trend of the contrast agent;

[0031] Based on the concentration change curve of the contrast agent, the time corresponding to when the concentration of the contrast agent reaches its peak value is obtained, which is the peak time of the contrast agent.

[0032] Preferably, the blood vessel identification and monitoring method further includes:

[0033] Based on the concentration change curve of the contrast agent, the CT scan of the patient is stopped when the concentration of the contrast agent begins to decrease from its peak.

[0034] According to a second aspect of the present invention, a blood vessel identification and monitoring system based on static CT enhanced scanning is provided, comprising:

[0035] The image acquisition unit acquires subtraction images of the blood vessels to be monitored in the patient;

[0036] An image processing unit is connected to the image acquisition unit and performs image processing on the subtraction images to divide each subtraction image into multiple regions of a preset size, and the positions of the regions of each subtraction image correspond one-to-one.

[0037] A calculation unit, connected to the image processing unit, is used to calculate the P-value of the region blocks in the processed subtraction image;

[0038] An editing unit, connected to the calculation unit, is used to plot the contrast agent concentration change curve of the region block, thereby determining the peak time of the contrast agent.

[0039] Compared with the prior art, the present invention has the following technical effects:

[0040] 1. It can accurately determine the location and trend of the blood vessel to be monitored, avoiding the situation where the ROI area circled by the doctor deviates from the original blood vessel, resulting in inaccurate CT values ​​of the monitored blood vessel, thus failing to reflect the CT value change curve over time and missing the peak time of the contrast agent.

[0041] 2. By observing the contrast agent concentration change curve, if a downward trend is observed, the CT scan can be terminated at any time to avoid the patient receiving unnecessary X-rays and reduce the radiation dose received by the patient. Attached Figure Description

[0042] Figure 1 This is a flowchart of a blood vessel identification and monitoring method based on static CT enhanced scanning provided in the first embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the structure for dividing a subtraction image into regions in the first embodiment of the present invention;

[0044] Figure 3 This is a structural diagram of a blood vessel identification and monitoring system based on static CT enhanced scanning provided in the second embodiment of the present invention;

[0045] Figure 4 This is a structural diagram of a blood vessel identification and monitoring system based on static CT enhanced scanning provided in the third embodiment of the present invention. Detailed Implementation

[0046] The technical content of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0047] <First Embodiment>

[0048] First, it should be noted that the vascular identification and monitoring method provided in this embodiment of the invention is mainly for phase II scanning, that is, after the first injection of a small dose of contrast agent into the patient, accurately determining the peak time of the contrast agent, so as to avoid missing the optimal scanning opportunity during phase III scanning.

[0049] like Figure 1 As shown, the first embodiment of the present invention provides a method for blood vessel identification and monitoring based on static CT enhanced scanning, specifically including steps S1 to S5:

[0050] S1: Image acquisition.

[0051] Specifically, this includes steps S11 to S13:

[0052] S11: Project the entire area of ​​the blood vessel to be monitored in the patient while the patient is receiving a small dose of contrast agent.

[0053] It is understood that in this embodiment, the monitored area is not the ROI region circled by the doctor on the tomographic image, but the entire area of ​​the monitored blood vessel, so as to ensure the comprehensiveness of the monitoring results.

[0054] S12: Within a preset time period, acquire an image of the entire region at preset intervals.

[0055] Specifically, in this embodiment, an image of the entire region is acquired every 250ms, for a total of 51 images of the entire region.

[0056] S13: Obtain the subtraction image.

[0057] Specifically, the first silhouette image is obtained by subtracting the first full-area image from the second full-area image; the second silhouette image is obtained by subtracting the first full-area image from the third full-area image; and so on, until the last subtracted silhouette image is obtained. Thus, 50 subtracted silhouette images can be obtained using 51 full-area images, and these 50 subtracted silhouette images together constitute a set of subtracted silhouette images.

[0058] S2: Image processing.

