WO2021208140A1 - Procédé et système de traitement d'images vasculaires, dispositif informatique, et support de stockage - Google Patents

Procédé et système de traitement d'images vasculaires, dispositif informatique, et support de stockage Download PDF

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WO2021208140A1
WO2021208140A1 PCT/CN2020/087304 CN2020087304W WO2021208140A1 WO 2021208140 A1 WO2021208140 A1 WO 2021208140A1 CN 2020087304 W CN2020087304 W CN 2020087304W WO 2021208140 A1 WO2021208140 A1 WO 2021208140A1
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blood vessel
interest
parameters
vessel segment
information
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PCT/CN2020/087304
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English (en)
Chinese (zh)
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涂圣贤
李莹光
余炜
吴鹏
赖琦彘
陈树湛
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博动医学影像科技（上海）有限公司
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Priority to JP2022551536A priority Critical patent/JP7465361B2/ja
Publication of WO2021208140A1 publication Critical patent/WO2021208140A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/344—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G06T2207/30104—Vascular flow; Blood flow; Perfusion

Definitions

  • the present invention relates to the medical field, in particular to a method, system, computing device and storage medium for processing blood vessel images.
  • Vascular stenosis will affect the blood supply of the myocardium. Coronary angiography can show the severity of coronary stenosis, but it cannot reflect the functional changes of the blood vessels.
  • Vascular pressure difference refers to the pressure difference between the proximal end and the distal end of the blood vessel segment of interest, which can effectively reflect the blood supply function of the blood vessel.
  • the geometric model of the coronary system can be obtained by three-dimensional or two-dimensional quantitative coronary angiography. Then, the reconstructed geometric model of the coronary system is analyzed by computer fluid mechanics. Solving complex fluid mechanics equations requires a lot of calculations. There are also methods to treat the length and stenosis rate of coronary artery stenosis as fixed values, which will reduce the accuracy of the calculation results.
  • CN107115108A discloses a method for quickly calculating blood vessel pressure difference based on blood vessel images.
  • the prior art calculates the reference lumen based on the lumen profile parameters in the blood vessel, which results in the presence of certain features in the blood vessel, for example, when there is plaque between the lumen and the media.
  • the lumen has undergone a large deformation, and the obtained reference lumen deviates greatly from the ideal reference lumen, especially when there are plaques in the entire blood vessel, it is impossible to pass through the lumen.
  • Obtaining an accurate reference lumen further leads to inaccurate calculation of the vascular pressure difference.
  • the media contour parameters can be used to obtain an accurate reference lumen, such as the reduction of the media contour. After removing the thickness of the normal intima, it is the corresponding contour of the reference lumen). Therefore, the new technical solution proposed by the present invention overcomes the technical problem of inaccurate calculation of the vascular pressure difference in the prior art.
  • the purpose of the present invention is to provide a method for processing blood vessel images to solve the problem of inaccurate calculation of blood vessel pressure difference in the prior art.
  • an embodiment of the present invention discloses a method for processing blood vessel images, including the following steps: acquiring image data of the blood vessel segment of interest; acquiring the blood flow parameters of the blood vessel segment of interest; detecting and analyzing the blood vessel of interest Segment image data to obtain reference information, which includes the lumen contour parameters and media contour parameters of the blood vessel segment of interest; obtain reference lumen information according to the media contour parameters; calculate according to blood flow parameters, reference information and reference lumen information Obtain the blood flow reserve score value of the blood vessel segment of interest.
  • the step of obtaining reference lumen information according to the media contour parameter includes: obtaining the proximal normal frame and the distal normal frame according to the media contour parameter; calculating the reference lumen according to the proximal normal frame and the distal normal frame information.
  • the step of obtaining reference lumen information according to media contour parameters includes: obtaining the normal intima thickness of the blood vessel segment of interest; obtaining reference lumen information according to the media contour parameters and the normal intima thickness.
  • the reference information further includes the side branch vessel segment parameters of the blood vessel segment of interest.
  • the side branch vessel segment parameters are calculated from the lumen contour parameters.
  • the step of obtaining reference lumen information according to the media contour parameters includes: The contour parameter obtains the near-end normal frame and the far-end normal frame; according to the side branch blood vessel segment parameters, the near-end normal frame and the far-end normal frame, the reference lumen information is calculated.
  • the reference information also includes side branch vessel segment parameters of the blood vessel segment of interest.
  • the side branch vessel segment parameters are calculated from the lumen contour parameters.
  • the step of obtaining reference lumen information according to the media contour parameters includes: according to the side branch The blood vessel segment parameter divides the blood vessel segment of interest into multiple sub-segments; the normal frame in each sub-segment is obtained according to the media contour parameter; the reference lumen information is obtained according to the normal frame in each sub-segment.
  • the blood vessel image processing method further includes the following steps: calculating the calibration value at each side branch according to the reference lumen information and the side branch blood vessel segment parameters; displaying the chart of the blood vessel segment of interest according to the side branch blood vessel segment parameters Display each side support at the corresponding position of the chart; and/or display the cross-sectional profile of the side support and display the calibration value.
  • the method for processing blood vessel images further includes the following steps: obtaining the blood vessel position of the blood vessel segment of interest; and correcting the blood flow reserve score value according to the blood vessel position.
  • the reference information further includes the side branch blood vessel segment parameters of the blood vessel segment of interest.
  • the side branch blood vessel segment parameters are calculated from the lumen contour parameters.
  • the blood vessel image processing method further includes the following steps: obtaining the blood vessel of interest according to the position of the blood vessel The blood vessel type of the segment; when the blood vessel type is a bifurcation model: obtain the bifurcation node information of the blood vessel segment of interest according to the parameters of the side branch vessel segment; display the longitudinal section window of the blood vessel segment of interest; use different colors according to the bifurcation node information
  • the main branch and branch of the vessel segment of interest are identified in the longitudinal section window.
  • the step of acquiring image data of the blood vessel segment of interest includes: acquiring image data of the target blood vessel and corresponding acquisition parameters, the acquisition parameters including layer thickness and pixel size; reconstructing and displaying based on the image data and acquisition parameters of the target blood vessel The image of the target blood vessel; to obtain the image data of the selected blood vessel segment of interest in the image.
  • the step of reconstructing and displaying the image of the target blood vessel according to the image data and acquisition parameters of the target blood vessel includes: re-sampling and re-ordering the image data of the target blood vessel; according to the re-ordered image data and acquisition of the target blood vessel The parameters reconstruct and display the image of the target blood vessel.
  • the step of reconstructing and displaying the image of the target blood vessel according to the image data and acquisition parameters of the target blood vessel includes: reconstructing the image of the target blood vessel according to the image data and acquisition parameters of the target blood vessel; and registering the image of the target blood vessel to Correct the error in the acquisition process of the image data of the target blood vessel; display the registered image.
  • the reference information further includes stent information, a method for processing blood vessel images, and further includes the following steps: acquiring stent parameters of the blood vessel segment of interest; detecting and rebuilding the stent according to the stent parameters, evaluating the stent to obtain the stent information,
  • the stent information includes stent position and stent contour information; the longitudinal section window showing the blood vessel segment of interest; the stent is identified by pseudo-color bars in the longitudinal section window according to the stent information.
  • the blood vessel image processing method further includes the following steps: quantify the first characteristic blood vessel segment whose stenosis rate is greater than the threshold value according to the reference lumen information and the reference information; display a chart of the blood vessel segment of interest; and mark the chart where the stenosis rate is located Set the first characteristic blood vessel segment of the interval.
