WO2022160973A1 - Coronary fractional flow reserve obtaining system and method, and medium - Google Patents

Coronary fractional flow reserve obtaining system and method, and medium Download PDF

Info

Publication number
WO2022160973A1
WO2022160973A1 PCT/CN2021/137375 CN2021137375W WO2022160973A1 WO 2022160973 A1 WO2022160973 A1 WO 2022160973A1 CN 2021137375 W CN2021137375 W CN 2021137375W WO 2022160973 A1 WO2022160973 A1 WO 2022160973A1
Authority
WO
WIPO (PCT)
Prior art keywords
coronary artery
coronary
artery model
model
module
Prior art date
Application number
PCT/CN2021/137375
Other languages
French (fr)
Chinese (zh)
Inventor
房劬
赵夕
Original Assignee
上海杏脉信息科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 上海杏脉信息科技有限公司 filed Critical 上海杏脉信息科技有限公司
Publication of WO2022160973A1 publication Critical patent/WO2022160973A1/en

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10Computer-aided planning, simulation or modelling of surgical operations
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026Measuring blood flow
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/045Combinations of networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/77Retouching; Inpainting; Scratch removal
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/10Segmentation; Edge detection
    • G06T7/11Region-based segmentation
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/10Segmentation; Edge detection
    • G06T7/136Segmentation; Edge detection involving thresholding
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/101Computer-aided simulation of surgical operations
    • A61B2034/105Modelling of the patient, e.g. for ligaments or bones
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20081Training; Learning
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20084Artificial neural networks [ANN]
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • G06T2207/30096Tumor; Lesion
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • G06T2207/30101Blood vessel; Artery; Vein; Vascular

Definitions

  • Fractional Flow Reserve is an effective indicator to measure the coronary blood supply in humans.
  • Fractional coronary flow reserve refers to the ratio of the maximum blood flow Q S that can be obtained in the myocardial area supplied by the blood vessel under the condition of coronary stenosis and the maximum blood flow Q N that can theoretically be obtained in the same area under normal conditions. ,which is: The methods of obtaining FFR in the prior art can be divided into invasive methods and non-invasive methods.
  • the method of obtaining FFR by an invasive method refers to invasively inserting a pressure guide wire into a coronary artery under the guidance of medical images, measuring the pressure at the distal end of the stenosis and the pressure at the proximal end of the stenosis, and calculating the FFR index. Invasive measurement methods cause great damage to patients, which limits the clinical application of FFR indicators.
  • the method of obtaining FFR by non-invasive method is to obtain the FFR index according to the medical image of the patient.
  • both Q S and Q N are indicators that are difficult to obtain. Therefore, in practice, it is often used replace To calculate FFR, at this time, the formula to obtain FFR is That is: the mean coronary pressure distal to the stenosis in the state of maximal myocardial hyperemia Coronary Oral Aortic Mean Pressure ratio.
  • the inventors found in practical applications, and There are often differences between the replace to calculate the FFR introduces additional error that affects the accuracy of the FFR. In particular, when the stenosis position of the coronary artery is far from the aorta, the error is also larger, and the accuracy of the obtained FFR is lower at this time.
  • the purpose of the present invention is to provide a system, method and medium for obtaining fractional coronary blood flow reserve, which are used to solve the problem of low accuracy of the obtained FFR in the prior art.
  • a first aspect of the present invention provides a system for obtaining fractional coronary blood flow reserve
  • the system for obtaining fractional coronary blood flow reserve includes: a medical image obtaining module for obtaining the medical image of a patient.
  • the medical image includes the heart region of the patient; a coronary artery model acquisition module, connected to the medical image acquisition module, is used to acquire the first coronary artery model and the second coronary artery model of the patient according to the medical image; wherein , the first coronary artery model refers to the actual coronary artery model of the patient, and the second coronary artery model refers to the coronary artery model obtained after repairing at least one lesion of the coronary artery of the patient; the blood flow reserve fraction is obtained
  • the module is connected to the coronary artery model acquisition module, and is used for acquiring the coronary blood flow reserve fraction of the patient according to the first coronary artery model and the second coronary artery model.
  • the coronary artery model acquisition module includes a first coronary artery model acquisition sub-module and a second coronary artery model acquisition sub-module; the first coronary artery model acquisition sub-module and the The medical image acquisition module is connected to the medical image to obtain the first coronary artery model by segmenting the medical image; the second coronary artery model acquisition sub-module is connected to the first coronary artery model acquisition sub-module, using performing simulated repair on at least one lesion of the patient's coronary vessel in the first coronary model to obtain the second coronary model; or, the second coronary model obtaining sub-module and the medical image The acquisition module is connected to the medical image for simulating repair of at least one lesion of the coronary blood vessel of the patient, and segmenting the medical image after the simulating repair to obtain the second coronary artery model.
  • the second coronary artery model acquisition sub-module includes: a first lesion parameter acquisition unit connected to the first coronary artery model acquisition sub-module for acquiring the first coronary artery model acquisition sub-module. a lesion parameter related to coronary artery stenosis in a coronary artery model; a first simulated repair unit, connected to the first lesion parameter acquisition unit, and configured to perform an analysis on the stenosis of the first coronary artery model according to the lesion parameter Modify to obtain the second coronary model.
  • the second coronary artery model acquisition sub-module includes: a second lesion parameter acquisition unit, which is connected to the image acquisition module and is used to acquire the coronary artery in the medical image. stenosis-related lesion parameters; a second simulated repair unit, connected to the second lesion parameter acquisition unit, configured to modify the stenosis of the patient's coronary blood vessels in the medical image according to the lesion parameters, so as to obtain the The medical image after simulating restoration; the image segmentation unit is connected to the second simulating restoration unit, and is used for segmenting the medical image after simulating restoration, so as to obtain the second coronary artery model.
  • the blood flow reserve fraction acquisition module includes: an actual blood flow acquisition unit, connected to the coronary artery model acquisition module, for acquiring a the actual maximum blood flow of the target blood supply area; the ideal blood flow acquisition unit, connected to the coronary artery model acquisition module, is used to acquire the ideal maximum blood flow of the target blood supply area according to the second coronary artery model; reserve fraction acquisition a unit, connected to the actual blood flow obtaining unit and the ideal blood flow obtaining unit, and configured to obtain the coronary blood flow reserve fraction according to the actual maximum blood flow and the ideal maximum blood flow of the target blood supply area.
  • the blood flow reserve fraction acquisition module includes: an actual pressure acquisition unit, which is connected to the coronary artery model acquisition module, and is used for acquiring myocardial maximum value according to the first coronary artery model.
  • the actual average pressure of the target position in the hyperemia state wherein the target position refers to the distal end of coronary artery stenosis
  • the ideal pressure acquisition unit is connected to the coronary artery model acquisition module, and is used for obtaining according to the second coronary artery
  • the model obtains the ideal average pressure of the target position under the state of maximum myocardial hyperemia
  • the reserve fraction obtaining unit is connected to the actual pressure obtaining unit and the ideal pressure obtaining unit, and is used for obtaining the actual average pressure and the ideal pressure according to the target position.
  • the mean pressure was used to obtain the fractional coronary flow reserve.
  • a second aspect of the present invention provides a method for obtaining fractional coronary blood flow reserve, the method for obtaining fractional coronary blood flow reserve includes: obtaining a medical image of a patient, the medical image including a heart region of the patient; The first coronary artery model and the second coronary artery model of the patient are obtained from the images; wherein, the first coronary artery model refers to the actual coronary artery model of the patient, and the second coronary artery model refers to at least the coronary artery model of the patient. Coronary artery model obtained after a lesion is repaired; the coronary blood flow reserve fraction of the patient is obtained according to the first coronary artery model and the second coronary artery model.
  • a third aspect of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the method for obtaining fractional coronary blood flow reserve described in the second aspect of the present invention.
  • the maximum blood flow Q S that can be obtained in the myocardial region supplied by the blood vessel under the condition of coronary stenosis and lesions can be obtained, and according to the second coronary artery model, the theoretical normal condition of the same region can be obtained.
  • the maximum blood flow Q N that can be obtained, the coronary blood flow reserve fraction of the patient can be directly obtained based on Q S and Q N . Therefore, the system for obtaining the fractional coronary blood flow reserve of the present invention does not adopt the method for obtaining the fractional coronary blood flow reserve. to replace Therefore, no additional errors will be introduced.
  • the fractional coronary blood flow reserve obtained by the system for obtaining the fractional coronary blood flow reserve of the present invention has higher accuracy than the prior art, especially in the case of coronary stenosis.
  • the advantages of the system for obtaining fractional coronary blood flow reserve of the present invention are more obvious.
  • FIG. 1 shows a schematic structural diagram of a system for obtaining fractional coronary blood flow reserve according to the present invention in a specific embodiment.
  • FIG. 2A is a schematic structural diagram of a coronary artery model acquisition module in a specific embodiment of the coronary blood flow fraction reserve acquisition system according to the present invention.
  • FIG. 2B is a schematic structural diagram of a second coronary artery model acquisition sub-module in an embodiment of the coronary blood flow fraction reserve acquisition system according to the present invention.
  • FIG. 2C is a diagram showing an example of part of blood vessels of the first coronary artery model of the system for obtaining fractional coronary flow reserve according to the present invention.
  • FIG. 2D is a diagram showing an example of part of blood vessels of the second coronary artery model of the system for obtaining fractional coronary flow reserve according to the present invention.
  • FIG. 3A is a schematic structural diagram of a coronary artery model acquisition module in a specific embodiment of the coronary flow reserve fraction acquisition system according to the present invention.
  • FIG. 3B is a schematic structural diagram of a second coronary artery model acquisition sub-module in a specific embodiment of the coronary blood flow fraction reserve acquisition system according to the present invention.
  • FIG. 4A is a schematic structural diagram of a coronary artery model acquisition module in a specific embodiment of the coronary flow reserve fraction acquisition system according to the present invention.
  • FIG. 4B is a schematic structural diagram of a coronary artery model acquisition module in a specific embodiment of the coronary blood flow fraction reserve acquisition system according to the present invention.
  • FIG. 5B is a schematic structural diagram of a fractional blood flow reserve acquisition module in a specific embodiment of the coronary blood flow reserve fraction acquisition system according to the present invention.
  • FIG. 6 shows a flowchart of the method for obtaining fractional coronary flow reserve according to the present invention in a specific embodiment.
  • the present invention provides a system for obtaining fractional coronary blood flow reserve.
  • the coronary blood flow reserve fraction acquisition system can acquire the patient's first coronary artery model according to the patient's medical image, and acquire the patient's second coronary artery model according to the patient's medical image or the first coronary artery model.
  • the first coronary artery model refers to the actual coronary artery model of the patient
  • the second coronary artery model refers to the coronary artery model obtained after repairing at least one lesion of the coronary artery of the patient.
  • the maximum blood flow Q S that can be obtained in the myocardial region supplied by the blood vessel under the condition of coronary stenosis and lesions can be obtained, and according to the second coronary artery model, the theoretical normal condition of the same region can be obtained.
  • the maximum blood flow Q N that can be obtained, the coronary blood flow reserve fraction of the patient can be directly obtained based on Q S and Q N . Therefore, the system for obtaining the fractional coronary blood flow reserve of the present invention does not adopt the method for obtaining the fractional coronary blood flow reserve. to replace Therefore, no additional errors will be introduced.
  • the fractional coronary blood flow reserve obtained by the system for obtaining the fractional coronary blood flow reserve of the present invention has higher accuracy than the prior art, especially in the case of coronary stenosis.
  • the advantages of the system for obtaining fractional coronary blood flow reserve of the present invention are more obvious.
  • the system 1 for acquiring fractional coronary blood flow reserve includes a medical image acquiring module 11 , a coronary artery model acquiring module 12 and a fractional blood flow reserve acquiring module 13 .
  • the medical image acquisition module 11 is used for acquiring a medical image of a patient, and the medical image of the patient includes a heart region of the patient.
  • the medical image is preferably a CT angiography (CTA, CT angiography) image, in addition, the medical image can also be a CT perfusion image, a plain CT image, a DSA angiography image, or an image obtained by using methods such as X-ray, nuclear magnetic resonance,
  • the medical images of the heart part obtained by scanning imaging methods such as ultrasound, PET, SPECT, etc., may also be intravascular images (eg, optical coherence imaging, intravascular ultrasound), and the like.
  • the fractional blood flow reserve acquisition module 13 is connected to the coronary artery model acquisition module 12, and is configured to acquire the coronary blood flow reserve fraction of the patient according to the first coronary artery model and the second coronary artery model.