[0059] Specifically, in this embodiment, after obtaining 50 subtraction images in step S1, each subtraction image needs to be processed to divide each subtraction image into multiple regions of a preset size, so that the positions of the regions of the 50 subtraction images correspond one-to-one.

[0060] Reference Figure 2 As shown, in this embodiment, each subtraction image includes m*n (m and n are both positive integers, the same below) pixel blocks. By dividing the m*n pixel blocks, multiple region blocks 101 are formed (i.e. Figure 2 (As shown in the black area in the image), each region block 101 comprises a*a pixel blocks. Since each subtraction image is the same size, the number of region blocks 101 is also the same. Therefore, the region block positions of the 50 subtraction images will correspond one-to-one.

[0061] In one embodiment of the present invention, preferably, the region block 101 comprises 16*16 pixel blocks, wherein each pixel block is approximately 0.265 mm in size. The size of the 16*16 pixel blocks is comparable to the thickness of the blood vessel to be monitored, thereby enabling it to match the blood vessel and improve the monitoring effect. It is understood that in other embodiments, the size of the region block 101 can be adaptively adjusted as needed.

[0062] S3: Calculate the P-value of each region in each subtraction image.

[0063] In this embodiment, after image processing is performed on each subtraction image to divide it into multiple regions 101, a P-value needs to be calculated for each region 101. If a contrast agent is present in a region 101 of the subtraction image, the P-value of that region 101 will be significant, thus accurately determining that region 101 is the location of the contrast agent; conversely, if no contrast agent is present in a region 101 of the subtraction image, the P-value of that region 101 will not be significant.

[0064] Understandably, in this embodiment, each subtraction image requires the calculation of multiple region blocks 101's P values. Whether the P value of any region block 101 is significant needs to be determined by comparing it with the P value of the region block at the same location in the previous subtraction image. Thus, the P value at the same location will have 50 values ​​changing over time, which can be plotted as a curve. The location of the contrast agent shows a significant change.

[0065] The calculation of the P value of a region block 101 specifically includes steps S31 to S33:

[0066] S31: Calculate the mean μ of region block 101 based on formula (1);

[0067]

[0068] S32: Calculate the standard deviation б of region block 101 based on formula (2);

[0069]

[0070] S33: Calculate the P-value of region block 101 based on the mean μ and standard deviation б using formula (3);

[0071]

[0072] Where M and N represent the number of pixels in the image; H i,j Let (i, j) represent the value of pixel (i, j); μ0 represents the mean at time t0, and σ0 represents the standard deviation at time t0, i.e., the mean and standard deviation of the first image; μ n Indicates t nThe mean at time n is the mean of the (n-1)th image.

[0073] S4: Plot the curve of P-value variation in region 101.

[0074] Specifically, after calculating the P values ​​of multiple regions in each subtraction image based on step S3, the P values ​​of the same region in each subtraction image are arranged in chronological order to determine which regions 101 have significant P values ​​and which regions 101 have insignificant P values, thereby obtaining the P value change process and plotting the P value change curve of the region.

[0075] S5: Determine the peak time of the contrast agent.

[0076] Specifically, this includes steps S51 to S53:

[0077] S51: Monitor the mean change of the region block based on the P-value change curve of the region block to obtain the concentration change trend of the contrast agent;

[0078] S52: Plot the concentration change curve of the contrast agent based on the concentration change trend of the contrast agent;

[0079] S53: Based on the concentration change curve of the contrast agent, obtain the time corresponding to when the concentration of the contrast agent reaches its peak, which is the peak time of the contrast agent.

[0080] Therefore, the vascular identification and monitoring method provided by this invention can accurately determine the location and trend of the monitored blood vessel, avoiding situations where the ROI region circled by the doctor deviates from the original blood vessel, resulting in inaccurate CT values ​​and failing to accurately reflect the CT value change curve over time, thus missing the peak time of the contrast agent. Furthermore, in this embodiment, the doctor can observe the contrast agent concentration change curve; if a decreasing trend is observed, the CT scan can be terminated at any time, avoiding unnecessary X-ray exposure for the patient and reducing the radiation dose received.