  • the reference information further includes the second feature information
  • the blood vessel image processing method further includes the following steps: reconstructing and displaying the blood vessel segment of interest and the second feature based on the reference information.
  • the blood vessel image processing method further includes the following steps: displaying the longitudinal section contour and/or the cross-sectional contour of the blood vessel segment of interest according to the reference information; adjusting the longitudinal section contour and/or the cross-sectional contour; according to the adjusted longitudinal
  • the section profile and/or the cross-sectional profile update the reference information, and/or the reference lumen information, and the blood flow reserve score value.
  • the blood vessel image processing method further includes the following steps: displaying the blood flow reserve score value on the chart of the blood vessel segment of interest in a pseudo-color form; and/or displaying the chart of the blood vessel segment of interest and superimposing the simulated Retracement curve; and/or three-dimensional reconstruction of the vessel segment of interest, and display the vessel segment of interest after the three-dimensional reconstruction.
  • the embodiment of the present invention also discloses a blood vessel image processing system, including: an acquisition module for acquiring image data of the blood vessel segment of interest and blood flow parameters of the blood vessel segment of interest; an analysis module for detecting and analyzing the blood vessel of interest Segment image data to obtain reference information.
  • the reference information includes the lumen contour parameters and media contour parameters of the blood vessel segment of interest;
  • the calculation module includes a first calculation unit and a second calculation unit. The parameter obtains the reference lumen information, and the second calculation unit is used to calculate the blood flow reserve score value of the blood vessel segment of interest according to the blood flow parameter, the reference information and the reference lumen information.
  • the calculation of the vascular pressure difference can be more accurate.
  • the first calculation unit includes a normal frame extraction unit and a reference lumen calculation unit.
  • the normal frame extraction unit is used to obtain the proximal normal frame and the distal normal frame according to the media contour parameters
  • the reference lumen calculation unit is used to obtain The reference lumen information is calculated from the near-end normal frame and the far-end normal frame.
  • the obtaining module is also used to obtain the normal intima thickness of the blood vessel segment of interest, and the first calculation unit obtains the reference lumen information according to the media contour parameter and the normal intima thickness.
  • the reference information further includes the side branch blood vessel segment parameters of the blood vessel segment of interest, the side branch blood vessel segment parameters are calculated by the analysis module according to the lumen contour parameters, and the first calculation unit includes a normal frame extraction unit and a reference lumen calculation unit ,
  • the normal frame extraction unit is used to obtain the proximal normal frame and the distal normal frame according to the media contour parameters
  • the reference lumen calculation unit is used to calculate the reference tube according to the side branch vessel segment parameters, the proximal normal frame and the distal normal frame.
  • Cavity information is used to calculate the reference tube according to the side branch vessel segment parameters, the proximal normal frame and the distal normal frame.
  • the reference information further includes the side branch blood vessel segment parameters of the blood vessel segment of interest, the side branch blood vessel segment parameters are calculated by the analysis module according to the lumen contour parameters, and the first calculation unit includes a normal frame extraction unit and a reference lumen calculation unit ,
  • the normal frame extraction unit is used to divide the blood vessel segment of interest into multiple sub-segments according to the side branch blood vessel segment parameters, and obtain the normal frame in each sub-segment according to the media contour parameters.
  • the reference lumen calculation unit is used to calculate according to each sub-segment The normal frame inside gets the reference lumen information.
  • the blood vessel image processing system further includes a first display module
  • the calculation module further includes a third calculation unit.
  • the third calculation unit is used to calculate the value of each side branch according to the reference lumen information and the side branch blood vessel segment parameters.
  • Calibration value the first display module is used to display the chart of the blood vessel segment of interest, and each side branch is displayed in the corresponding position of the chart according to the side branch blood vessel segment parameters, and/or the first display module is also used to display the side branch Cross-section profile and display calibration value.
  • the blood vessel image processing system further includes a correction module, the acquisition module is also used to acquire the blood vessel position of the blood vessel segment of interest, and the correction module is used to correct the blood flow reserve score value of the blood vessel segment of interest according to the blood vessel position.
  • the blood vessel image processing system further includes a second display module
  • the reference information also includes side branch blood vessel segment parameters of the blood vessel segment of interest
  • the side branch blood vessel segment parameters are calculated from the lumen contour parameters
  • the acquisition module is also used for Obtain the blood vessel type of the blood vessel segment of interest according to the position of the blood vessel.
  • the analysis module obtains the bifurcation node information of the blood vessel segment of interest according to the side branch vessel segment parameters
  • the second display module displays the blood vessel segment of interest.
  • the longitudinal section window, and according to the bifurcation node information, use different colors to identify the main branch and branch of the blood vessel segment of interest in the longitudinal section window.
  • the acquisition module includes a first acquisition unit, a second acquisition unit, a reconstruction calculation unit, and a display unit.
  • the first acquisition unit is used to acquire image data of the target blood vessel and corresponding acquisition parameters.
  • the acquisition parameters include layer thickness and pixel size.
  • the reconstruction calculation unit is used to perform reconstruction calculation according to the image data and acquisition parameters of the target blood vessel
  • the display unit is used to display the image of the reconstructed target blood vessel
  • the second acquisition unit is used to obtain the selected blood vessel segment of interest in the image Image data.
  • the acquisition module further includes a re-sampling unit, the re-sampling unit is used to re-sample and re-order the image data of the target blood vessel acquired by the first acquisition unit, and the reconstruction calculation unit is used to re-order the image data of the target blood vessel Data and acquisition parameters are reconstructed and calculated.
  • the re-sampling unit is used to re-sample and re-order the image data of the target blood vessel acquired by the first acquisition unit
  • the reconstruction calculation unit is used to re-order the image data of the target blood vessel Data and acquisition parameters are reconstructed and calculated.
  • the acquisition module further includes a registration unit, the registration unit is used to register the image of the target blood vessel to correct errors in the acquisition process of the image data of the target blood vessel, and the display unit is used to display the registered image .
  • the blood vessel image processing system further includes a third display module
  • the reference information also includes stent information
  • the acquisition module is also used to acquire stent parameters of the blood vessel segment of interest
  • the analysis module is also used to detect and reconstruct the stent according to the stent parameters
  • the stent is evaluated and the stent information is obtained.
  • the stent information includes stent position and stent contour information.
  • the third display module is used to display the longitudinal section window of the blood vessel segment of interest, and according to the stent information, the longitudinal section window is displayed in false color Bar identification bracket.
  • the blood vessel image processing system further includes a fourth display module
  • the calculation module further includes a fourth calculation unit configured to quantify the first feature that the stenosis rate is greater than the threshold according to the reference lumen information and the reference information
  • the blood vessel segment, the fourth display module is used to display the chart of the blood vessel segment of interest, and mark the first characteristic blood vessel segment whose stenosis rate is in the set interval in the chart.
  • the blood vessel image processing system further includes a fifth display module, when the blood vessel segment of interest contains the second feature, the reference information further includes second feature information, and the analysis module is further used to reconstruct the blood vessel segment of interest based on the reference information And the second feature, the fifth display module is used to display the blood vessel segment of interest and the second feature.
  • the blood vessel image processing system further includes an adjustment module
  • the adjustment module includes a display unit, an adjustment unit and an update unit
  • the display unit is used to display the longitudinal section contour and/or the cross section contour of the blood vessel segment of interest according to the reference information
  • the adjustment unit is used to adjust the longitudinal section profile and/or cross-sectional profile
  • the update unit is used to update the reference information, and/or reference lumen information, and the blood flow reserve score value according to the adjusted longitudinal section profile and/or cross-sectional profile.