  • the system for obtaining fractional coronary blood flow reserve obtains the fractional coronary blood flow reserve of the patient according to the first coronary model and the second coronary model, instead of using to replace to obtain the patient's coronary flow reserve fraction without introducing additional errors. Therefore, the fractional coronary blood flow reserve obtained in this embodiment has a higher accuracy than the prior art, especially when the stenosis position of the coronary artery is far away from the aorta, the coronary blood flow described in this embodiment is more accurate. The advantage of the reserve score acquisition system is even more obvious.
  • the medical image of the patient is a 3D image.
  • the three-dimensional image obtained after processing and calculating the two-dimensional image (such as DSA).
  • the first coronary artery model and the second coronary artery model are both three-dimensional geometric models of the coronary artery.
  • the first coronary artery model obtaining sub-module 121 may use a neural network-based image segmentation model (eg, U-Net, V-Net, etc.) to segment the medical image to obtain the first coronary artery model.
  • a neural network-based image segmentation model eg, U-Net, V-Net, etc.
  • the first coronary artery model obtaining sub-module 121 inputs the medical image into the image segmentation model, and the first coronary artery model can be obtained according to the output of the image segmentation model.
  • the first coronary artery model obtaining sub-module 121 may also use a threshold method to segment the medical image to obtain the first coronary artery model. Specifically, the first coronary artery model obtaining sub-module 121 obtains the gray value range of coronary blood vessels, and obtains all voxels (or pixel points) located within the gray value range from the medical image, The set composed of these voxel points (or pixel points) is the first coronary artery model.
  • the first lesion parameter acquisition unit 1221 is connected to the first coronary artery model acquisition sub-module 121, and is configured to acquire lesion parameters related to coronary stenosis in the first coronary artery model.
  • the lesion parameters are, for example, the stenosis position of the coronary artery, the blood vessel centerline of the stenosis position, the cross-section of the proximal stenosis and the cross-section of the distal end of the stenosis (or, the diameter of the blood vessel at the proximal end of the stenosis and the diameter of the distal end of the stenosis). vessel diameter).
  • the first lesion parameter obtaining unit 1221 may use a stenosis detection algorithm to obtain at least one stenosis position in the first coronary artery model, and use an existing geometric method to obtain the stenosis position Vessel centerline, cross-section proximal to the stenosis, and cross-section distal to the stenosis (or, vessel diameter proximal to the stenosis and vessel diameter distal to the stenosis).
  • An implementation manner of the stenosis detection algorithm is: according to the coronary vessels obtained by segmentation and their centerlines, calculate the diameters or cross-sections of different positions of the blood vessels along the centerline, and select the positions with diameters or cross-sections smaller than a threshold value as the coronary arteries. stenosis of the arteries.
  • Another implementation manner of the stenosis detection algorithm is: using an AI stenosis detection model to process the patient's first coronary artery model to obtain the coronary stenosis position.
  • the AI stenosis detection model is a trained deep learning network model, and its training data includes multiple coronary images marked with stenosis positions.
  • the stenosis positions of coronary images can be manually marked ;
  • the training of the AI stenosis detection model can be implemented by using an existing training method, which will not be repeated here.
  • the first simulated repair unit 1222 is connected to the first lesion parameter acquisition unit 1221, and is configured to modify the stenosis of the first coronary artery model according to the lesion parameters, so as to obtain the stenosis position when the stenosis does not appear.
  • the ideal blood vessel in stenotic lesions is obtained, and then the second coronary artery model is obtained.
  • the first simulated repair unit 1222 may take the blood vessel centerline of the stenosis position B as the axis of symmetry, and the cross-section of the proximal stenosis and the cross-section of the distal stenosis as the The end face generates a geometry of a specific shape, and uses the geometry of the specific shape to replace the blood vessel at the stenosis position B to obtain the second coronary artery model.
  • the first simulated repairing unit 1222 can use the blood vessel centerline of the stenosis position B as the axis of symmetry, and generate a geometry with a specific shape according to the blood vessel diameter at the proximal end of the stenosis and the blood vessel diameter at the distal end of the stenosis, and use the The geometry of the specific shape replaces the blood vessel at the stenosis position B to obtain the second coronary artery model.
  • Fig. 2C and Fig. 2D wherein Fig. 2C shows an example diagram of a vessel at a stenosis position in the first coronary artery model, and Fig. 2D shows the result of modifying the vessel at the stenosis position shown in Fig. 2C.
  • the first coronary artery model obtaining sub-module 121 is connected to the medical image obtaining module 11 , and is used for segmenting the medical image to obtain the first coronary artery model.
  • the second coronary artery model acquisition sub-module 122 is connected to the medical image acquisition module 11, and is used to simulate and repair at least one lesion of the patient's coronary vessels in the medical image, and to simulate and repair the medical image after the repair. Segmentation is performed to obtain the second coronary artery model.
  • an implementation structure of the second coronary artery model acquisition sub-module 122 includes a second lesion parameter acquisition unit 1223 , a second simulated repair unit 1224 and an image segmentation unit 1225 .
  • the second lesion parameter acquisition unit 1223 is connected to the image acquisition module 11, and is configured to acquire lesion parameters related to coronary stenosis in the medical image.
  • the lesion parameters are, for example, the stenosis position of the coronary artery, the blood vessel centerline of the stenosis position, the cross-section of the proximal stenosis and the cross-section of the distal end of the stenosis (or, the diameter of the blood vessel at the proximal end of the stenosis and the diameter of the distal end of the stenosis). vessel diameter).
  • the second lesion parameter obtaining unit 1223 may use a stenosis detection algorithm to obtain at least one stenosis position in the medical image, and use an existing geometric method to obtain the blood vessel centerline of the stenosis position , a cross-section of the proximal end of the stenosis and a cross-section of the distal end of the stenosis (or, the vessel diameter at the proximal end of the stenosis and the vessel diameter at the distal end of the stenosis).
  • the image segmentation unit 1225 is connected to the second simulated restoration unit 1224, and is used for segmenting the medical image after the simulated restoration to obtain the second coronary artery model. Specifically, the image segmentation unit 1225 may use a neural network-based image segmentation model or a threshold method to segment the medical image after the simulated restoration.
  • this embodiment provides a method for automatically repairing a patient's coronary vascular disease by using an algorithm to obtain a second coronary model.
  • the second coronary model can be directly used to obtain the coronary blood flow of the patient.
  • the reserve fraction can also be further revised manually on the basis of the second coronary artery model to obtain a more accurate second coronary artery model.
  • the fourth coronary artery model acquisition sub-module 124 uses a stenosis detection algorithm to acquire the stenosis position of the coronary artery in the first coronary artery model.
  • the arterial model obtaining sub-module 124 performs virtual treatment on at least one stenosis position of the coronary blood vessel of the patient.
  • the virtual treatment method is, for example, a virtual stent implantation technique or a virtual balloon dilation technique.
  • the coronary artery obtained after the virtual treatment is completed.
  • the model is the second coronary artery model.
  • the virtual stent implantation technology refers to that the fourth coronary artery model acquisition sub-module 124 implants a virtual stent into the stenosis position, so that the blood vessel in the stenosis position can be supported by the virtual stent to restore the normal state, so as to realize the virtual treatment.
  • the virtual balloon dilation technique means that the fourth coronary artery model acquisition sub-module 124 implants a virtual balloon implant into the stenosis position, and the virtual balloon implant has a load inside, and when After the virtual balloon implant is implanted into the three-dimensional model of the blood vessel, its internal load will expand under the action of external force, which will cause the balloon implant to produce plastic deformation, thereby supporting the blood vessel at the stenotic position.
  • the virtual treatment is realized by external expansion and eventually return to normal state.
  • the method for load expansion in the virtual balloon implant includes, but is not limited to, inflating it.
  • the third coronary artery model obtaining sub-module 123 is connected to the medical image obtaining module 11 , and is used for segmenting the medical image to obtain the first coronary artery model.
  • the fourth coronary artery model acquisition sub-module 124 is connected to the medical image acquisition module 11, and is used to perform virtual treatment on at least one lesion of the coronary artery of the patient in the medical image, and perform virtual treatment on the medical image after virtual treatment. Segmentation is performed to obtain the second coronary artery model.
  • the actual blood flow acquisition unit 131 is connected to the coronary artery model acquisition module 12, and is used for acquiring the actual maximum blood flow Q S of the target blood supply area according to the first coronary artery model, wherein the actual blood flow acquisition unit 131
  • Methods for obtaining Q S include, but are not limited to, fluid dynamics simulation, deep learning, and the like.
  • this embodiment can directly obtain the actual maximum blood flow Q S and the ideal maximum blood flow Q N of the target blood supply region, and obtain the coronary blood flow reserve fraction according to Q S and Q N . During this process, no to replace Thus no additional error is introduced.
  • the ideal pressure obtaining unit 135 is connected to the coronary artery model obtaining module 12, and is used to obtain the ideal average pressure of the target position in the maximum myocardial hyperemia state according to the second coronary artery model
  • the ideal pressure acquisition unit 135 acquires
  • the methods include but are not limited to fluid dynamics simulation, deep learning, etc.
  • the reserve score obtaining unit 136 is connected with the actual pressure obtaining unit 134 and the ideal pressure obtaining unit 135, and is used for obtaining the actual average pressure according to the target position and ideal mean pressure Obtain the coronary blood flow reserve fraction, wherein the coronary blood flow reserve fraction
  • the system for obtaining fractional coronary blood flow reserve further includes a user interaction module.
  • the user can input parameter annotation instructions by using tools such as brushes and erasers provided by the user interaction module through an input device such as a mouse (for example, by clicking a corresponding tool icon with a mouse, and dragging, clicking, or selecting a box, etc.) inputting the parameter labeling instructions) in the medical image and/or the first coronary artery model for stenosis position, stenosis proximal end, stenosis distal end, vessel centerline, cross section of stenosis proximal end and/or stenosis
  • the parameters such as the cross section of the distal end are marked, and the coronary artery model obtaining module can obtain the corresponding lesion parameters according to the marking result of the user.
  • the user interaction module is connected to the medical image acquisition module for displaying the medical image.
  • the user inputs corresponding model generation instructions using the tools provided by the user interaction module.
  • the coronary artery model generation module segments the medical image according to the model generation instruction input by the user, so as to obtain the first coronary artery model.
  • the user can input model generation instructions by using tools such as a brush, an eraser, etc. provided by the user interaction module through an input device such as a mouse (for example, by using a mouse to click on the corresponding tool icon, and dragging, clicking, or box-selecting, etc. to segment the medical image, thereby segmenting coronary vessels from the medical image to obtain the first coronary artery model.
  • an input device such as a mouse can be used to input model editing instructions by using tools such as a brush, an eraser and the like provided by the user interaction module (for example, by using a mouse Click the corresponding tool icon, and input the model editing instruction by dragging, clicking, or box selection) to adjust the boundary of the first coronary artery model.
  • tools such as a brush, an eraser and the like provided by the user interaction module (for example, by using a mouse Click the corresponding tool icon, and input the model editing instruction by dragging, clicking, or box selection) to adjust the boundary of the first coronary artery model.
  • the user interaction module (for example, by using a mouse to click the corresponding tool icon, and input the model generation instruction by dragging, clicking or box selection) to modify the blood vessel centerline, blood vessel cross-section and/or blood vessel wall of at least one stenosis position in the medical image , so as to restore the blood vessel centerline, blood vessel cross-section and/or blood vessel wall of the at least one stenosis position to a normal state, so as to repair the lesion at the at least one stenosis position; after the repair is completed, the user can An input device such as a mouse, using tools such as brushes and erasers provided by the user interaction module to continue to input the model generation instructions to segment the medical image, thereby segmenting coronary vessels from the medical image, The second coronary artery model is obtained.
  • An input device such as a mouse
  • the user interaction module is connected to the coronary artery model obtaining module, and is configured to display the second coronary artery model.
  • the second coronary artery model is automatically acquired by the coronary artery model acquiring module using an algorithm.
  • the user inputs corresponding model editing instructions using the tool provided by the user interaction module.
  • the coronary artery model generation module edits the second coronary artery model according to the model editing instruction input by the user.
  • the user interaction module is further configured to receive an automatic repair instruction input by the user, so as to trigger the coronary artery model acquisition module to perform the first coronary artery model and/or the medical image.
  • Automatic repair For example, when the user inputs the automatic repair instruction by clicking a certain stenosis position C with the mouse, the simulated repair module starts to repair the stenotic lesion at the stenosis position C according to the automatic repair instruction.
  • the present invention also provides a method for obtaining fractional coronary blood flow reserve.
  • the score acquisition system is implemented.
  • the method for obtaining fractional coronary blood flow reserve includes:
  • the first coronary artery model refers to the actual coronary artery model of the patient
  • the second coronary artery model refers to the Coronary artery model obtained after at least one lesion of the coronary artery of the patient is repaired.
  • S63 Obtain the coronary blood flow reserve fraction of the patient according to the first coronary artery model and the second coronary artery model.