[0081] <Second Embodiment>

[0082] like Figure 3 As shown, based on the first embodiment described above, the second embodiment of the present invention also provides a blood vessel identification and monitoring system based on static CT enhanced scanning, which includes at least an image acquisition unit 10, an image processing unit 20, a calculation unit 30, and an editing unit 40.

[0083] Specifically, the image acquisition unit 10 acquires a set of subtraction images of the patient's blood vessels to be monitored. The image processing unit 20 is connected to the image acquisition unit 10 and performs image processing on the set of subtraction images to divide each subtraction image into multiple regions of preset size, with the positions of the regions of each subtraction image corresponding one-to-one. The calculation unit 30 is connected to the image processing unit 20 to calculate the P-value of the processed subtraction image regions. The editing unit 40 is connected to the calculation unit 30 to plot the contrast agent concentration change curve of the regions, thereby determining the peak time of the contrast agent.

[0084] <Third Embodiment>

[0085] like Figure 4 As shown, based on the first embodiment described above, the third embodiment of the present invention further provides a blood vessel identification and monitoring system based on static CT enhanced scanning, including one or more processors 21 and a memory 22. The memory 22 is coupled to the processors 21 and is used to store one or more programs. When the one or more programs are executed by the one or more processors 21, the one or more processors 21 implement the blood vessel identification and monitoring method as described in the first embodiment.

[0086] The processor 21 controls the overall operation of the monitoring system to complete all or part of the steps of the aforementioned blood vessel identification monitoring method. The processor 21 can be a central processing unit (CPU), graphics processing unit (GPU), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), digital signal processing (DSP) chip, etc. The memory 22 stores various types of data to support the operation of the monitoring system. This data may include, for example, instructions for any application or method used to operate on the monitoring system, as well as application-related data. The memory 22 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, etc.

[0087] In one exemplary embodiment, the monitoring system may be implemented by a computer chip or physical entity, or by a product with certain functions, to perform the aforementioned blood vessel identification and monitoring method and achieve the same technical effect as the method described above. A typical embodiment is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interface device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0088] In another exemplary embodiment, the present invention also provides a computer-readable storage medium including program instructions that, when executed by a processor, implement the steps of the blood vessel identification and monitoring method in any of the above embodiments. For example, the computer-readable storage medium may be the memory including the program instructions, which can be executed by the processor of the monitoring system to complete the blood vessel identification and monitoring method described above and achieve the same technical effects as the method described above.

[0089] The above provides a detailed description of the vascular identification and monitoring method and system based on static CT enhanced scanning provided by this invention. Any obvious modifications made by those skilled in the art without departing from the essence of this invention will constitute an infringement of the patent rights of this invention and will incur corresponding legal liability.

Claims

1. A method for vascular identification and monitoring based on static CT enhanced scanning, characterized in that... Includes the following steps: Obtain subtraction images of the entire area of ​​the patient's blood vessels to be monitored; The subtraction images are processed to divide each subtraction image into multiple regions of a preset size. Furthermore, the positions of the regions in each of the subtraction images correspond one-to-one; For each region block in each of the subtraction images, the mean and standard deviation are calculated, and the p-value of the region block is calculated; specifically including: The mean μ of the region block is calculated based on the following formula; The standard deviation σ of the region block is calculated based on the following formula; The P-value of the region block is calculated based on the mean μ and standard deviation б using the following formula; wherein M, N represent the number of pixels of the image; H i,j represents the value of the (i, j) pixel; μ0represents the mean value at t0, σ0represents the standard deviation at t0, i.e. the mean value and the standard deviation of the first image; μ n represents the mean value at t n , i.e. the mean value of the (n-1)th image; Arrange the P values ​​of the same region block in each of the subtraction images in chronological order, and plot the P value variation curve of the region block; Based on the P-value change curve of the region block, a contrast agent concentration change curve of the region block is plotted to determine the peak time of the contrast agent.