  • the blood vessel image processing system further includes a sixth display module for displaying the blood flow reserve score value on the chart of the blood vessel segment of interest in a pseudo-color form, and/or the displayed interest
  • the chart of the blood vessel segment is superimposed to display the simulated retracement curve, and/or the blood vessel segment of interest is reconstructed in three dimensions, and the blood vessel segment of interest after the three-dimensional reconstruction is displayed.
  • the embodiment of the present invention also discloses a computing device, including: a processor, which is suitable for implementing various instructions; a memory, which is suitable for storing multiple instructions, and the instructions are suitable for being loaded by the processor and executing any of the aforementioned blood vessel images. Approach.
  • the calculation of the blood vessel pressure difference can be more accurate.
  • the embodiment of the present invention also discloses a storage medium, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executed by any of the aforementioned blood vessel image processing methods.
  • FIG. 1 shows a flowchart of a method for processing a blood vessel image according to an embodiment of the present invention
  • FIG. 2 shows a partial flowchart of a method for processing a blood vessel image according to another embodiment of the present invention
  • Fig. 3 shows a flowchart of a method for processing a blood vessel image according to another embodiment of the present invention
  • FIG. 4 shows a partial flowchart of a method for processing a blood vessel image according to another embodiment of the present invention
  • Fig. 5 shows a schematic block diagram of a blood vessel image processing system according to an embodiment of the present invention.
  • Fig. 6 shows a schematic diagram of a blood vessel segment of interest in an embodiment of the present invention
  • Fig. 7 shows a schematic diagram of a blood vessel segment of interest in another embodiment of the present invention.
  • Figure 8 shows a three-dimensional schematic diagram of a blood vessel segment of interest in an embodiment of the present invention
  • Fig. 9 shows a schematic diagram of a blood vessel segment of interest in an embodiment of the present invention.
  • Figure 10 shows a schematic diagram of plaques in an embodiment of the present invention
  • Figure 11 shows a schematic cross-sectional view of a side support in an embodiment of the present invention
  • Figure 12 shows a schematic diagram of a cross-sectional profile of a blood vessel segment of interest in an embodiment of the present invention
  • Figure 13 shows a schematic diagram of a bracket in an embodiment of the present invention
  • Figure 14 shows a schematic diagram of a stent in another embodiment of the present invention.
  • Fig. 15 shows a schematic diagram of a blood vessel segment of interest in another embodiment of the present invention.
  • the embodiment of the present invention discloses a method for processing blood vessel images, including the following steps: acquiring image data of a blood vessel segment of interest; acquiring blood flow parameters of the blood vessel segment of interest; detecting and analyzing the blood vessel of interest Segment image data to obtain reference information, which includes the lumen contour parameters and media contour parameters of the blood vessel segment of interest; obtain reference lumen information according to the media contour parameters; calculate according to blood flow parameters, reference information and reference lumen information Obtain the Fractional Flow Reserve (FFR) value of the blood vessel segment of interest.
  • FFR Fractional Flow Reserve
  • the image data may be directly acquired, or image data corresponding to the blood vessel segment of interest selected from the image data of a blood vessel segment, which is not limited in this embodiment.
  • the source of the image data can be directly imported related files, it can also be obtained from the real-time configuration connection of other resource libraries, or it can be obtained from the stored image database after searching and locking based on the user’s name and other information. This is not limited.
  • the image data is in DICOM (Digital Imaging and Communication in Medicine) format.
  • DICOM covers almost all information exchange protocols such as the collection, archiving, communication, display and query of medical digital images; it is based on an open interconnected architecture and object-oriented
  • the method defines a set of objects containing various types of medical diagnostic images and related analysis, reports and other information; defines the service classes and command sets used for information transmission and exchange, as well as the standard response of messages; details The technology to identify various information objects; provide service support applied to the network environment (OSI or TCP/IP); structured definition of the manufacturer’s compatibility statement (Conformance Statement).
  • Adopting the DICOM format can greatly simplify the realization of medical imaging information exchange, and facilitate the association and synergy with other medical application systems such as HIS and RIS.
  • the image data is an image of 200-4000 frames.
  • the blood flow parameters may include the average maximum blood flow velocity. It is understandable that the average maximum blood flow velocity of the blood vessel segment of interest is required to calculate the FFR value of the blood vessel segment of interest, but there are many forms of the source and acquisition sequence of blood flow parameters, either in the image data collection What is measured in the process may also be obtained by the user in the previous measurement and stored in the database, and obtained directly this time, or it may be obtained by obtaining the input fixed value. It is understandable that the acquisition of blood flow parameters can be carried out before the acquisition of the image data, or simultaneously with the acquisition of the image data, or after the acquisition of the image data, as long as it is before the FFR is calculated. This is not limited.
  • Algorithms can be used to segment the contours of the lumen and media boundary of the longitudinal section image at multiple cut angles of the blood vessel segment of interest, and combine the longitudinal section contours to segment the contours of the lumen and media boundary in each cross-sectional image , And quantify the area and diameter of the lumen and media boundary in the cross-sectional image in each frame, so as to obtain the corresponding reference information, that is, the lumen profile parameters and media profile parameters of the blood vessel segment of interest.
  • the profile parameters can be The area and diameter corresponding to the contour.
  • Obtaining the reference lumen through the media profile parameters can make it possible even when there are certain features in the blood vessel segment of interest, such as plaques, so that the lumen profile is significantly squeezed inward, because the media profile is affected by these features.
  • the influence is small, so the reference lumen information obtained by the media profile parameters is closer to the ideal reference lumen and more accurate, which makes the calculation result of FFR more accurate.
  • the reference lumen information may be the boundary contour, area, diameter, etc. of the reference lumen.
  • Fractional Flow Reserve (FFR) value of the blood vessel segment of interest based on the blood flow parameters, reference information and reference lumen information
  • FFR Fractional Flow Reserve
  • the reference information only includes the media contour information and the lumen contour information
  • the blood flow parameters, the lumen contour information and the reference lumen information can be used to calculate the FFR.
  • the reference information also includes other information, such as stent information and plaque information
  • the FFR calculation can be corrected accordingly based on these information.
  • the calculation of FFR can be corrected according to the position of the blood vessel.
  • the media contour parameters are mainly used for the calculation of reference lumen information, and the reference lumen information directly participates in the calculation of FFR.
  • the blood vessel image processing method disclosed in this embodiment can achieve a more accurate calculation result, which facilitates the subsequent application of the FFR value by the user.
  • the step of obtaining reference lumen information according to media contour parameters includes: obtaining proximal normal frames and distal normal frames according to media contour parameters. End normal frame; the reference lumen information is calculated according to the near-end normal frame and the far-end normal frame.
  • the blood vessel of interest can be divided into three segments: the proximal blood vessel segment, the middle blood vessel segment, and the distal blood vessel segment.
  • the specific segmentation method can be divided into three equal parts according to the number of frames.
  • the frames are respectively referred to as the near-end normal frame (PN) and the far-end normal frame (DN).
  • PN near-end normal frame
  • DN far-end normal frame
  • Membrane profile parameters replace the lumen profile parameters to determine PN and DN, and the PN and DN obtained thereby are more reliable.
  • the degree of smoothness can be represented by the mean square error of the area of the area enclosed by the media contour.