  • the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, realizes the coronary artery shown in FIG. 6 .
  • Methods of obtaining fractional blood flow reserve are described in detail below.
  • the protection scope of the coronary blood flow fraction calculation method of the present invention is not limited to the execution sequence of the steps listed in this embodiment, and any solution implemented by adding or subtracting steps and replacing steps in the prior art according to the principles of the present invention All are included in the protection scope of the present invention.
  • the present invention also provides a system for obtaining fractional coronary blood flow reserve, which can realize the method for obtaining fractional coronary blood flow reserve of the present invention, but the
  • the devices for realizing the fractional flow reserve acquisition method include, but are not limited to, the structures of the coronary blood flow fractional fractional acquisition system listed in this embodiment. within the scope of protection of the invention.
  • the system for obtaining fractional coronary blood flow reserve of the present invention can obtain the patient's first coronary model according to the patient's medical image, and obtain the patient's second coronary model according to the patient's medical image or the first coronary model.
  • the first coronary artery model refers to the actual coronary artery model of the patient
  • the second coronary artery model refers to the coronary artery model obtained after repairing at least one lesion of the coronary artery of the patient.
  • the maximum blood flow Q S that can be obtained in the myocardial region supplied by the blood vessel under the condition of coronary stenosis and lesions can be obtained, and according to the second coronary artery model, the theoretical normal condition of the same region can be obtained.
  • the maximum blood flow Q N that can be obtained, the coronary blood flow reserve fraction of the patient can be directly obtained based on Q S and Q N . Therefore, the system for obtaining the fractional coronary blood flow reserve of the present invention does not adopt the method for obtaining the fractional coronary blood flow reserve. to replace Therefore, no additional errors will be introduced.
  • the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Biophysics (AREA)
  • Surgery (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Animal Behavior & Ethology (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Veterinary Medicine (AREA)
  • Computational Linguistics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Robotics (AREA)
  • Hematology (AREA)
  • Cardiology (AREA)
  • Physiology (AREA)
  • Pathology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Apparatus For Radiation Diagnosis (AREA)

Abstract

A coronary fractional flow reserve obtaining system and method, and a medium. The coronary fractional flow reserve obtaining system (1) comprises: a medical image obtaining module (11) used for obtaining a medical image of a patient, the medical image comprising a heart region of a patient; a coronary artery model obtaining module (12) connected to the medical image obtaining module (11) and used for obtaining a first coronary artery model and a second coronary artery model of the patient according to the medical image, wherein the first coronary artery model refers to an actual coronary artery model of the patient, and the second coronary artery model refers to a coronary artery model obtained after repairing at least one lesion of a coronary vessel of the patient; and a fractional flow reserve obtaining module (13) connected to the coronary artery model obtaining module (12) and used for obtaining the coronary fractional flow reserve of the patient according to the first coronary artery model and the second coronary artery model. Compared with the prior art, the coronary fractional flow reserve obtained by the coronary fractional flow reserve obtaining system (1) has higher accuracy.

Description

一种冠脉血流储备分数获取系统、方法及介质A system, method and medium for obtaining fractional coronary blood flow reserve 技术领域technical field
本发明属于医学图像处理领域,涉及一种冠脉血流储备分数获取系统,特别是涉及一种利用计算机图像处理技术对心脏冠脉部位的医学影像进行处理,以获取冠脉血流动力学指标的系统、方法及介质。The invention belongs to the field of medical image processing, and relates to a system for obtaining coronary blood flow reserve fraction, in particular to a method for processing medical images of coronary parts of the heart by using computer image processing technology to obtain coronary blood flow dynamic indexes system, method and medium.
背景技术Background technique
心肌缺血通常是由冠状动脉(冠脉)狭窄病变所导致,严重时会危及患者的生命。冠脉血流储备分数(Fractional Flow Reserve,FFR)是衡量人体冠状动脉供血状况的一个有效指标。冠脉血流储备分数是指冠脉存在狭窄病变的情况下、血管所供心肌区域能够获得的最大血流量Q S,与同一区域理论上正常情况下所能获得的最大血流量Q N之比,即:
Figure PCTCN2021137375-appb-000001
现有技术中获取FFR的方式可以分为有创方式和无创方式。
Myocardial ischemia is usually caused by coronary artery (coronary artery) stenosis, which can be life-threatening in severe cases. Fractional Flow Reserve (FFR) is an effective indicator to measure the coronary blood supply in humans. Fractional coronary flow reserve refers to the ratio of the maximum blood flow Q S that can be obtained in the myocardial area supplied by the blood vessel under the condition of coronary stenosis and the maximum blood flow Q N that can theoretically be obtained in the same area under normal conditions. ,which is:
Figure PCTCN2021137375-appb-000001
The methods of obtaining FFR in the prior art can be divided into invasive methods and non-invasive methods.
采用有创方式获取FFR的方法,是指在医学影像的引导下,将压力导丝侵入性地置入冠状动脉血管中,分别测量血管狭窄远端的压力和狭窄近端的压力,计算得到FFR指标。有创的测量方式对患者损伤较大,限制了FFR指标的临床应用。The method of obtaining FFR by an invasive method refers to invasively inserting a pressure guide wire into a coronary artery under the guidance of medical images, measuring the pressure at the distal end of the stenosis and the pressure at the proximal end of the stenosis, and calculating the FFR index. Invasive measurement methods cause great damage to patients, which limits the clinical application of FFR indicators.
采用无创方式获取FFR的方法,则是根据患者的医学影像来获取FFR指标。在实际应用中,Q S和Q N均为难以获得的指标,临床上通常认为
Figure PCTCN2021137375-appb-000002
因此,实际临床中往往利用
Figure PCTCN2021137375-appb-000003
代替
Figure PCTCN2021137375-appb-000004
来计算FFR,此时,获取FFR的公式为
Figure PCTCN2021137375-appb-000005
即:心肌最大充血状态下的狭窄远端冠脉平均压力
Figure PCTCN2021137375-appb-000006
与冠脉口部主动脉平均压力
Figure PCTCN2021137375-appb-000007
的比值。然而,发明人在实际应用中发现,
Figure PCTCN2021137375-appb-000008
Figure PCTCN2021137375-appb-000009
之间往往存在差异,利用
Figure PCTCN2021137375-appb-000010
代替
Figure PCTCN2021137375-appb-000011
来计算FFR会引入额外的误差,该误差会影响FFR的准确度。特别地,当冠脉的狭窄位置距离主动脉较远时,该误差也较大,此时获取的FFR的准确度较低。
The method of obtaining FFR by non-invasive method is to obtain the FFR index according to the medical image of the patient. In practical applications, both Q S and Q N are indicators that are difficult to obtain.
Figure PCTCN2021137375-appb-000002
Therefore, in practice, it is often used
Figure PCTCN2021137375-appb-000003
replace
Figure PCTCN2021137375-appb-000004
To calculate FFR, at this time, the formula to obtain FFR is
Figure PCTCN2021137375-appb-000005
That is: the mean coronary pressure distal to the stenosis in the state of maximal myocardial hyperemia
Figure PCTCN2021137375-appb-000006
Coronary Oral Aortic Mean Pressure
Figure PCTCN2021137375-appb-000007
ratio. However, the inventors found in practical applications,
Figure PCTCN2021137375-appb-000008
and
Figure PCTCN2021137375-appb-000009
There are often differences between the
Figure PCTCN2021137375-appb-000010
replace
Figure PCTCN2021137375-appb-000011
to calculate the FFR introduces additional error that affects the accuracy of the FFR. In particular, when the stenosis position of the coronary artery is far from the aorta, the error is also larger, and the accuracy of the obtained FFR is lower at this time.
发明内容SUMMARY OF THE INVENTION
鉴于以上所述现有技术的缺点,本发明的目的在于提供一种冠脉血流储备分数获取系统、 方法及介质,用于解决现有技术中获取的FFR准确度较低的问题。In view of the above shortcomings of the prior art, the purpose of the present invention is to provide a system, method and medium for obtaining fractional coronary blood flow reserve, which are used to solve the problem of low accuracy of the obtained FFR in the prior art.
为实现上述目的及其他相关目的,本发明的第一方面提供一种冠脉血流储备分数获取系统,所述冠脉血流储备分数获取系统包括:医学图像获取模块,用于获取患者的医学图像,所述医学图像包括患者的心脏区域;冠脉模型获取模块,与所述医学图像获取模块相连,用于根据所述医学图像获取患者的第一冠脉模型和第二冠脉模型;其中,所述第一冠脉模型是指患者的实际冠脉模型,所述第二冠脉模型是指对患者冠脉血管的至少一处病变进行修复以后获取的冠脉模型;血流储备分数获取模块,与所述冠脉模型获取模块相连,用于根据所述第一冠脉模型和所述第二冠脉模型获取患者的冠脉血流储备分数。In order to achieve the above object and other related objects, a first aspect of the present invention provides a system for obtaining fractional coronary blood flow reserve, the system for obtaining fractional coronary blood flow reserve includes: a medical image obtaining module for obtaining the medical image of a patient. an image, the medical image includes the heart region of the patient; a coronary artery model acquisition module, connected to the medical image acquisition module, is used to acquire the first coronary artery model and the second coronary artery model of the patient according to the medical image; wherein , the first coronary artery model refers to the actual coronary artery model of the patient, and the second coronary artery model refers to the coronary artery model obtained after repairing at least one lesion of the coronary artery of the patient; the blood flow reserve fraction is obtained The module is connected to the coronary artery model acquisition module, and is used for acquiring the coronary blood flow reserve fraction of the patient according to the first coronary artery model and the second coronary artery model.
于所述第一方面的一实施例中,所述冠脉模型获取模块包括第一冠脉模型获取子模块和第二冠脉模型获取子模块;所述第一冠脉模型获取子模块与所述医学图像获取模块相连,用于对所述医学图像进行分割以获取所述第一冠脉模型;所述第二冠脉模型获取子模块与所述第一冠脉模型获取子模块相连,用于对所述第一冠脉模型中患者冠脉血管的至少一处病变进行模拟修复,以获取所述第二冠脉模型;或者,所述第二冠脉模型获取子模块与所述医学图像获取模块相连,用于对所述医学图像中患者冠脉血管的至少一处病变进行模拟修复,并对模拟修复以后的医学图像进行分割以获取所述第二冠脉模型。In an embodiment of the first aspect, the coronary artery model acquisition module includes a first coronary artery model acquisition sub-module and a second coronary artery model acquisition sub-module; the first coronary artery model acquisition sub-module and the The medical image acquisition module is connected to the medical image to obtain the first coronary artery model by segmenting the medical image; the second coronary artery model acquisition sub-module is connected to the first coronary artery model acquisition sub-module, using performing simulated repair on at least one lesion of the patient's coronary vessel in the first coronary model to obtain the second coronary model; or, the second coronary model obtaining sub-module and the medical image The acquisition module is connected to the medical image for simulating repair of at least one lesion of the coronary blood vessel of the patient, and segmenting the medical image after the simulating repair to obtain the second coronary artery model.
于所述第一方面的一实施例中,所述第二冠脉模型获取子模块包括:第一病变参数获取单元,与所述第一冠脉模型获取子模块相连,用于获取所述第一冠脉模型中与冠脉狭窄相关的病变参数;第一模拟修复单元,与所述第一病变参数获取单元相连,用于根据所述病变参数对所述第一冠脉模型的狭窄处进行修改以获取所述第二冠脉模型。In an embodiment of the first aspect, the second coronary artery model acquisition sub-module includes: a first lesion parameter acquisition unit connected to the first coronary artery model acquisition sub-module for acquiring the first coronary artery model acquisition sub-module. a lesion parameter related to coronary artery stenosis in a coronary artery model; a first simulated repair unit, connected to the first lesion parameter acquisition unit, and configured to perform an analysis on the stenosis of the first coronary artery model according to the lesion parameter Modify to obtain the second coronary model.
于所述第一方面的一实施例中,所述第二冠脉模型获取子模块包括:第二病变参数获取单元,与所述图像获取模块相连,用于获取所述医学图像中与冠脉狭窄相关的病变参数;第二模拟修复单元,与所述第二病变参数获取单元相连,用于根据所述病变参数对所述医学图像中患者冠脉血管的狭窄处进行修改,以获取所述模拟修复以后的医学图像;图像分割单元,与所述第二模拟修复单元相连,用于对所述模拟修复以后的医学图像进行分割,以获取所述第二冠脉模型。In an embodiment of the first aspect, the second coronary artery model acquisition sub-module includes: a second lesion parameter acquisition unit, which is connected to the image acquisition module and is used to acquire the coronary artery in the medical image. stenosis-related lesion parameters; a second simulated repair unit, connected to the second lesion parameter acquisition unit, configured to modify the stenosis of the patient's coronary blood vessels in the medical image according to the lesion parameters, so as to obtain the The medical image after simulating restoration; the image segmentation unit is connected to the second simulating restoration unit, and is used for segmenting the medical image after simulating restoration, so as to obtain the second coronary artery model.