2. The blood vessel identification and monitoring method as described in claim 1, characterized in that... The acquisition of subtraction images of the entire area of ​​the patient's blood vessels to be monitored specifically includes: While the patient is receiving a small dose of contrast agent, the entire area of ​​the blood vessel to be monitored in the patient is projected. Within a preset time period, an image of the entire region is acquired at preset intervals. Subtract the first image of the entire region from the second image of the entire region to obtain the first subtracted image; Subtract the first image of the entire region from the third image of the entire region to obtain the second subtractive image; and so on, to obtain the last subtractive image. The first subtraction image to the last subtraction image together constitute a set of subtraction images.

3. The blood vessel identification and monitoring method as described in claim 1, characterized in that: The preset size of the region block is Pixels, to match the diameter of the blood vessel to be monitored.

4. The blood vessel identification and monitoring method as described in claim 1, characterized in that... The step of plotting the contrast agent concentration change curve of the region based on the P-value change curve of the region to determine the peak time of the contrast agent specifically includes: The average value change of the region is monitored based on the P-value change curve of the region to obtain the concentration change trend of the contrast agent; A concentration change curve of the contrast agent was plotted based on the concentration change trend of the contrast agent; Based on the concentration change curve of the contrast agent, the time corresponding to when the concentration of the contrast agent reaches its peak value is obtained, which is the peak time of the contrast agent.

5. The blood vessel identification and monitoring method as described in claim 1, characterized in that... Also includes: Based on the concentration change curve of the contrast agent, the CT scan of the patient is stopped when the concentration of the contrast agent begins to decrease from its peak.

6. A blood vessel identification and monitoring system based on static CT enhanced scanning, characterized in that... include: The image acquisition unit acquires subtraction images of the blood vessels to be monitored in the patient; An image processing unit is connected to the image acquisition unit and performs image processing on the subtraction images to divide each subtraction image into multiple regions of a preset size, and the positions of the regions of each subtraction image correspond one-to-one. A calculation unit, connected to the image processing unit, calculates the P-value for regions of the processed subtraction image; specifically, it includes: The mean μ of the region block is calculated based on the following formula; The standard deviation σ of the region block is calculated based on the following formula; The P-value of the region block is calculated based on the mean μ and standard deviation б using the following formula; wherein M, N represent the number of pixels of the image; H i,j represents the value of the (i, j) pixel; μ0represents the mean value at t0, σ0represents the standard deviation at t0, i.e. the mean value and the standard deviation of the first image; μ n represents the mean value at t n , i.e. the mean value of the (n-1)th image; An editing unit, connected to the calculation unit, is used to plot the contrast agent concentration change curve of the region block, thereby determining the peak time of the contrast agent; wherein, the P values ​​of the same region block in each subtraction image are arranged in chronological order, and the P value change curve of the region block is plotted; based on the P value change curve of the region block, the contrast agent concentration change curve of the region block is plotted to determine the peak time of the contrast agent.

7. The blood vessel identification and monitoring system as described in claim 6, characterized in that... The acquisition of the subtraction image of the patient's blood vessel to be monitored specifically includes: While the patient is receiving a small dose of contrast agent, the entire area of ​​the blood vessel to be monitored in the patient is projected. Within a preset time period, an image of the entire region is acquired at preset intervals. Subtract the first image of the entire region from the second image of the entire region to obtain the first subtracted image; Subtract the first image of the entire region from the third image of the entire region to obtain the second subtracted image; and so on, to obtain the last subtracted image. The first subtraction image to the last subtraction image together constitute a set of subtraction images.

8. The blood vessel identification and monitoring system as described in claim 7, characterized in that... The step of plotting the contrast agent concentration change curve of the region block to determine the peak time of the contrast agent specifically includes: The average value change of the region is monitored based on the P-value change curve of the region to obtain the concentration change trend of the contrast agent; A concentration change curve of the contrast agent was plotted based on the concentration change trend of the contrast agent; Based on the concentration change curve of the contrast agent, the time corresponding to when the concentration of the contrast agent reaches its peak value is obtained, which is the peak time of the contrast agent.

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