  • the diameter or area of the media at PN and DN can be used as the diameter or area of the reference lumen to calculate the information of the reference lumen .
  • linear interpolation can be performed based on PN and DN to obtain the reference lumen size at each position of the blood vessel segment of interest.
  • the media contour parameters replace the lumen contour parameters to determine the proximal normal frame and the distal normal frame, and then calculate the corresponding reference lumen, so that the obtained reference lumen information is more accurate, and plaques are reduced.
  • the error caused by PN and DN is determined by the parameters of the lumen profile.
  • the step of obtaining reference lumen information according to media contour parameters includes: obtaining information about the blood vessel segment of interest. Normal intima thickness; reference lumen information is obtained according to media contour parameters and normal intima thickness. It can be understood that the thickness of the intima between the lumen and the media is basically fixed when the blood vessel segment of interest is in a general state, that is, when it does not contain characteristic information such as plaque. This value is called in this embodiment It is the normal intima thickness. Therefore, when using the media contour parameters to calculate the reference lumen information, according to the normal intima thickness, the reference lumen information in the ideal state can be accurately obtained.
  • the source and acquisition of the normal intima thickness can be measured at the same time during the acquisition of image data, or acquired together with the image data. It can also be directly obtained from the database of the individual corresponding to the blood vessel segment of interest, or it can be determined based on the input value. This embodiment is not limited, and the normal intima thickness can be obtained as long as the reference lumen information is calculated. Just before.
  • the diameter of the reference lumen can be obtained by subtracting twice the normal intima thickness from the diameter of the media, and the area of the reference lumen can be calculated. It is also possible to convert the area of the media to an equivalent diameter and subtract twice the normal intima thickness to obtain the diameter of the reference lumen, and the area of the reference lumen can be calculated, which is not limited in this embodiment.
  • the corresponding reference lumen is calculated based on the media contour parameters and the normal intima thickness, so that the obtained reference lumen information is more accurate, the influence of the intima thickness is eliminated, and the corresponding FFR value is also more accurate.
  • the reference information also includes the side branch blood vessel segment parameters of the blood vessel segment of interest.
  • the side branch blood vessel segment parameters are determined by The lumen contour parameters are calculated, and the steps of obtaining reference lumen information according to the media contour parameters include: obtaining the proximal normal frame and the distal normal frame according to the media contour parameter; according to the side branch vessel segment parameters, the proximal normal frame and The far-end normal frame is calculated to obtain the reference lumen information.
  • the present invention does not limit the specific length of the blood vessel segment of interest.
  • the blood vessel segment of interest may have side branches. The existence of the side branch will affect the calculation of the reference lumen.
  • the reference information obtained by detecting and analyzing the image data of the blood vessel segment of interest also includes the side branch blood vessel segment.
  • the side branch blood vessel segment parameters can include one or more of the opening area of the side branch, the position of the side branch blood vessel segment, and the direction of the side branch.
  • Other information of the side branch blood vessel can also be obtained as needed for calculation Get more accurate reference lumen information. Taking the area of the opening as an example, by dividing the contour of the side branch, and then counting the number of pixels in the contour, the opening area of each side branch can be calculated according to the image resolution.
  • the specific calculation of the lumen and the FFR calculation considering the influence of the side branch blood vessel segment can refer to the method in the patent publication number CN108022650A.
  • the reference information also includes side branch blood vessel segment parameters of the blood vessel segment of interest, and the side branch blood vessel segment parameters are calculated from the lumen contour parameters.
  • the step of obtaining reference lumen information according to the media contour parameters includes: dividing the blood vessel segment of interest into multiple sub-segments according to the side branch vessel segment parameters; obtaining the normal frame in each sub-segment according to the media contour parameters; according to each sub-segment The normal frame in the segment gets the reference lumen information.
  • the blood vessel segment of interest can be divided into multiple sub-segments according to the position of the side branch opening on the main branch.
  • the blood vessel segment of interest when the blood vessel segment of interest has three side branches, the blood vessel segment of interest can be divided into four sub-segments according to the positions of the openings of the three side branches. Then, referring to the determination method of PN and DN, the normal frame in each sub-segment is determined. Then, according to the normal frame in each sub-segment, the reference lumen is reconstructed, and the corresponding reference lumen information is calculated.
  • the specific method can refer to the method of calculating the reference lumen information from PN and DN.
  • the blood vessel segment of interest is segmented according to the side branches, multiple normal frames are obtained to calculate the reference lumen information, which increases the sampling amount, that is, the number of acquisitions of normal frames, and the side branch segmentation is used More reasonable, so the calculation of reference lumen information is more accurate.
  • the corresponding reference lumen information is calculated by the media contour parameters and the side branch vessel segment parameters.
  • the reference lumen information is not accurate. And the influence of the presence of side branch vessels on the calculation of the reference lumen, so that the obtained reference lumen information is more accurate, and the corresponding FFR value is also more accurate.
  • the method for processing blood vessel images further includes the following steps: calculating each side branch according to the reference lumen information and the side branch blood vessel segment parameters Display the chart of the blood vessel segment of interest, and display each side branch in the corresponding position of the chart according to the parameters of the side branch blood vessel segment; and/or display the cross-sectional profile of the side branch and display the calibration value.
  • the specific type of calibration value can be set as required.
  • the calibration value may be one or more of Murray parameters, Finet parameters, HK parameters, and AP parameters.
  • D 0 the diameter of the main branch before the bifurcation at each side branch vessel segment
  • D 1 the opening diameter of the side branch vessel on the main branch
  • the ideal state of these parameter values is 1, so the closer the calibration value is to 1, the more reasonable in theory, so as to help users judge the division of each side branch and the rationality of the reference lumen information.
  • the threshold range of the calibration value can be set as required.
  • the threshold range of the calibration value can be set to 0.7-1.3.
  • the calculation results of the side branch vessel segment parameters and/or reference lumen information may deviate from the normal physiological range, prompting the user to obtain the image data again Or adjust the image data to ensure that the FFR calculation result is more accurate and improve the user experience.
  • each side branch is displayed in the corresponding position of the chart according to the parameters of the side branch blood vessel segment.
  • the chart display form of the blood vessel segment of interest can be in various forms, such as long and short axis display, or It is an equivalent diameter display. This embodiment does not limit this, and can be set and selected according to the user's habits and needs.
  • the corresponding side branch blood vessel segment can be displayed on the corresponding position of the chart.
  • the equivalent diameter of the respective side branch openings can also be displayed on the corresponding position of the graph. Displaying the side branches in the chart of the blood vessel segment of interest can facilitate the user to observe the blood vessel segment of interest intuitively and comprehensively.
  • the cross-section of the side branch blood vessel segment and the lumen contour of the side branch can be reconstructed and displayed through the three-dimensional image slicing algorithm and other methods.
  • the obtained calibration value is displayed in the corresponding side branch snapshot, so that the user can visually observe the calibration value and contour, and determine whether the side branch blood vessel segment parameters and the reference lumen information corresponding to the main branch are incorrect.
  • Branch in the figure corresponds to “side branch”
  • MU corresponds to "Murray parameter”
  • 3D corresponds to "three-dimensional”
  • ADD SB corresponds to "add side branch”
  • DELETE SB corresponds to "delete side branch”.
  • the upper part of the figure shows the cross-sectional contours of the four side branches and marked them. The marking shows the corresponding lumen contour of each side branch. In the cross-sectional contour
  • the upper right of the display shows the calibration value corresponding to the side branch, and also shows the lumen area corresponding to the lumen contour of each side branch.