于所述第一方面的一实施例中,所述冠脉模型获取模块包括第三冠脉模型获取子模块和第四冠脉模型获取子模块;所述第三冠脉模型获取子模块与所述医学图像获取模块相连,用于对所述医学图像进行分割以获取所述第一冠脉模型;所述第四冠脉模型获取子模块与所述第三冠脉模型获取子模块相连,用于对所述第一冠脉模型中患者冠脉血管的至少一处病变进行虚拟治疗,以获取所述第二冠脉模型;或者所述第四冠脉模型获取子模块与所述医学图像 获取模块相连,用于对所述医学图像中患者冠脉血管的至少一处病变进行虚拟治疗,并对虚拟治疗以后的医学图像进行分割以获取所述第二冠脉模型。In an embodiment of the first aspect, the coronary artery model acquisition module includes a third coronary artery model acquisition sub-module and a fourth coronary artery model acquisition sub-module; the third coronary artery model acquisition sub-module and the The medical image acquisition module is connected to the medical image to obtain the first coronary artery model by segmenting the medical image; the fourth coronary artery model acquisition sub-module is connected to the third coronary artery model acquisition sub-module, using performing virtual treatment on at least one lesion of the coronary vessel of the patient in the first coronary model to obtain the second coronary model; or the fourth coronary model obtaining sub-module and the medical image obtaining The modules are connected, and are used for performing virtual treatment on at least one lesion of the coronary blood vessel of the patient in the medical image, and segmenting the medical image after the virtual treatment to obtain the second coronary artery model.
于所述第一方面的一实施例中,所述血流储备分数获取模块包括:实际血流量获取单元,与所述冠脉模型获取模块相连,用于根据所述第一冠脉模型获取一目标供血区域的实际最大血流量;理想血流量获取单元,与所述冠脉模型获取模块相连,用于根据所述第二冠脉模型获取所述目标供血区域的理想最大血流量;储备分数获取单元,与所述实际血流量获取单元和所述理想血流量获取单元相连,用于根据所述目标供血区域的实际最大血流量和理想最大血流量获取所述冠脉血流储备分数。In an embodiment of the first aspect, the blood flow reserve fraction acquisition module includes: an actual blood flow acquisition unit, connected to the coronary artery model acquisition module, for acquiring a the actual maximum blood flow of the target blood supply area; the ideal blood flow acquisition unit, connected to the coronary artery model acquisition module, is used to acquire the ideal maximum blood flow of the target blood supply area according to the second coronary artery model; reserve fraction acquisition a unit, connected to the actual blood flow obtaining unit and the ideal blood flow obtaining unit, and configured to obtain the coronary blood flow reserve fraction according to the actual maximum blood flow and the ideal maximum blood flow of the target blood supply area.
于所述第一方面的一实施例中,所述血流储备分数获取模块包括:实际压力获取单元,与所述冠脉模型获取模块相连,用于根据所述第一冠脉模型获取心肌最大充血状态下一目标位置的实际平均压力,其中,所述目标位置是指冠脉的狭窄远端;理想压力获取单元,与所述冠脉模型获取模块相连,用于根据所述第二冠脉模型获取心肌最大充血状态下所述目标位置的理想平均压力;储备分数获取单元,与所述实际压力获取单元和所述理想压力获取单元相连,用于根据所述目标位置的实际平均压力和理想平均压力获取所述冠脉血流储备分数。In an embodiment of the first aspect, the blood flow reserve fraction acquisition module includes: an actual pressure acquisition unit, which is connected to the coronary artery model acquisition module, and is used for acquiring myocardial maximum value according to the first coronary artery model. The actual average pressure of the target position in the hyperemia state, wherein the target position refers to the distal end of coronary artery stenosis; the ideal pressure acquisition unit is connected to the coronary artery model acquisition module, and is used for obtaining according to the second coronary artery The model obtains the ideal average pressure of the target position under the state of maximum myocardial hyperemia; the reserve fraction obtaining unit is connected to the actual pressure obtaining unit and the ideal pressure obtaining unit, and is used for obtaining the actual average pressure and the ideal pressure according to the target position. The mean pressure was used to obtain the fractional coronary flow reserve.
于所述第一方面的一实施例中,所述冠脉血流储备分数获取系统还包括:用户交互模块,与所述医学图像获取模块和/或所述冠脉模型获取模块相连,用于显示所述医学图像、所述第一冠脉模型和/或所述第二冠脉模型,并用于获取用户输入的模型生成指令和/或模型编辑指令;所述冠脉模型获取模块根据所述模型生成指令获取所述第二冠脉模型,和/或所述冠脉模型获取模块根据所述模型编辑指令对所述第一冠脉模型和/或所述第二冠脉模型进行编辑。In an embodiment of the first aspect, the system for obtaining fractional coronary blood flow reserve further includes: a user interaction module, connected to the medical image obtaining module and/or the coronary model obtaining module, for Displaying the medical image, the first coronary artery model and/or the second coronary artery model, and being used to obtain model generation instructions and/or model editing instructions input by the user; the coronary artery model obtaining module is based on the The model generating instruction acquires the second coronary artery model, and/or the coronary artery model acquiring module edits the first coronary artery model and/or the second coronary artery model according to the model editing instruction.
本发明的第二方面提供一种冠脉血流储备分数获取方法,所述冠脉血流储备分数获取方法包括:获取患者的医学图像,所述医学图像包括患者的心脏区域;根据所述医学图像获取患者的第一冠脉模型和第二冠脉模型;其中,所述第一冠脉模型是指患者的实际冠脉模型,所述第二冠脉模型是指对患者冠脉血管的至少一处病变进行修复以后获取的冠脉模型;根据所述第一冠脉模型和所述第二冠脉模型获取患者的冠脉血流储备分数。A second aspect of the present invention provides a method for obtaining fractional coronary blood flow reserve, the method for obtaining fractional coronary blood flow reserve includes: obtaining a medical image of a patient, the medical image including a heart region of the patient; The first coronary artery model and the second coronary artery model of the patient are obtained from the images; wherein, the first coronary artery model refers to the actual coronary artery model of the patient, and the second coronary artery model refers to at least the coronary artery model of the patient. Coronary artery model obtained after a lesion is repaired; the coronary blood flow reserve fraction of the patient is obtained according to the first coronary artery model and the second coronary artery model.
本发明的第三方面提供一种计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器执行时实现本发明第二方面所述的冠脉血流储备分数获取方法。A third aspect of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the method for obtaining fractional coronary blood flow reserve described in the second aspect of the present invention.
如上所述,本发明所述冠脉血流储备分数获取系统、方法及介质的一个技术方案具有以下有益效果:As mentioned above, a technical solution of the system, method and medium for obtaining fractional coronary blood flow reserve according to the present invention has the following beneficial effects:
本发明所述冠脉血流储备分数获取系统能够根据患者的医学图像获取患者的第一冠脉模型,并根据所述患者的医学图像或者所述第一冠脉模型获取患者的第二冠脉模型。其中,所 述第一冠脉模型是指患者的实际冠脉模型,所述第二冠脉模型是指对患者冠脉血管的至少一处病变进行修复以后获取的冠脉模型。根据所述第一冠脉模型可以获取冠脉存在狭窄病变的情况下、血管所供心肌区域能够获得的最大血流量Q S,根据所述第二冠脉模型可以获取同一区域理论上正常情况下所能获得的最大血流量Q N,基于Q S和Q N即可直接获取患者的冠脉血流储备分数。因此,本发明所述冠脉血流储备分数获取系统在获取冠脉血流储备分数时并不采用
Figure PCTCN2021137375-appb-000012
来代替
Figure PCTCN2021137375-appb-000013
因而不会引入额外的误差,故,本发明所述冠脉血流储备分数获取系统获取的冠脉血流储备分数相较于现有技术具有更高的准确度,尤其是在冠脉的狭窄位置距离主动脉较远时,本发明所述冠脉血流储备分数获取系统的优势更加明显。
The system for obtaining fractional coronary blood flow reserve of the present invention can obtain the patient's first coronary model according to the patient's medical image, and obtain the patient's second coronary model according to the patient's medical image or the first coronary model. Model. Wherein, the first coronary artery model refers to the actual coronary artery model of the patient, and the second coronary artery model refers to the coronary artery model obtained after repairing at least one lesion of the coronary artery of the patient. According to the first coronary artery model, the maximum blood flow Q S that can be obtained in the myocardial region supplied by the blood vessel under the condition of coronary stenosis and lesions can be obtained, and according to the second coronary artery model, the theoretical normal condition of the same region can be obtained. The maximum blood flow Q N that can be obtained, the coronary blood flow reserve fraction of the patient can be directly obtained based on Q S and Q N . Therefore, the system for obtaining the fractional coronary blood flow reserve of the present invention does not adopt the method for obtaining the fractional coronary blood flow reserve.
Figure PCTCN2021137375-appb-000012
to replace
Figure PCTCN2021137375-appb-000013
Therefore, no additional errors will be introduced. Therefore, the fractional coronary blood flow reserve obtained by the system for obtaining the fractional coronary blood flow reserve of the present invention has higher accuracy than the prior art, especially in the case of coronary stenosis. When the location is far away from the aorta, the advantages of the system for obtaining fractional coronary blood flow reserve of the present invention are more obvious.
附图说明Description of drawings
图1显示本发明所述冠脉血流储备分数获取系统于一具体实施例中的结构示意图。FIG. 1 shows a schematic structural diagram of a system for obtaining fractional coronary blood flow reserve according to the present invention in a specific embodiment.
图2A显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中冠脉模型获取模块的结构示意图。2A is a schematic structural diagram of a coronary artery model acquisition module in a specific embodiment of the coronary blood flow fraction reserve acquisition system according to the present invention.
图2B显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中第二冠脉模型获取子模块的结构示意图。FIG. 2B is a schematic structural diagram of a second coronary artery model acquisition sub-module in an embodiment of the coronary blood flow fraction reserve acquisition system according to the present invention.
图2C显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中第一冠脉模型的部分血管示例图。FIG. 2C is a diagram showing an example of part of blood vessels of the first coronary artery model of the system for obtaining fractional coronary flow reserve according to the present invention.
图2D显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中第二冠脉模型的部分血管示例图。FIG. 2D is a diagram showing an example of part of blood vessels of the second coronary artery model of the system for obtaining fractional coronary flow reserve according to the present invention.
图3A显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中冠脉模型获取模块的结构示意图。FIG. 3A is a schematic structural diagram of a coronary artery model acquisition module in a specific embodiment of the coronary flow reserve fraction acquisition system according to the present invention.
图3B显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中第二冠脉模型获取子模块的结构示意图。3B is a schematic structural diagram of a second coronary artery model acquisition sub-module in a specific embodiment of the coronary blood flow fraction reserve acquisition system according to the present invention.
图4A显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中冠脉模型获取模块的结构示意图。FIG. 4A is a schematic structural diagram of a coronary artery model acquisition module in a specific embodiment of the coronary flow reserve fraction acquisition system according to the present invention.
图4B显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中冠脉模型获取模块的结构示意图。FIG. 4B is a schematic structural diagram of a coronary artery model acquisition module in a specific embodiment of the coronary blood flow fraction reserve acquisition system according to the present invention.
图5A显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中血流储备分数获取模块的结构示意图。FIG. 5A is a schematic structural diagram of a fractional blood flow reserve acquisition module in a specific embodiment of the coronary blood flow fraction acquisition system according to the present invention.
图5B显示为本发明所述冠脉血流储备分数获取系统于一具体实施例中血流储备分数获取模块的结构示意图。FIG. 5B is a schematic structural diagram of a fractional blood flow reserve acquisition module in a specific embodiment of the coronary blood flow reserve fraction acquisition system according to the present invention.
图6显示为本发明所述冠脉血流储备分数获取方法于一具体实施例中的流程图。FIG. 6 shows a flowchart of the method for obtaining fractional coronary flow reserve according to the present invention in a specific embodiment.
元件标号说明Component label description
1冠脉血流储备分数获取系统1 Coronary flow reserve fraction acquisition system
11医学图像获取模块11 Medical Image Acquisition Module
12冠脉模型获取模块12 coronary artery model acquisition module
121第一冠脉模型获取子模块121 First coronary artery model acquisition sub-module
122第二冠脉模型获取子模块122 Second coronary artery model acquisition sub-module
1221第一病变参数获取单元1221 The first lesion parameter acquisition unit
1222第一模拟修复单元1222 First Simulation Repair Unit
1223第二病变参数获取单元1223 Second lesion parameter acquisition unit
1224第二模拟修复单元1224 Second Simulation Repair Unit
1225图像分割单元1225 image segmentation unit
123第三冠脉模型获取子模块123 Third coronary artery model acquisition sub-module
124第四冠脉模型获取子模块124 Fourth coronary artery model acquisition sub-module
13血流储备分数获取模块13. Fractional flow reserve acquisition module
131实际血流量获取单元131 Actual blood flow acquisition unit
132理想血流量获取单元132 ideal blood flow acquisition unit
133储备分数获取单元133 Reserve Score Acquisition Unit
134实际压力获取单元134 Actual pressure acquisition unit
135理想压力获取单元135 ideal pressure acquisition unit
136储备分数获取单元136 Reserve Score Acquisition Unit
S61~S63步骤Steps from S61 to S63
具体实施方式Detailed ways
以下通过特定的具体实例说明本发明的实施方式,本领域技术人员可由本说明书所揭露的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用,本说明书中的各项细节也可以基于不同观点与应用,在没有背离本发明的精 神下进行各种修饰或改变。需说明的是,在不冲突的情况下,以下实施例及实施例中的特征可以相互组合。The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other under the condition of no conflict.
需要说明的是,以下实施例中所提供的图示仅以示意方式说明本发明的基本构想,图示中仅显示与本发明中有关的组件而非按照实际实施时的组件数目、形状及尺寸绘制,其实际实施时各组件的型态、数量及比例可为一种随意的改变,且其组件布局型态也可能更为复杂。此外,此外,在本文中,诸如“第一”、“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来,而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。It should be noted that the drawings provided in the following embodiments are only to illustrate the basic concept of the present invention in a schematic way, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in actual implementation. For drawing, the type, quantity and proportion of each component can be arbitrarily changed during actual implementation, and the layout of components may also be more complicated. Furthermore, herein, relational terms such as "first," "second," etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply those entities or that there is any such actual relationship or sequence between operations.
在实际应用中,Q S和Q N均为难以获得的指标,临床上通常认为
Figure PCTCN2021137375-appb-000014
因此,实际临床中往往利用
Figure PCTCN2021137375-appb-000015
代替
Figure PCTCN2021137375-appb-000016
来计算FFR,此时,获取FFR的公式为
Figure PCTCN2021137375-appb-000017
即:心肌最大充血状态下的狭窄远端冠脉平均压力
Figure PCTCN2021137375-appb-000018
与冠脉口部主动脉平均压力
Figure PCTCN2021137375-appb-000019
的比值。然而,发明人在实际应用中发现,利用
Figure PCTCN2021137375-appb-000020
代替
Figure PCTCN2021137375-appb-000021
来计算FFR依赖于三个假设,具体地:
Figure PCTCN2021137375-appb-000022
此时,假设R S=R N,得到
Figure PCTCN2021137375-appb-000023
接着假设
Figure PCTCN2021137375-appb-000024
得到
Figure PCTCN2021137375-appb-000025
最后假设
Figure PCTCN2021137375-appb-000026
得到
Figure PCTCN2021137375-appb-000027
在以上公式中,Q表示最大血流量,P表示压力,R表示冠脉微循环阻力,下标S表示冠脉存在狭窄的实际情况,下标N表示冠脉不存在狭窄的理想情况,上标a表示狭窄位置的近端(通常为冠脉口部主动脉位置),上标d表示狭窄位置的远端,上标v表示静脉位置,例如,
Figure PCTCN2021137375-appb-000028
表示实际情况下静脉的压力,
Figure PCTCN2021137375-appb-000029
表示理想情况下冠脉的狭窄位置的远端的压力。由此可知,利用
Figure PCTCN2021137375-appb-000030
代替
Figure PCTCN2021137375-appb-000031
来计算FFR依赖于R S=R N
Figure PCTCN2021137375-appb-000032
以及
Figure PCTCN2021137375-appb-000033
这三个假设(等效于
Figure PCTCN2021137375-appb-000034
这一假设),然而,上述三个假设在实际应用中往往并不严格成立,尤其是
Figure PCTCN2021137375-appb-000035
这一假设。在冠脉的狭窄位置离主动脉较远时,假设
Figure PCTCN2021137375-appb-000036
会引入较大的误差,从而导致获取的FFR的准确度较低。
In practical applications, both Q S and Q N are indicators that are difficult to obtain.
Figure PCTCN2021137375-appb-000014
Therefore, in practice, it is often used
Figure PCTCN2021137375-appb-000015
replace
Figure PCTCN2021137375-appb-000016
To calculate FFR, at this time, the formula to obtain FFR is
Figure PCTCN2021137375-appb-000017
That is: the mean coronary pressure distal to the stenosis in the state of maximal myocardial hyperemia
Figure PCTCN2021137375-appb-000018
Coronary Oral Aortic Mean Pressure
Figure PCTCN2021137375-appb-000019
ratio. However, the inventors found in practical applications that using
Figure PCTCN2021137375-appb-000020
replace
Figure PCTCN2021137375-appb-000021
To calculate FFR relies on three assumptions, specifically:
Figure PCTCN2021137375-appb-000022
At this time, assuming R S =R N , we get
Figure PCTCN2021137375-appb-000023
Then suppose
Figure PCTCN2021137375-appb-000024
get
Figure PCTCN2021137375-appb-000025
final hypothesis
Figure PCTCN2021137375-appb-000026
get
Figure PCTCN2021137375-appb-000027
In the above formula, Q represents the maximum blood flow, P represents the pressure, R represents the coronary microcirculation resistance, the subscript S represents the actual situation of coronary stenosis, the subscript N represents the ideal situation of no coronary stenosis, and the superscript a indicates the proximal end of the stenosis (usually the aorta at the coronary ostium), the superscript d indicates the distal end of the stenosis, and the superscript v indicates the venous position, for example,
Figure PCTCN2021137375-appb-000028
represents the actual venous pressure,
Figure PCTCN2021137375-appb-000029
Indicates the pressure distal to the location of the coronary artery stenosis ideally. From this, it can be seen that using
Figure PCTCN2021137375-appb-000030
replace
Figure PCTCN2021137375-appb-000031
to calculate FFR depends on R S =R N ,
Figure PCTCN2021137375-appb-000032
as well as
Figure PCTCN2021137375-appb-000033
These three assumptions (equivalent to
Figure PCTCN2021137375-appb-000034
This assumption), however, the above three assumptions are often not strictly true in practical applications, especially
Figure PCTCN2021137375-appb-000035
this assumption. When the stenosis of the coronary artery is far from the aorta, it is assumed that
Figure PCTCN2021137375-appb-000036
Larger errors will be introduced, resulting in lower accuracy of the obtained FFR.
针对这一问题,本发明提供一种冠脉血流储备分数获取系统。所述冠脉血流储备分数获取系统能够根据患者的医学图像获取患者的第一冠脉模型,并根据所述患者的医学图像或者所述第一冠脉模型获取患者的第二冠脉模型。其中,所述第一冠脉模型是指患者的实际冠脉模型,所述第二冠脉模型是指对患者冠脉血管的至少一处病变进行修复以后获取的冠脉模型。根据所述第一冠脉模型可以获取冠脉存在狭窄病变的情况下、血管所供心肌区域能够获得的最大血流量Q S,根据所述第二冠脉模型可以获取同一区域理论上正常情况下所能获得的最大血流量Q N,基于Q S和Q N即可直接获取患者的冠脉血流储备分数。因此,本发明所述冠脉血流储备分数获取系统在获取冠脉血流储备分数时并不采用
Figure PCTCN2021137375-appb-000037
来代替
Figure PCTCN2021137375-appb-000038
因而不会引入额外的误差,故,本发明所述冠脉血流储备分数获取系统获取的冠脉血流储备分数相较于现有技术具有更高的准确度,尤其是在冠脉的狭窄位置距离主动脉较远时,本发明所述冠脉血流储备分数获取系统的优势更加明显。
In response to this problem, the present invention provides a system for obtaining fractional coronary blood flow reserve. The coronary blood flow reserve fraction acquisition system can acquire the patient's first coronary artery model according to the patient's medical image, and acquire the patient's second coronary artery model according to the patient's medical image or the first coronary artery model. Wherein, the first coronary artery model refers to the actual coronary artery model of the patient, and the second coronary artery model refers to the coronary artery model obtained after repairing at least one lesion of the coronary artery of the patient. According to the first coronary artery model, the maximum blood flow Q S that can be obtained in the myocardial region supplied by the blood vessel under the condition of coronary stenosis and lesions can be obtained, and according to the second coronary artery model, the theoretical normal condition of the same region can be obtained. The maximum blood flow Q N that can be obtained, the coronary blood flow reserve fraction of the patient can be directly obtained based on Q S and Q N . Therefore, the system for obtaining the fractional coronary blood flow reserve of the present invention does not adopt the method for obtaining the fractional coronary blood flow reserve.
Figure PCTCN2021137375-appb-000037
to replace
Figure PCTCN2021137375-appb-000038
Therefore, no additional errors will be introduced. Therefore, the fractional coronary blood flow reserve obtained by the system for obtaining the fractional coronary blood flow reserve of the present invention has higher accuracy than the prior art, especially in the case of coronary stenosis. When the location is far away from the aorta, the advantages of the system for obtaining fractional coronary blood flow reserve of the present invention are more obvious.
请参阅图1,于本发明的一实施例中,所述冠脉血流储备分数获取系统1包括医学图像获取模块11、冠脉模型获取模块12和血流储备分数获取模块13。Referring to FIG. 1 , in an embodiment of the present invention, the system 1 for acquiring fractional coronary blood flow reserve includes a medical image acquiring module 11 , a coronary artery model acquiring module 12 and a fractional blood flow reserve acquiring module 13 .
所述医学图像获取模块11用于获取患者的医学图像,所述患者的医学图像包括患者的心脏区域。所述医学图像优选为CT血管造影(CTA,CT angiography)图像,此外,所述医学图像也可以是CT灌注图像、平扫CT图像、DSA血管造影图像,或者是采用诸如X射线、核磁共振、超声、PET、SPECT等扫描成像方式获得的心脏部位的医学图像,还可以是血管腔内影像(如光学相干成像、血管内超声)等。The medical image acquisition module 11 is used for acquiring a medical image of a patient, and the medical image of the patient includes a heart region of the patient. The medical image is preferably a CT angiography (CTA, CT angiography) image, in addition, the medical image can also be a CT perfusion image, a plain CT image, a DSA angiography image, or an image obtained by using methods such as X-ray, nuclear magnetic resonance, The medical images of the heart part obtained by scanning imaging methods such as ultrasound, PET, SPECT, etc., may also be intravascular images (eg, optical coherence imaging, intravascular ultrasound), and the like.
所述冠脉模型获取模块12与所述医学图像获取模块11相连,用于根据所述患者的医学图像获取患者的第一冠脉模型和第二冠脉模型,其中,所述第一冠脉模型为患者的实际冠脉模型,所述第一冠脉模型包含患者的至少一处狭窄病变。所述第二冠脉模型是指对患者冠脉血管的至少一处病变进行修复以后获取的冠脉模型。所述冠脉模型获取模块12可以根据所述第一冠脉模型获取所述第二冠脉模型,也可以根据所述患者的医学图像获取所述第二冠脉模型。在具体应用中,所述冠脉模型获取模块12可以采用算法自动修复、用户手动修复或二者相结合的方式对所述患者冠脉血管的至少一处病变进行修复。The coronary artery model acquisition module 12 is connected with the medical image acquisition module 11, and is used to acquire the patient's first coronary artery model and the second coronary artery model according to the medical image of the patient, wherein the first coronary artery model The model is an actual coronary artery model of the patient, and the first coronary artery model contains at least one stenotic lesion of the patient. The second coronary artery model refers to a coronary artery model obtained after repairing at least one lesion of a patient's coronary artery. The coronary artery model acquisition module 12 may acquire the second coronary artery model according to the first coronary artery model, and may also acquire the second coronary artery model according to the medical image of the patient. In a specific application, the coronary artery model obtaining module 12 may repair at least one lesion of the patient's coronary vessels by using automatic algorithm repair, manual repair by a user, or a combination of the two.
所述血流储备分数获取模块13与所述冠脉模型获取模块12相连,用于根据所述第一冠脉模型和所述第二冠脉模型获取患者的冠脉血流储备分数。The fractional blood flow reserve acquisition module 13 is connected to the coronary artery model acquisition module 12, and is configured to acquire the coronary blood flow reserve fraction of the patient according to the first coronary artery model and the second coronary artery model.