  • 3D can be used to switch to display the main branch and/or the main branch and/or in the blood vessel segment of interest after 3D reconstruction Side branch blood vessel segment.
  • the lower part of the figure is used to edit the side branches, that is, add or delete side branches, and display any cross-sectional window of the side branches, so that the user can directly and clearly view the side branch segmentation of the blood vessel of interest, so as to judge the obtained side branch blood vessel Whether the segment parameter is wrong.
  • the method for processing a blood vessel image further includes the following steps: obtaining the blood vessel position of the blood vessel segment of interest; and correcting the blood vessel segment of interest according to the blood vessel position Fractional flow reserve (FFR) value of
  • FFR Fractional flow reserve
  • there may be multiple vessel positions corresponding to the vessel segment of interest such as anterior descending vessel, circumflex vessel, right coronary vessel, diagonal branch vessel, septal branch vessel, middle branch vessel, obtuse edge Branch blood vessels and so on.
  • the same algorithm is used in the analysis and detection process corresponding to the image data, it will cause a certain deviation of the reference information, and then affect the FFR value. Therefore, acquiring the blood vessel position of the blood vessel segment of interest, adjusting the detection and analysis algorithm of the image data according to the difference of the blood vessel position, or correcting the FFR value according to the blood vessel position can make the FFR value more accurate.
  • the reference information also includes side branch blood vessel segment parameters of the blood vessel segment of interest, and the side branch blood vessel segment parameters are calculated from the lumen contour parameters.
  • the blood vessel image processing method further includes the following steps: obtaining the blood vessel type of the blood vessel segment of interest according to the position of the blood vessel; when the blood vessel type is a bifurcation model: obtaining the bifurcation node information of the blood vessel segment of interest according to the side branch vessel segment parameters; Display the longitudinal section window of the vessel segment of interest; according to the bifurcation node information, use different colors to mark the main branch and branch of the vessel segment of interest in the longitudinal section window.
  • the type corresponding to the blood vessel of interest can be obtained, for example, whether it is a bifurcation model or a single-vessel model.
  • the bifurcation will affect the calculation of FFR. Therefore, in this embodiment, when the blood vessel type is a bifurcation model, bifurcation node information can be obtained according to the side branch vessel segment parameters.
  • the largest side branch is set as the bifurcation node, and the largest side branch can be found through the area or diameter of the side branch blood vessel in the side branch blood vessel segment parameters, as the bifurcation node, then the blood vessel corresponding to the side branch
  • the segment parameter is the branch node information corresponding to the branch.
  • the step of obtaining image data of a blood vessel segment of interest includes: obtaining image data of a target blood vessel and corresponding
  • the acquisition parameters include layer thickness and pixel size; reconstruct and display the image of the target blood vessel according to the image data and acquisition parameters of the target blood vessel; obtain the image data of the selected blood vessel segment of interest in the image.
  • the blood vessel segment corresponding to one image data collection is called the target blood vessel.
  • the blood vessel segment of interest can be either the target blood vessel or just a certain segment of the target blood vessel.
  • the acquisition parameters include layer thickness and pixel size.
  • the layer thickness refers to the collection distance interval between adjacent frames of image data, and there are many ways to obtain it.
  • the layer thickness may be obtained by directly obtaining the layer thickness value, or may be calculated after obtaining the withdrawal speed and the acquisition frame rate, which is not limited in this embodiment.
  • the pixel size refers to the actual size corresponding to each pixel in the image data, and there are many ways to obtain it.
  • the pixel size can be obtained by directly obtaining the actual size value corresponding to each pixel point, or by calculating the size of the catheter opening and the number of pixels corresponding to it during the measurement process, which is not limited in this embodiment.
  • methods such as three-dimensional image slicing algorithms can be used to reconstruct and display the image corresponding to the target blood vessel.
  • the longitudinal and cross-sectional view windows of the reconstructed target blood vessel can be displayed.
  • the user can select the vessel segment for which the FFR value needs to be calculated according to the corresponding image, that is, the vessel segment of interest.
  • the image data belonging to the blood vessel segment of interest is extracted from the image data of the target blood vessel, and the image data of the blood vessel segment of interest is obtained.
  • the step of reconstructing and displaying the image of the target blood vessel according to the image data and acquisition parameters of the target blood vessel includes: image data of the target blood vessel Perform resampling and reordering; reconstruct and display the image of the target blood vessel based on the reordered image data and acquisition parameters of the target blood vessel.
  • the image data of the target blood vessel can come from multiple sources according to different collection methods.
  • the image data may be OCT (Optical Coherence Tomography) images or IVUS (Intravascular Ultrasound) images.
  • the image data can be resampled and reordered to eliminate this influence.
  • the specific resampling method can be equal interval sampling, importance sampling, etc., which is not limited in this embodiment.
  • the IVUS image can be sampled at equal intervals with an interval of 5 frames, that is, the first frame, the sixth frame...the 1+5N frame in the image data are all sampled and sorted accordingly. Then according to the image data after resampling and reordering and the acquisition parameters of the target blood vessel, the image of the target blood vessel is reconstructed and displayed. This not only overcomes the problem of chaotic frames that may exist in the image, makes the subsequent acquisition of reference information more accurate, but also reduces the computational load in the reconstruction and display process.
  • the step of reconstructing and displaying the image of the target blood vessel according to the image data and acquisition parameters of the target blood vessel includes: according to the image data of the target blood vessel And the acquisition parameters are used to reconstruct the image of the target blood vessel; the image of the target blood vessel is registered to correct the error in the acquisition process of the image data of the target blood vessel; the registered image is displayed.
  • the catheter may be displaced in the radial direction during retraction, that is, the position of the catheter in each frame of the image data is not fixed at this time. Therefore, the registration of the image of the target blood vessel can eliminate the deviation caused by this displacement.
  • the center of the catheter may be the center of each frame of image, and each frame of image may be aligned in the longitudinal direction.
  • image registration can eliminate the influence of catheter displacement during the image data collection process, facilitate observation, and make the reference information obtained by subsequent detection and analysis of image data more accurate.
  • the reference information also includes stent information
  • the method for processing a blood vessel image further includes the following steps: acquiring stent parameters of the blood vessel segment of interest; Stent parameters Detect the stent and reconstruct the stent, and evaluate the stent to obtain stent information.
  • the stent information includes stent position and stent contour information; the longitudinal section window showing the blood vessel segment of interest; the pseudo-color bar is displayed in the longitudinal section window according to the stent information Identify the bracket.
  • the method disclosed in this embodiment acquires the stent parameters of the blood vessel segment of interest before detecting and analyzing the image data of the blood vessel segment of interest.
  • the stent parameters can be set as required, for example, Including the type and thickness of the stent.
  • the image data is segmented with reference to the stent parameters, the corresponding stent is detected, and the stent information is obtained, including the stent position and the stent contour information.
  • detection methods can be used to pre-process and post-process the image data to be segmented.
  • the pre-processing performs polar coordinate transformation and image standardization on the image data.
  • Original coordinate reconstruction and continuity-based misdetection elimination can also be used for stent detection based on deep learning.
  • the stent is reconstructed.
  • the volume reconstruction method can be used to perform three-dimensional volume reconstruction on the continuous two-dimensional mask results of the stent segmentation, that is, to assign the layer spacing of the original image to the stent on each frame of image. Become a single voxel for volume reconstruction.