根据以上描述可知,本实施例所述冠脉血流储备分数获取系统根据所述第一冠脉模型和 所述第二冠脉模型获取患者的冠脉血流储备分数,而非采用
Figure PCTCN2021137375-appb-000039
来代替
Figure PCTCN2021137375-appb-000040
以获取患者的冠脉血流储备分数,因而不会引入额外的误差。故,本实施例中获取的冠脉血流储备分数相对于现有技术具有较高的准确度,尤其是在冠脉的狭窄位置距离主动脉较远时,本实施例所述冠脉血流储备分数获取系统的优势更加明显。
It can be seen from the above description that the system for obtaining fractional coronary blood flow reserve according to this embodiment obtains the fractional coronary blood flow reserve of the patient according to the first coronary model and the second coronary model, instead of using
Figure PCTCN2021137375-appb-000039
to replace
Figure PCTCN2021137375-appb-000040
to obtain the patient's coronary flow reserve fraction without introducing additional errors. Therefore, the fractional coronary blood flow reserve obtained in this embodiment has a higher accuracy than the prior art, especially when the stenosis position of the coronary artery is far away from the aorta, the coronary blood flow described in this embodiment is more accurate. The advantage of the reserve score acquisition system is even more obvious.
于本发明的一实施例中,所述患者的医学图像为3D图像,所述3D图像例如为采用CT、核磁共振等成像方式获得的包含三维体素信息的图像,或者是根据多幅不同角度的二维图像(如DSA)进行处理计算后得到的三维图像。本实施例中,所述第一冠脉模型和所述第二冠脉模型均为冠脉的三维几何模型。In an embodiment of the present invention, the medical image of the patient is a 3D image. The three-dimensional image obtained after processing and calculating the two-dimensional image (such as DSA). In this embodiment, the first coronary artery model and the second coronary artery model are both three-dimensional geometric models of the coronary artery.
于本发明的一实施例中,所述冠脉模型获取模块包括第一冠脉模型获取子模块和第二冠脉模型获取子模块。In an embodiment of the present invention, the coronary artery model acquisition module includes a first coronary artery model acquisition sub-module and a second coronary artery model acquisition sub-module.
可选地,请参阅图2A,所述第一冠脉模型获取子模块121与所述医学图像获取模块11相连,用于对所述医学图像进行分割以获取所述第一冠脉模型。所述第二冠脉模型获取子模块122与所述第一冠脉模型获取子模块121相连,用于对所述第一冠脉模型中患者冠脉血管的至少一处病变进行模拟修复,以获取所述第二冠脉模型。Optionally, please refer to FIG. 2A , the first coronary artery model acquisition sub-module 121 is connected to the medical image acquisition module 11, and is used for segmenting the medical image to acquire the first coronary artery model. The second coronary artery model acquisition sub-module 122 is connected to the first coronary artery model acquisition sub-module 121, and is used to simulate and repair at least one lesion of the coronary artery of the patient in the first coronary artery model, to Obtain the second coronary artery model.
所述第一冠脉模型获取子模块121可以采用一基于神经网络的图像分割模型(例如U-Net、V-Net等)对所述医学图像进行分割以获取所述第一冠脉模型。具体地,所述第一冠脉模型获取子模块121将所述医学图像输入所述图像分割模型,根据所述图像分割模型的输出即可获取所述第一冠脉模型。The first coronary artery model obtaining sub-module 121 may use a neural network-based image segmentation model (eg, U-Net, V-Net, etc.) to segment the medical image to obtain the first coronary artery model. Specifically, the first coronary artery model obtaining sub-module 121 inputs the medical image into the image segmentation model, and the first coronary artery model can be obtained according to the output of the image segmentation model.
所述第一冠脉模型获取子模块121也可以采用阈值法对所述医学图像进行分割以获取所述第一冠脉模型。具体地,所述第一冠脉模型获取子模块121获取冠脉血管的灰度值范围,并从所述医学图像中获取所有位于该灰度值范围内的体素点(或像素点),这些体素点(或像素点)组成的集合即为所述第一冠脉模型。The first coronary artery model obtaining sub-module 121 may also use a threshold method to segment the medical image to obtain the first coronary artery model. Specifically, the first coronary artery model obtaining sub-module 121 obtains the gray value range of coronary blood vessels, and obtains all voxels (or pixel points) located within the gray value range from the medical image, The set composed of these voxel points (or pixel points) is the first coronary artery model.
请参阅图2B,所述第二冠脉模型获取子模块122的一种实现结构包括第一病变参数获取单元1221和第一模拟修复单元1222。Referring to FIG. 2B , an implementation structure of the second coronary artery model obtaining sub-module 122 includes a first lesion parameter obtaining unit 1221 and a first simulated repairing unit 1222 .
所述第一病变参数获取单元1221与所述第一冠脉模型获取子模块121相连,用于获取所述第一冠脉模型中与冠脉狭窄相关的病变参数。其中,所述病变参数例如为冠脉的狭窄位置、所述狭窄位置的血管中心线、狭窄近端的横截面和狭窄远端的横截面(或者,狭窄近端的血管直径和狭窄远端的血管直径)。在具体应用中,所述第一病变参数获取单元1221可以采用狭窄检出算法来获取所述第一冠脉模型中的至少一处狭窄位置,采用现有的几何方法来获取 所述狭窄位置的血管中心线、狭窄近端的横截面和狭窄远端的横截面(或者,狭窄近端的血管直径和狭窄远端的血管直径)。The first lesion parameter acquisition unit 1221 is connected to the first coronary artery model acquisition sub-module 121, and is configured to acquire lesion parameters related to coronary stenosis in the first coronary artery model. Wherein, the lesion parameters are, for example, the stenosis position of the coronary artery, the blood vessel centerline of the stenosis position, the cross-section of the proximal stenosis and the cross-section of the distal end of the stenosis (or, the diameter of the blood vessel at the proximal end of the stenosis and the diameter of the distal end of the stenosis). vessel diameter). In a specific application, the first lesion parameter obtaining unit 1221 may use a stenosis detection algorithm to obtain at least one stenosis position in the first coronary artery model, and use an existing geometric method to obtain the stenosis position Vessel centerline, cross-section proximal to the stenosis, and cross-section distal to the stenosis (or, vessel diameter proximal to the stenosis and vessel diameter distal to the stenosis).
所述狭窄检出算法的一种实现方式为:根据分割得到的冠脉血管及其中心线,沿中心线计算血管不同位置的直径或横截面,并选取直径或横截面小于阈值的位置作为冠脉的狭窄位置。An implementation manner of the stenosis detection algorithm is: according to the coronary vessels obtained by segmentation and their centerlines, calculate the diameters or cross-sections of different positions of the blood vessels along the centerline, and select the positions with diameters or cross-sections smaller than a threshold value as the coronary arteries. stenosis of the arteries.
所述狭窄检出算法的另一种实现方式为:利用一AI狭窄检出模型对患者的第一冠脉模型进行处理,以获取冠脉的狭窄位置。其中,所述AI狭窄检出模型为一训练好的深度学习网络模型,其训练数据包括多幅标注有狭窄位置的冠脉图像,具体应用中,可以通过人工方式标注出冠脉图像的狭窄位置;对所述AI狭窄检出模型的训练可以采用现有训练方式实现,此处不做过多赘述。Another implementation manner of the stenosis detection algorithm is: using an AI stenosis detection model to process the patient's first coronary artery model to obtain the coronary stenosis position. The AI stenosis detection model is a trained deep learning network model, and its training data includes multiple coronary images marked with stenosis positions. In specific applications, the stenosis positions of coronary images can be manually marked ; The training of the AI stenosis detection model can be implemented by using an existing training method, which will not be repeated here.
所述第一模拟修复单元1222与所述第一病变参数获取单元1221相连,用于根据所述病变参数对所述第一冠脉模型的狭窄处进行修改,以获得所述狭窄位置在不出现狭窄病变时的理想血管,进而获取所述第二冠脉模型。The first simulated repair unit 1222 is connected to the first lesion parameter acquisition unit 1221, and is configured to modify the stenosis of the first coronary artery model according to the lesion parameters, so as to obtain the stenosis position when the stenosis does not appear. The ideal blood vessel in stenotic lesions is obtained, and then the second coronary artery model is obtained.
具体地,对于任一狭窄位置B,所述第一模拟修复单元1222可以将所述狭窄位置B的血管中心线作为对称轴、以所述狭窄近端的横截面和狭窄远端的横截面作为端面生成一特定形状的几何体,并利用所述特定形状的几何体替代该狭窄位置B的血管以获取所述第二冠脉模型,所述特定形状的几何体例如为圆柱、圆台等。或者,所述第一模拟修复单元1222可以将所述狭窄位置B的血管中心线作为对称轴,并根据狭窄近端的血管直径和狭窄远端的血管直径生成一特定形状的几何体,且利用所述特定形状的几何体替代该狭窄位置B的血管以获取所述第二冠脉模型。例如,请参阅图2C和图2D,其中,图2C显示为第一冠脉模型中某一狭窄位置的血管示例图,图2D显示为对图2C所示狭窄位置的血管进行修改的结果。Specifically, for any stenosis position B, the first simulated repair unit 1222 may take the blood vessel centerline of the stenosis position B as the axis of symmetry, and the cross-section of the proximal stenosis and the cross-section of the distal stenosis as the The end face generates a geometry of a specific shape, and uses the geometry of the specific shape to replace the blood vessel at the stenosis position B to obtain the second coronary artery model. Alternatively, the first simulated repairing unit 1222 can use the blood vessel centerline of the stenosis position B as the axis of symmetry, and generate a geometry with a specific shape according to the blood vessel diameter at the proximal end of the stenosis and the blood vessel diameter at the distal end of the stenosis, and use the The geometry of the specific shape replaces the blood vessel at the stenosis position B to obtain the second coronary artery model. For example, please refer to Fig. 2C and Fig. 2D, wherein Fig. 2C shows an example diagram of a vessel at a stenosis position in the first coronary artery model, and Fig. 2D shows the result of modifying the vessel at the stenosis position shown in Fig. 2C.
可选地,请参阅图3A,所述第一冠脉模型获取子模块121与所述医学图像获取模块11相连,用于对所述医学图像进行分割以获取所述第一冠脉模型。所述第二冠脉模型获取子模块122与所述医学图像获取模块11相连,用于对所述医学图像中患者冠脉血管的至少一处病变进行模拟修复,并对模拟修复以后的医学图像进行分割以获取所述第二冠脉模型。Optionally, please refer to FIG. 3A , the first coronary artery model obtaining sub-module 121 is connected to the medical image obtaining module 11 , and is used for segmenting the medical image to obtain the first coronary artery model. The second coronary artery model acquisition sub-module 122 is connected to the medical image acquisition module 11, and is used to simulate and repair at least one lesion of the patient's coronary vessels in the medical image, and to simulate and repair the medical image after the repair. Segmentation is performed to obtain the second coronary artery model.
请参阅图3B,所述第二冠脉模型获取子模块122的一种实现结构包括第二病变参数获取单元1223、第二模拟修复单元1224和图像分割单元1225。Referring to FIG. 3B , an implementation structure of the second coronary artery model acquisition sub-module 122 includes a second lesion parameter acquisition unit 1223 , a second simulated repair unit 1224 and an image segmentation unit 1225 .
所述第二病变参数获取单元1223与所述图像获取模块11相连,用于获取所述医学图像中与冠脉狭窄相关的病变参数。其中,所述病变参数例如为冠脉的狭窄位置、所述狭窄位置的血管中心线、狭窄近端的横截面和狭窄远端的横截面(或者,狭窄近端的血管直径和狭窄 远端的血管直径)。在具体应用中,所述第二病变参数获取单元1223可以采用狭窄检出算法来获取所述医学图像中的至少一处狭窄位置,采用现有的几何方法来获取所述狭窄位置的血管中心线、狭窄近端的横截面和狭窄远端的横截面(或者,狭窄近端的血管直径和狭窄远端的血管直径)。The second lesion parameter acquisition unit 1223 is connected to the image acquisition module 11, and is configured to acquire lesion parameters related to coronary stenosis in the medical image. Wherein, the lesion parameters are, for example, the stenosis position of the coronary artery, the blood vessel centerline of the stenosis position, the cross-section of the proximal stenosis and the cross-section of the distal end of the stenosis (or, the diameter of the blood vessel at the proximal end of the stenosis and the diameter of the distal end of the stenosis). vessel diameter). In a specific application, the second lesion parameter obtaining unit 1223 may use a stenosis detection algorithm to obtain at least one stenosis position in the medical image, and use an existing geometric method to obtain the blood vessel centerline of the stenosis position , a cross-section of the proximal end of the stenosis and a cross-section of the distal end of the stenosis (or, the vessel diameter at the proximal end of the stenosis and the vessel diameter at the distal end of the stenosis).