  • the stent can also be evaluated, for example, by calculating the distance between the stent and the tube wall, the attachment and expansion of the stent can be evaluated.
  • the specific method can be to draw a ray from the center of the lumen to the center of the stent according to the center position of each stent point and the lumen contour curve based on the reference information, and calculate the intersection point of the ray and the lumen, so as to calculate the center point of the stent and the lumen
  • the qualitative analysis result of judging the distance between the stent and the lumen and whether the stent is attached to the wall, not attached or covered It is also possible to fit the stent contour ellipse based on the position of each stent in a single frame image, calculate the average contour area and the minimum contour area of the stent, and compare it with the reference lumen information to determine the expansion of the stent.
  • the stent in the longitudinal section window of the blood vessel segment of interest, can be marked with a pseudo-color bar in the longitudinal section window according to the stent information.
  • the different attachment conditions of the stent can be marked according to the color depth, as shown in Figure 13, This facilitates the user to understand the status of the stent. It is also possible to display the relevant parameters of the stent in the cross-sectional profile window of the blood vessel segment of interest, as shown in Figure 12, where "Stent” corresponds to "stent” and "Expansion” corresponds to "expansion”.
  • the bracket information is used as a part of the reference information, and the FFR calculation result will be corrected.
  • its lumen contour information can be corrected according to the stent information.
  • the area occupied by the stent point itself can be obtained according to the stent contour information in the stent information, and the lumen area of each frame of image is subtracted from all of the frame.
  • the area of the stent point is used to obtain the lumen area after placing the stent as a part of the lumen profile parameters for subsequent FFR calculations. Therefore, this embodiment can improve the accuracy of FFR calculation for the blood vessel segment of interest with stent placement and facilitate the user to observe and understand the state of the stent.
  • the method for processing a blood vessel image further includes the following steps: quantifying the first with a stenosis rate greater than a threshold according to reference lumen information and reference information.
  • Characteristic blood vessel segment a chart showing the blood vessel segment of interest; marking the first characteristic blood vessel segment whose stenosis rate is in the set interval in the chart.
  • the diameter information of the reference lumen can be obtained according to the reference lumen information, and the actual lumen diameter can be obtained according to the lumen contour parameters in the reference information, so as to calculate the stenosis rate corresponding to each position (each frame of image) in the blood vessel segment of interest .
  • the threshold of the stenosis rate can be set according to the user's observation and attention needs. For example, if the user needs to focus on the blood vessel segment with a stenosis rate greater than 20% according to his habit, he can set the threshold of the stenosis rate to 0.2.
  • the blood vessel segment whose stenosis rate is greater than the threshold is called the first characteristic blood vessel segment.
  • the corresponding first characteristic blood vessel segment can be marked on the chart showing the blood vessel segment of interest.
  • the marked interval can also be set. For example, if only the first M first characteristic blood vessel segments with the largest stenosis rate are marked, then the first M first characteristic blood vessel segments can be marked with a marking line on the chart, and each The stenosis rate corresponding to the segment is marked below the first characteristic blood vessel segment, which is convenient for the user to observe the stenosis of the blood vessel segment of interest.
  • Another embodiment of the present invention discloses a method for processing a blood vessel image.
  • the reference information further includes the second feature information
  • the blood vessel image processing method further includes The following steps: reconstruct and display the blood vessel segment of interest and the second feature according to the reference information.
  • the second feature can be set as needed, such as plaque, thrombus, dissection, etc., which is not limited in this embodiment.
  • image processing can be performed on multiple frames of images in the image data.
  • the second feature information can include information such as the area, thickness, and angle of the patch.
  • the area can be accumulated and calculated pixel by pixel, the distance between pixels can be calculated thickness, and the pixel distribution calculation angle can be calculated.
  • the cross-sectional contour of the blood vessel segment of interest and the corresponding plaque can be displayed.
  • a correction factor is added to the calculation of FFR, and the calculation of FFR is adjusted to make the calculation result of FFR more accurate.
  • both the longitudinal section window of the blood vessel segment of interest, the cross-sectional window of the blood vessel segment of interest are displayed, and the three-dimensional reconstruction window of the blood vessel segment of interest is also displayed, which is convenient for the user to observe intuitively and clearly.
  • the display mode is set in advance according to the possible types of plaques. For example, you can include all modes to display all types of plaques; vulnerable plaque mode to display lipid plaques, fibrous caps, and macrophages; and calcification mode to display calcified plaques.
  • the patch display mode can be adjusted according to the user's choice, which is convenient for the user to classify and observe.
  • different plaques can be displayed with different color markers, which is convenient for the user to observe.
  • the statistical parameters corresponding to the plaques such as the area corresponding to the plaques, are displayed at the same time, so as to facilitate the user's quantitative analysis.
  • the method for processing a blood vessel image further includes the following steps: displaying the longitudinal profile and/or transverse profile of the blood vessel segment of interest according to the reference information. Sectional profile; adjust the longitudinal section profile and/or cross-sectional profile; update the reference information, and/or reference lumen information, and the blood flow reserve (FFR) value according to the adjusted longitudinal section profile and/or cross-sectional profile.
  • the contour of the blood vessel segment of interest is displayed, and the corresponding segmentation of the lumen contour and the media contour can be observed from the contour.
  • the longitudinal section profile and/or cross section profile can be dynamically adjusted to correct the FFR value. For example, when calculating the reference lumen information, whether the media contour parameters are used to obtain PN and DN, and then the reference lumen is calculated, or the media contour parameters and the normal intima thickness are used to obtain the reference lumen, the media contour Obvious errors in segmentation will affect the calculation of reference lumen information and FFR. Therefore, the corresponding contour can be displayed to assist the user in judging whether there is an obvious error in the segmentation. If there is, the contour can be adjusted according to the user's operation.
  • the media contour or the lumen contour in PN and DN can be adjusted. Changing the media contour parameters and the lumen contour parameters, you can also directly select other frames as PN and/or DN, and you can also animate the ellipse as PN and/or DN in the existing frame, which improves the accuracy of FFR calculation.
  • the longitudinal section contour and/or the cross section contour of the blood vessel segment of interest are displayed.
  • the contour can be adjusted according to the user's operation, and the cross-sectional contour can be directly modified in each frame.
  • the chart of the blood vessel segment of interest is displayed, such as the chart of the equivalent diameter, the corresponding chart will also be updated.
  • contour includes the display and adjustment of contour.
  • the right side of the figure is used to display the contour, including the cross-sectional contour and the longitudinal section contour of the blood vessel segment of interest.
  • different markings can be used to mark the corresponding lumen contour and media contour.
  • the area of the plaque at the cross section and the corresponding load rate can also be displayed.
  • the lower part can display the chart of this segment of blood vessels, such as the equivalent diameter chart in the figure, or switch to display other charts, such as the short axis diameter chart.
  • you can edit the contour marked in the chart and the reselection of the blood vessel segment of interest, and re-detect and analyze the image data of the blood vessel segment of interest according to the updated contour to obtain new reference information, and update the FFR calculate.
  • the calculation result of FFR can also be displayed on the corresponding chart, and the cross-sectional view of the branch can also be displayed together, which is convenient for the user to adjust the contour or side branch in the window to make the value of FFR more precise.
  • the longitudinal section contour and/or cross-sectional contour of the blood vessel segment of interest are displayed according to the reference information, and according to the adjustment of the side branch, such as the adjustment of the contour and size of the side branch, Update the parameters of the side branch vessel segment, thereby updating the calculation result of FFR, making the calculation of FFR more accurate.