所述第二模拟修复单元1224与所述第二病变参数获取单元1223相连,用于根据所述病变参数对所述医学图像中患者冠脉血管的狭窄处进行修改,以获取所述模拟修复以后的医学图像。所述第二模拟修复单元1224对患者冠脉血管的狭窄处进行的修改与所述第一模拟单元1222类似,此处不做赘述。The second simulated repair unit 1224 is connected to the second lesion parameter acquisition unit 1223, and is configured to modify the stenosis of the patient's coronary blood vessels in the medical image according to the lesion parameters, so as to obtain the simulated repair after medical images. The modification performed by the second simulation repair unit 1224 on the stenosis of the coronary blood vessel of the patient is similar to that of the first simulation unit 1222 , and details are not described here.
所述图像分割单元1225与所述第二模拟修复单元1224相连,用于对所述模拟修复以后的医学图像进行分割,以获取所述第二冠脉模型。具体地,所述图像分割单元1225可以采用基于神经网络的图像分割模型或阈值法对所述模拟修复以后的医学图像进行分割。The image segmentation unit 1225 is connected to the second simulated restoration unit 1224, and is used for segmenting the medical image after the simulated restoration to obtain the second coronary artery model. Specifically, the image segmentation unit 1225 may use a neural network-based image segmentation model or a threshold method to segment the medical image after the simulated restoration.
根据以上描述可知,本实施例提供了采用算法自动修复患者冠脉血管病变来获取第二冠脉模型的方法,具体应用中,可以直接使用该第二冠脉模型来获取患者的冠脉血流储备分数,也可以在该第二冠脉模型的基础上采用人工方式进一步修正,以得到更精确的第二冠脉模型。As can be seen from the above description, this embodiment provides a method for automatically repairing a patient's coronary vascular disease by using an algorithm to obtain a second coronary model. In specific applications, the second coronary model can be directly used to obtain the coronary blood flow of the patient. The reserve fraction can also be further revised manually on the basis of the second coronary artery model to obtain a more accurate second coronary artery model.
于本发明的一实施例中,所述冠脉模型获取模块包括第三冠脉模型获取子模块和第四冠脉模型获取子模块。In an embodiment of the present invention, the coronary artery model acquisition module includes a third coronary artery model acquisition sub-module and a fourth coronary artery model acquisition sub-module.
可选地,请参阅图4A,所述第三冠脉模型获取子模块123与所述医学图像获取模块11相连,用于对所述医学图像进行分割以获取所述第一冠脉模型。所述第四冠脉模型获取子模块124与所述第三冠脉模型获取子模块123相连,用于对所述第一冠脉模型中患者冠脉血管的至少一处病变进行虚拟治疗,以获取所述第二冠脉模型。其中,对所述医学图像进行分割以获取所述第一冠脉模型的方式与所述第一冠脉模型获取子模块121类似,此处不做赘述。Optionally, referring to FIG. 4A , the third coronary artery model obtaining sub-module 123 is connected to the medical image obtaining module 11 , and is used for segmenting the medical image to obtain the first coronary artery model. The fourth coronary artery model acquisition sub-module 124 is connected to the third coronary artery model acquisition sub-module 123, and is used to perform virtual treatment on at least one lesion of the coronary artery of the patient in the first coronary artery model, so as to Obtain the second coronary artery model. The manner of segmenting the medical image to obtain the first coronary artery model is similar to that of the first coronary artery model obtaining sub-module 121 , and details are not described here.
在获取所述第一冠脉模型以后,所述第四冠脉模型获取子模块124采用狭窄检出算法获取所述第一冠脉模型中冠脉的狭窄位置,基于此,所述第四冠脉模型获取子模块124在患者冠脉血管的至少一处狭窄位置进行虚拟治疗,所述虚拟治疗的方法例如为虚拟植入支架技术或虚拟球囊扩张技术等,虚拟治疗完成以后得到的冠脉模型即为所述第二冠脉模型。After acquiring the first coronary artery model, the fourth coronary artery model acquisition sub-module 124 uses a stenosis detection algorithm to acquire the stenosis position of the coronary artery in the first coronary artery model. The arterial model obtaining sub-module 124 performs virtual treatment on at least one stenosis position of the coronary blood vessel of the patient. The virtual treatment method is, for example, a virtual stent implantation technique or a virtual balloon dilation technique. The coronary artery obtained after the virtual treatment is completed. The model is the second coronary artery model.
具体地,所述虚拟植入支架技术是指所述第四冠脉模型获取子模块124将一虚拟支架植入所述狭窄位置,以使所述狭窄位置的血管在所述虚拟支架的支撑作用下恢复正常状态,从而实现所述虚拟治疗。Specifically, the virtual stent implantation technology refers to that the fourth coronary artery model acquisition sub-module 124 implants a virtual stent into the stenosis position, so that the blood vessel in the stenosis position can be supported by the virtual stent to restore the normal state, so as to realize the virtual treatment.
所述虚拟球囊扩张技术是指所述第四冠脉模型获取子模块124将一虚拟球囊型植入物植入所述狭窄位置,所述虚拟球囊型植入物内部有一载荷,当所述虚拟球囊型植入物植入所述 血管三维模型后,其内部载荷会在外力作用下产生膨胀促使所述球囊型植入物产生塑性形变,进而支撑所述狭窄位置的血管向外扩张并最终恢复正常状态,从而实现所述虚拟治疗。其中,所述虚拟球囊型植入物中载荷膨胀的方法包括但不限于对其进行充气。The virtual balloon dilation technique means that the fourth coronary artery model acquisition sub-module 124 implants a virtual balloon implant into the stenosis position, and the virtual balloon implant has a load inside, and when After the virtual balloon implant is implanted into the three-dimensional model of the blood vessel, its internal load will expand under the action of external force, which will cause the balloon implant to produce plastic deformation, thereby supporting the blood vessel at the stenotic position. The virtual treatment is realized by external expansion and eventually return to normal state. Wherein, the method for load expansion in the virtual balloon implant includes, but is not limited to, inflating it.
可选地,请参阅图4B,所述第三冠脉模型获取子模块123与所述医学图像获取模块11相连,用于对所述医学图像进行分割以获取所述第一冠脉模型。所述第四冠脉模型获取子模块124与所述医学图像获取模块11相连,用于对所述医学图像中患者冠脉血管的至少一处病变进行虚拟治疗,并对虚拟治疗以后的医学图像进行分割以获取所述第二冠脉模型。Optionally, referring to FIG. 4B , the third coronary artery model obtaining sub-module 123 is connected to the medical image obtaining module 11 , and is used for segmenting the medical image to obtain the first coronary artery model. The fourth coronary artery model acquisition sub-module 124 is connected to the medical image acquisition module 11, and is used to perform virtual treatment on at least one lesion of the coronary artery of the patient in the medical image, and perform virtual treatment on the medical image after virtual treatment. Segmentation is performed to obtain the second coronary artery model.
请参阅图5A,于本发明的一实施例中,所述血流储备分数获取模块13包括实际血流量获取单元131、理想血流量获取单元132和储备分数获取单元133。Referring to FIG. 5A , in an embodiment of the present invention, the fractional blood flow reserve acquiring module 13 includes an actual blood flow acquiring unit 131 , an ideal blood flow acquiring unit 132 and a fractional reserve acquiring unit 133 .
所述实际血流量获取单元131与所述冠脉模型获取模块12相连,用于根据所述第一冠脉模型获取目标供血区域的实际最大血流量Q S,其中,所述实际血流量获取单元131获取Q S的方法包括但不限于流体动力学仿真、深度学习等。 The actual blood flow acquisition unit 131 is connected to the coronary artery model acquisition module 12, and is used for acquiring the actual maximum blood flow Q S of the target blood supply area according to the first coronary artery model, wherein the actual blood flow acquisition unit 131 Methods for obtaining Q S include, but are not limited to, fluid dynamics simulation, deep learning, and the like.
所述理想血流量获取单元132与所述冠脉模型获取模块12相连,用于根据所述第二冠脉模型获取所述目标供血区域的理想最大血流量Q N,其中,所述理想血流量获取单元132获取Q N的方法包括但不限于流体动力学仿真、深度学习等。 The ideal blood flow obtaining unit 132 is connected to the coronary artery model obtaining module 12, and is configured to obtain the ideal maximum blood flow Q N of the target blood supply region according to the second coronary artery model, wherein the ideal blood flow The method by which the obtaining unit 132 obtains Q N includes, but is not limited to, fluid dynamics simulation, deep learning, and the like.
所述储备分数获取单元133与所述实际血流量获取单元131和所述理想血流量获取单元132相连,用于根据所述目标供血区域的实际最大血流量和理想最大血流量获取所述冠脉血流储备分数,其中,所述冠脉血流储备分数
Figure PCTCN2021137375-appb-000041
The reserve fraction obtaining unit 133 is connected to the actual blood flow obtaining unit 131 and the ideal blood flow obtaining unit 132, and is used to obtain the coronary artery according to the actual maximum blood flow and the ideal maximum blood flow of the target blood supply area. Fractional flow reserve, wherein the fractional coronary flow reserve
Figure PCTCN2021137375-appb-000041
根据以上描述可知,本实施例能够直接获取所述目标供血区域的实际最大血流量Q S和理想最大血流量Q N,并根据Q S和Q N来获取所述冠脉血流储备分数。在此过程中并不采用
Figure PCTCN2021137375-appb-000042
来代替
Figure PCTCN2021137375-appb-000043
因而不会引入额外的误差。
According to the above description, this embodiment can directly obtain the actual maximum blood flow Q S and the ideal maximum blood flow Q N of the target blood supply region, and obtain the coronary blood flow reserve fraction according to Q S and Q N . During this process, no
Figure PCTCN2021137375-appb-000042
to replace
Figure PCTCN2021137375-appb-000043
Thus no additional error is introduced.
发明人通过研究和实践发现,
Figure PCTCN2021137375-appb-000044
的数值等于心肌最大充血状态下的实际狭窄远端冠状动脉内平均压力、与理想情况下没有狭窄病变时该位置的平均压力的比值,现有技术中无法获取理想情况下没有狭窄病变时该位置的平均压力的比值,因而才会选择使用
Figure PCTCN2021137375-appb-000045
来代替
Figure PCTCN2021137375-appb-000046
针对这一问题,请参阅图5B,于本发明的一实施例中,所述分数获取模块包括13包括实际压力获取单元134、理想压力获取单元135和储备分数获取单元136。
The inventor discovered through research and practice,
Figure PCTCN2021137375-appb-000044
The value is equal to the ratio of the actual mean pressure in the coronary artery at the distal end of the stenosis under the state of maximum myocardial hyperemia to the mean pressure at this position when there is no stenotic lesion under ideal conditions. The ratio of the average pressure of the
Figure PCTCN2021137375-appb-000045
to replace
Figure PCTCN2021137375-appb-000046
For this problem, please refer to FIG. 5B . In an embodiment of the present invention, the score obtaining module includes 13 an actual pressure obtaining unit 134 , an ideal pressure obtaining unit 135 and a reserve score obtaining unit 136 .
所述实际压力获取单元134与所述冠脉模型获取模块12相连,用于根据所述第一冠脉模型获取心肌最大充血状态下目标位置的实际平均压力
Figure PCTCN2021137375-appb-000047
其中,所述目标位置是指冠脉的狭窄远端,所述实际压力获取单元134获取
Figure PCTCN2021137375-appb-000048
的方式包括但不限于流体动力学仿真、深度学习等。
The actual pressure obtaining unit 134 is connected to the coronary artery model obtaining module 12, and is used for obtaining the actual average pressure of the target position in the maximum myocardial hyperemia state according to the first coronary artery model
Figure PCTCN2021137375-appb-000047
Wherein, the target position refers to the distal end of coronary artery stenosis, and the actual pressure acquisition unit 134 acquires
Figure PCTCN2021137375-appb-000048
The methods include but are not limited to fluid dynamics simulation, deep learning, etc.
所述理想压力获取单元135与所述冠脉模型获取模块12相连,用于根据所述第二冠脉模型获取心肌最大充血状态下目标位置的理想平均压力
Figure PCTCN2021137375-appb-000049
所述理想压力获取单元135获取
Figure PCTCN2021137375-appb-000050
的方式包括但不限于流体动力学仿真、深度学习等。
The ideal pressure obtaining unit 135 is connected to the coronary artery model obtaining module 12, and is used to obtain the ideal average pressure of the target position in the maximum myocardial hyperemia state according to the second coronary artery model
Figure PCTCN2021137375-appb-000049
The ideal pressure acquisition unit 135 acquires
Figure PCTCN2021137375-appb-000050
The methods include but are not limited to fluid dynamics simulation, deep learning, etc.