  • the bifurcation node information can be updated according to the adjustment of the bifurcation node, for example, any side branch is reselected as the bifurcation node, thereby updating the calculation result of FFR, making the calculation of FFR more precise.
  • the first characteristic blood vessel segment can also be adjusted, such as adding, deleting or editing.
  • the adjustment of the longitudinal section profile and/or cross-sectional profile of the blood vessel segment of interest can be achieved by adjusting the media profile, lumen profile, and side branch profile of the corresponding image.
  • the setting and selection can dynamically adjust the FFR calculation result, which has the effect of making the FFR value more accurate.
  • the method for processing a blood vessel image further includes the following step: displaying the fractional blood flow reserve (FFR) value in the form of pseudo-color.
  • FFR fractional blood flow reserve
  • the chart of the blood vessel segment of interest On the chart of the blood vessel segment of interest; and/or display the chart of the blood vessel segment of interest and superimpose the simulated retracement curve; and/or reconstruct the blood vessel segment of interest in three dimensions and display the blood vessel segment of interest after the three-dimensional reconstruction.
  • the specific type of the chart of the blood vessel segment of interest can be selected as required.
  • Displaying the FFR value on the chart of the blood vessel segment of interest in the form of false color can clearly and intuitively reflect the shape of the blood vessel segment of interest and the change of the FFR value, which is convenient for users to observe and analyze.
  • There are many specific display methods for example, you can refer to the method in the publication number CN109166101A.
  • Superimposing the simulated retracement curve on the chart of the blood vessel segment of interest can assist the user in judging the FFR calculation result based on the simulated retracement curve.
  • Three-dimensional reconstruction of the blood vessel segment of interest, and display of the blood vessel segment of interest after the three-dimensional reconstruction can be drawn through volume reconstruction, using a ray tracing method, which is easy to implement and has a good reconstruction effect.
  • the three-dimensionally reconstructed longitudinal section image, three-dimensional image, and cross-sectional image of the blood vessel segment of interest are displayed, so that the user can comprehensively observe the blood vessel segment of interest.
  • a three-dimensional image of the blood vessel segment of interest after three-dimensional reconstruction is displayed.
  • the corresponding stents, side branches, plaques, etc. can be identified in the corresponding images, so that the user can better observe and analyze the blood vessel segment of interest.
  • the three-dimensional reconstructed stent can be displayed.
  • the corresponding stent may be displayed in the chart of the blood vessel segment of interest.
  • the embodiment of the present invention also discloses a blood vessel image processing system, including: an acquisition module 1 for acquiring image data of the blood vessel segment of interest and blood flow parameters of the blood vessel segment of interest; an analysis module 2. Detect and analyze the image data of the blood vessel segment of interest to obtain reference information.
  • the reference information includes the lumen contour parameters and media contour parameters of the blood vessel segment of interest;
  • the calculation module 3 includes a first calculation unit 31 and a second calculation unit 32
  • the first calculation unit 31 is used to obtain the reference lumen information according to the media contour parameters
  • the second calculation unit 32 is used to calculate the blood flow reserve score of the blood vessel segment of interest ( FFR) value.
  • the calculation of the blood vessel pressure difference can be more accurate.
  • the first calculation unit 31 includes a normal frame extraction unit and a reference lumen calculation unit.
  • the normal frame extraction unit is used to obtain the proximal normal frame and the distal normal frame according to the media contour parameters
  • the reference lumen calculation unit is used for
  • the reference lumen information is calculated according to the near-end normal frame and the far-end normal frame.
  • the proximal normal frame and the distal normal frame are determined by replacing the lumen contour parameters with the media contour parameters, and then the corresponding reference lumen is calculated, so that the reference lumen is obtained
  • the information is more accurate, reducing the error caused by determining the PN and DN by the lumen contour parameters when the characteristic information such as plaque exists.
  • the obtaining module 1 is also used to obtain the normal intima thickness of the blood vessel segment of interest, and the first calculation unit 31 obtains the reference lumen information according to the media contour parameter and the normal intima thickness.
  • the corresponding reference lumen is calculated based on the media contour parameters and the normal intima thickness, so that the obtained reference lumen information is more accurate, and the influence of the intima thickness is eliminated.
  • the FFR value is also more accurate.
  • the reference information also includes side branch vessel segment parameters of the blood vessel segment of interest.
  • the side branch vessel segment parameters are calculated by the analysis module 2 according to the lumen contour parameters.
  • the first calculation unit 31 includes a normal frame extraction unit and a reference lumen.
  • the calculation unit, the normal frame extraction unit is used to obtain the proximal normal frame and the distal normal frame according to the media contour parameters
  • the reference lumen calculation unit is used to calculate according to the side branch blood vessel segment parameters, the proximal normal frame and the distal normal frame Refer to lumen information.
  • the corresponding reference lumen information is calculated through the media contour parameters and the side branch vessel segment parameters, so that the obtained reference lumen information is more accurate, and the corresponding FFR value is also more accurate.
  • the reference information also includes side branch vessel segment parameters of the blood vessel segment of interest.
  • the side branch vessel segment parameters are calculated by the analysis module 2 according to the lumen contour parameters.
  • the first calculation unit 31 includes a normal frame extraction unit and a reference lumen.
  • the calculation unit, the normal frame extraction unit is used to divide the blood vessel segment of interest into multiple sub-segments according to the side branch vessel segment parameters, and obtain the normal frame in each sub-segment according to the media contour parameters.
  • the reference lumen calculation unit is used to The normal frame in each sub-segment gets the reference lumen information.
  • multiple normal frames are acquired to calculate the reference lumen information, which increases the sampling amount and uses side branch segmentation to be more reasonable, thus making the calculation of the reference lumen information more accurate.
  • the calculation module 3 further includes a third calculation unit.
  • the third calculation unit is used to calculate the calibration value at each side branch according to the reference lumen information and the side branch blood vessel segment parameters.
  • the display module is used to display the chart of the blood vessel segment of interest, and displays each side branch in the corresponding position of the chart according to the side branch blood vessel segment parameters, and/or the first display module is also used to display the cross-sectional profile of the side branch, and The calibration value is displayed.
  • the user can help the user to judge the rationality of the segmentation of each side branch and the reference lumen information, so that the user can intuitively and comprehensively observe the blood vessel segment of interest, and observe the corresponding media contour and tube. Whether there are obvious errors in the cavity contour.
  • the blood vessel image processing system further includes a correction module.
  • the acquisition module 1 is also used to acquire the blood vessel position of the blood vessel segment of interest, and the correction module is used to correct the blood flow reserve (FFR) of the blood vessel segment of interest according to the blood vessel position. value.
  • FFR blood flow reserve
  • the algorithm for detecting and analyzing image data is adjusted according to the position of the blood vessel, or the FFR value is corrected according to the blood vessel position, which can make the FFR value more accurate.
  • the blood vessel image processing system further includes a second display module
  • the reference information also includes side branch blood vessel segment parameters of the blood vessel segment of interest
  • the side branch blood vessel segment parameters are calculated from the lumen contour parameters
  • the acquisition module 1 also uses
  • the analysis module 2 obtains the bifurcation node information of the blood vessel segment of interest according to the side branch vessel segment parameters
  • the second display module displays the blood vessel of interest The longitudinal section window of the segment, and according to the bifurcation node information, use different colors to identify the main branch and branch of the blood vessel segment of interest in the longitudinal section window.
  • the acquisition module 1 includes a first acquisition unit, a second acquisition unit, a reconstruction calculation unit, and a display unit.
  • the first acquisition unit is used to acquire image data of the target blood vessel and corresponding acquisition parameters.
  • the acquisition parameters include layer thickness and pixels.
  • the reconstruction calculation unit is used to perform reconstruction calculation according to the image data and acquisition parameters of the target blood vessel
  • the display unit is used to display the reconstructed target blood vessel image
  • the second acquisition unit is used to obtain the selected blood vessel segment of interest in the image Image data.
  • the acquisition of the image data of the blood vessel segment of interest can be made more convenient, intuitive, and easy to operate.
  • the acquisition module 1 further includes a re-sampling unit, the re-sampling unit is used to re-sample and re-order the image data of the target blood vessel acquired by the first acquisition unit, and the reconstruction calculation unit is used to perform re-sampling and re-ordering according to the re-ordered target blood vessel Image data and acquisition parameters are reconstructed and calculated.
  • the problem of chaotic frames that may exist in the image is overcome, so that the subsequent reference information obtained is more accurate, and the calculation load in the reconstruction and display process is reduced.
  • the acquisition module 1 further includes a registration unit.
  • the registration unit is used to register the image of the target blood vessel to correct errors in the acquisition process of the image data of the target blood vessel.
  • the display unit is used to display the registered image data. image.
  • the reference information also includes stent information
  • the acquisition module 1 is also used to acquire stent parameters of the blood vessel segment of interest
  • the analysis module 2 is also used to detect the stent and reconstruct the stent according to the stent parameters
  • the stent is evaluated to obtain stent information.
  • the stent information includes stent position and stent contour information.
  • the third display module is used to display the longitudinal section window of the blood vessel segment of interest, and mark the stent with pseudo-color bars in the longitudinal section window according to the stent information.
  • the calculation module 3 further includes a fourth calculation unit configured to quantify the first characteristic blood vessel segment whose stenosis rate is greater than the threshold according to the reference lumen information and the reference information, and the fourth The display module is used to display the chart of the blood vessel segment of interest, and mark the first characteristic blood vessel segment whose stenosis rate is in the set interval in the chart.
  • the blood vessel segment of interest includes the second feature
  • the reference information further includes second feature information
  • the analysis module 2 is further configured to reconstruct the blood vessel segment of interest and the second feature based on the reference information
  • the fifth display module is used to display the blood vessel segment of interest and the second feature.
  • the adjustment module includes a display unit, an adjustment unit, and an update unit.
  • the section profile and/or cross-sectional profile, the updating unit is used to update the reference information, and/or reference lumen information, and the blood flow reserve (FFR) value according to the adjusted longitudinal section profile and/or cross-sectional profile.
  • the longitudinal section contour and/or cross-sectional contour of the blood vessel segment of interest can be adjusted by adjusting the media contour, lumen contour, and side branch contour of the corresponding image.
  • users can set and select according to their needs, and can dynamically adjust the FFR calculation results, which has the effect of making the FFR value more accurate.
  • a sixth display module which is used to display the fractional blood flow reserve (FFR) value on the chart of the blood vessel segment of interest in a pseudo-color form, and/or the displayed blood vessel segment of interest
  • the chart superimposes and displays the simulated retracement curve, and/or three-dimensional reconstruction of the blood vessel segment of interest, and displays the three-dimensional reconstruction of the blood vessel segment of interest.
  • these display modules can be separate and independent so as to be able to display corresponding information and images at the same time, or they can be combined and displayed alternately.
  • the display image and information can be switched by pressing the button, which can be specifically set according to the needs of the user.
  • the embodiment of the present invention also discloses a computing device, including: a processor, which is suitable for implementing various instructions; a memory, which is suitable for storing multiple instructions, and the instructions are suitable for being loaded by the processor and executed by any of the foregoing embodiments.
  • a computing device including: a processor, which is suitable for implementing various instructions; a memory, which is suitable for storing multiple instructions, and the instructions are suitable for being loaded by the processor and executed by any of the foregoing embodiments.
  • the processing method of blood vessel images including: a processor, which is suitable for implementing various instructions; a memory, which is suitable for storing multiple instructions, and the instructions are suitable for being loaded by the processor and executed by any of the foregoing embodiments.
  • the calculation of the blood vessel pressure difference can be more accurate.
  • the embodiment of the present invention also discloses a storage medium, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executed by any one of the blood vessel image processing methods in the foregoing embodiments.
  • the various embodiments disclosed in this application may be implemented in hardware, software, firmware, or a combination of these implementation methods.
  • the embodiments of the present application can be implemented as a computer program or program code executed on a programmable system including at least one processor and a storage system (including volatile and non-volatile memory and/or storage elements) , At least one input device and at least one output device.
  • Program codes can be applied to input instructions to perform the functions described in this application and generate output information.
  • the output information can be applied to one or more output devices in a known manner.
  • a processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • the program code can be implemented in a high-level programming language or an object-oriented programming language to communicate with the processing system.
  • assembly language or machine language can also be used to implement the program code.
  • the mechanism described in this application is not limited to the scope of any particular programming language. In either case, the language can be a compiled language or an interpreted language.
  • the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof.
  • the disclosed embodiments can also be implemented as instructions carried by or stored on one or more transient or non-transitory machine-readable (eg, computer-readable) storage media, which can be executed by one or more processors. Read and execute.
  • the instructions can be distributed through a network or through other computer-readable media.
  • a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (for example, a computer), including, but not limited to, floppy disks, optical disks, optical disks, read-only memories (CD-ROMs), magnetic Optical disk, read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), magnetic or optical card, flash memory, or A tangible machine-readable memory used to transmit information (for example, carrier waves, infrared signals, digital signals, etc.) using the Internet with electric, optical, acoustic, or other forms of propagating signals. Therefore, a machine-readable medium includes any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (for example, a computer).
  • each module/unit mentioned in each device embodiment of this application is a logical module/unit.
  • a logical module/unit can be a physical module/unit or a physical module/unit.
  • a part of the unit can also be realized by a combination of multiple physical modules/units.
  • the physical realization of these logical modules/units is not the most important.
  • the combination of the functions implemented by these logical modules/units is the solution to this application.
  • the above-mentioned device embodiments of this application do not introduce modules/units that are not closely related to solving the technical problems raised by this application. This does not mean that the above-mentioned device embodiments do not exist. Other modules/units.

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Abstract

L'invention concerne un procédé et un appareil de traitement d'images vasculaires, ainsi qu'un dispositif informatique et un support de stockage. Le procédé comprend les étapes consistant à: acquérir des données d'image d'un segment vasculaire d'intérêt; acquérir des paramètres de flux sanguin du segment vasculaire d'intérêt; tester et à analyser les données d'image du segment vasculaire d'intérêt pour obtenir des informations de référence, les informations de référence comprenant des paramètres de profil de lumière et des paramètres de profil de tunique moyenne du segment vasculaire d'intérêt; obtenir des informations de lumière de référence selon les paramètres de profil de tunique moyenne; et calculer une valeur de réserve de débit fractionnaire du segment vasculaire d'intérêt en fonction des paramètres de flux sanguin, des informations de référence et des informations de lumière de référence. Au moyen du procédé, le calcul d'une différence de pression vasculaire peut être plus précis.
PCT/CN2020/087304 2020-04-14 2020-04-27 Procédé et système de traitement d'images vasculaires, dispositif informatique, et support de stockage WO2021208140A1 (fr)

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