所述储备分数获取单元136与所述实际压力获取单元134和所述理想压力获取单元135相连,用于根据所述目标位置的实际平均压力
Figure PCTCN2021137375-appb-000051
和理想平均压力
Figure PCTCN2021137375-appb-000052
获取所述冠脉血流储备分数,其中,所述冠脉血流储备分数
Figure PCTCN2021137375-appb-000053
The reserve score obtaining unit 136 is connected with the actual pressure obtaining unit 134 and the ideal pressure obtaining unit 135, and is used for obtaining the actual average pressure according to the target position
Figure PCTCN2021137375-appb-000051
and ideal mean pressure
Figure PCTCN2021137375-appb-000052
Obtain the coronary blood flow reserve fraction, wherein the coronary blood flow reserve fraction
Figure PCTCN2021137375-appb-000053
根据以上描述可知,本实施例能够直接获取心肌最大充血状态下目标位置的实际平均压力
Figure PCTCN2021137375-appb-000054
和平均压力
Figure PCTCN2021137375-appb-000055
并根据
Figure PCTCN2021137375-appb-000056
Figure PCTCN2021137375-appb-000057
来获取所述冠脉血流储备分数。在此过程中并不采用
Figure PCTCN2021137375-appb-000058
来代替
Figure PCTCN2021137375-appb-000059
因而不会引入额外的误差。
According to the above description, this embodiment can directly obtain the actual average pressure of the target position in the maximum myocardial hyperemia state
Figure PCTCN2021137375-appb-000054
and mean pressure
Figure PCTCN2021137375-appb-000055
and according to
Figure PCTCN2021137375-appb-000056
and
Figure PCTCN2021137375-appb-000057
to obtain the fractional coronary flow reserve. During this process, no
Figure PCTCN2021137375-appb-000058
to replace
Figure PCTCN2021137375-appb-000059
Thus no additional error is introduced.
于本发明的一实施例中,所述冠脉血流储备分数获取系统还包括一用户交互模块。In an embodiment of the present invention, the system for obtaining fractional coronary blood flow reserve further includes a user interaction module.
可选地,所述用户交互模块与所述医学图像获取模块和/或冠脉模型获取模块相连,用于显示所述医学图像和/或第一冠脉模型。用户通过观察所述医学图像和/或第一冠脉模型,使用所述用户交互模块提供的工具输入相应的参数标注指令。所述冠脉模型获取模块根据用户输入的参数标注指令获取所述医学图像和/或第一冠脉模型中与冠脉狭窄相关的病变参数。例如,用户可以通过鼠标等输入设备,利用所述用户交互模块提供的画笔、橡皮擦等工具输入参数标注指令(例如可以通过使用鼠标点击相应的工具图标,并以拖动、点击或框选等方式输入所述参数标注指令),以对所述医学图像和/或第一冠脉模型中的狭窄位置、狭窄近端、狭窄远端、血管中心线、狭窄近端的横截面和/或狭窄远端的横截面等参数进行标注,所述冠脉模型获取模块根据用户的标注结果即可获取相应的病变参数。Optionally, the user interaction module is connected to the medical image acquisition module and/or the coronary artery model acquisition module, and is configured to display the medical image and/or the first coronary artery model. By observing the medical image and/or the first coronary artery model, the user inputs corresponding parameter labeling instructions using the tool provided by the user interaction module. The coronary artery model acquiring module acquires the lesion parameters related to coronary artery stenosis in the medical image and/or the first coronary artery model according to the parameter labeling instruction input by the user. For example, the user can input parameter annotation instructions by using tools such as brushes and erasers provided by the user interaction module through an input device such as a mouse (for example, by clicking a corresponding tool icon with a mouse, and dragging, clicking, or selecting a box, etc.) inputting the parameter labeling instructions) in the medical image and/or the first coronary artery model for stenosis position, stenosis proximal end, stenosis distal end, vessel centerline, cross section of stenosis proximal end and/or stenosis The parameters such as the cross section of the distal end are marked, and the coronary artery model obtaining module can obtain the corresponding lesion parameters according to the marking result of the user.
此外,当所述冠脉模型获取模块采用算法自动获取所述病变参数时,所述用户交互模块 还用于在所述医学图像和/或第一冠脉模型标注所述病变参数,用户通过观察所述医学图像和/或第一冠脉模型标注中标注的病变参数,使用所述用户交互模块提供的工具输入相应的参数修改指令。所述冠脉模型获取模块根据用户输入的参数修改指令对所述算法自动获取的病变参数进行修改。例如,当用户观察到算法自动获取的血管中心线不够准确时,可以通过鼠标等输入设备,利用所述用户交互模块提供的画笔、橡皮擦等工具输入参数修改指令(例如可以通过使用鼠标点击相应的工具图标,并以拖动、点击或框选等方式输入所述参数修改指令),以对所述医学图像和/或第一冠脉模型中的血管中心线进行调整。优选地,所述用户交互模块还为用户提供一控制点,用户通过鼠标选中并拖动所述控制点即可编辑样条曲线、曲面、实体等。In addition, when the coronary artery model acquisition module adopts an algorithm to automatically acquire the lesion parameters, the user interaction module is further configured to mark the lesion parameters on the medical image and/or the first coronary artery model, and the user can observe the lesion parameters by observing For the lesion parameters marked in the medical image and/or the first coronary artery model marking, a corresponding parameter modification instruction is input using the tool provided by the user interaction module. The coronary artery model obtaining module modifies the lesion parameters automatically obtained by the algorithm according to the parameter modification instruction input by the user. For example, when the user observes that the blood vessel centerline automatically obtained by the algorithm is not accurate enough, the user can use the input device such as a mouse to input a parameter modification instruction by using tools such as a brush and an eraser provided by the user interaction module (for example, by using a mouse to click on the corresponding tool icon, and input the parameter modification instruction by dragging, clicking or box selection) to adjust the blood vessel centerline in the medical image and/or the first coronary artery model. Preferably, the user interaction module further provides a control point for the user, and the user can edit the spline curve, surface, solid, etc. by selecting and dragging the control point with the mouse.
可选地,所述用户交互模块与所述医学图像获取模块相连,用于显示所述医学图像。用户通过观察所述医学图像,使用所述用户交互模块提供的工具输入相应的模型生成指令。所述冠脉模型生成模块根据用户输入的模型生成指令对所述医学图像进行分割,以获取所述第一冠脉模型。例如,用户可以通过鼠标等输入设备,利用所述用户交互模块提供的画笔、橡皮擦等工具输入模型生成指令(例如可以通过使用鼠标点击相应的工具图标,并以拖动、点击或框选等方式输入所述模型生成指令),以对所述医学图像进行分割,从而将冠脉血管从所述医学图像中分割出来,得到所述第一冠脉模型。Optionally, the user interaction module is connected to the medical image acquisition module for displaying the medical image. By observing the medical image, the user inputs corresponding model generation instructions using the tools provided by the user interaction module. The coronary artery model generation module segments the medical image according to the model generation instruction input by the user, so as to obtain the first coronary artery model. For example, the user can input model generation instructions by using tools such as a brush, an eraser, etc. provided by the user interaction module through an input device such as a mouse (for example, by using a mouse to click on the corresponding tool icon, and dragging, clicking, or box-selecting, etc. to segment the medical image, thereby segmenting coronary vessels from the medical image to obtain the first coronary artery model.
此外,当所述冠脉模型获取模块采用算法自动获取所述第一冠脉模型时,所述用户交互界面还用于显示所述第一冠脉模型。用户通过观察所述第一冠脉模型,使用所述用户交互模块提供的工具输入相应的模型编辑指令。所述冠脉模型生成模块根据用户输入的模型编辑指令对所述第一冠脉模型进行编辑。例如,当用户观察到所述第一冠脉模型的边界不准确时,可以通过鼠标等输入设备,利用所述用户交互模块提供的画笔、橡皮擦等工具输入模型编辑指令(例如可以通过使用鼠标点击相应的工具图标,并以拖动、点击或框选等方式输入所述模型编辑指令),以对所述第一冠脉模型的边界进行调整。In addition, when the coronary artery model obtaining module adopts an algorithm to automatically obtain the first coronary artery model, the user interaction interface is further configured to display the first coronary artery model. By observing the first coronary artery model, the user inputs corresponding model editing instructions using the tool provided by the user interaction module. The coronary artery model generation module edits the first coronary artery model according to a model editing instruction input by a user. For example, when the user observes that the boundary of the first coronary artery model is inaccurate, an input device such as a mouse can be used to input model editing instructions by using tools such as a brush, an eraser and the like provided by the user interaction module (for example, by using a mouse Click the corresponding tool icon, and input the model editing instruction by dragging, clicking, or box selection) to adjust the boundary of the first coronary artery model.
可选地,所述用户交互模块与所述医学图像获取模块相连,用于显示所述医学图像。用户通过观察所述医学图像,使用所述用户交互模块提供的工具输入相应的模型生成指令。所述冠脉模型生成模块根据用户输入的模型生成指令对所述医学图像中患者冠脉血管的至少一处病变进行修复,并对修复以后的医学图像进行分割,以获取所述第二冠脉模型。例如,当用户观察到所述医学图像中的狭窄病变时,可以通过鼠标等输入设备,利用所述用户交互模块提供的画笔、橡皮擦等工具输入模型生成指令(例如可以通过使用鼠标点击相应的工具图标,并以拖动、点击或框选等方式输入所述模型生成指令),以对所述医学图像中的至少一 处狭窄位置的血管中心线、血管横截面和/或血管壁进行修改,从而使所述至少一处狭窄位置的血管中心线、血管横截面和/或血管壁恢复正常状态,以实现对所述至少一处狭窄位置的病变进行修复;在修复完成以后,用户可以通过鼠标等输入设备,利用所述用户交互模块提供的画笔、橡皮擦等工具继续输入所述模型生成指令,以对所述医学图像进行分割,从而将冠脉血管从所述医学图像中分割出来,得到所述第二冠脉模型。Optionally, the user interaction module is connected to the medical image acquisition module for displaying the medical image. By observing the medical image, the user inputs corresponding model generation instructions using the tools provided by the user interaction module. The coronary artery model generation module repairs at least one lesion of the patient's coronary blood vessel in the medical image according to the model generation instruction input by the user, and segments the repaired medical image to obtain the second coronary artery Model. For example, when a user observes a stenotic lesion in the medical image, the model generation instruction can be input by using an input device such as a mouse, and tools such as a brush, an eraser, etc. provided by the user interaction module (for example, by using a mouse to click the corresponding tool icon, and input the model generation instruction by dragging, clicking or box selection) to modify the blood vessel centerline, blood vessel cross-section and/or blood vessel wall of at least one stenosis position in the medical image , so as to restore the blood vessel centerline, blood vessel cross-section and/or blood vessel wall of the at least one stenosis position to a normal state, so as to repair the lesion at the at least one stenosis position; after the repair is completed, the user can An input device such as a mouse, using tools such as brushes and erasers provided by the user interaction module to continue to input the model generation instructions to segment the medical image, thereby segmenting coronary vessels from the medical image, The second coronary artery model is obtained.
可选地,所述用户交互模块与所述冠脉模型获取模块相连,用于显示所述第一冠脉模型。其中,所述第一冠脉模块可以由算法自动生成,也可以由用户通过模型生成指令手动生成。用户通过观察所述第一冠脉模型,使用所述用户交互模块提供的工具输入相应的模型生成指令。所述冠脉模型生成模块根据用户输入的模型生成指令对所述第一冠脉模型中患者冠脉血管的至少一处病变进行修复,以获取所述第二冠脉模型。例如,当用户观察到所述第一冠脉模型中的狭窄病变时,可以通过鼠标等输入设备,利用所述用户交互模块提供的画笔、橡皮擦等工具输入模型生成指令(例如可以通过使用鼠标点击相应的工具图标,并以拖动、点击或框选等方式输入所述模型生成指令),以对所述第一冠脉模型中的至少一处狭窄位置的血管中心线、血管横截面和/或血管壁进行修改,从而使所述至少一处狭窄位置的血管中心线、血管横截面和/或血管壁恢复正常状态,以实现对所述至少一处狭窄位置的病变进行修复,修复以后即可得到所述第二冠脉模型。Optionally, the user interaction module is connected to the coronary artery model obtaining module, and is configured to display the first coronary artery model. The first coronary artery module may be automatically generated by an algorithm, or manually generated by a user through a model generation instruction. By observing the first coronary artery model, the user inputs corresponding model generation instructions using the tool provided by the user interaction module. The coronary artery model generation module repairs at least one lesion of the coronary artery of the patient in the first coronary artery model according to the model generation instruction input by the user, so as to obtain the second coronary artery model. For example, when the user observes a stenosis lesion in the first coronary artery model, the model generation instruction can be input by using an input device such as a mouse, using tools such as a brush and an eraser provided by the user interaction module (for example, by using a mouse Click the corresponding tool icon, and input the model generation instruction by dragging, clicking or box-selecting), to analyze the blood vessel centerline, blood vessel cross-section and /or the vessel wall is modified, so that the centerline of the vessel, the vessel cross-section and/or the vessel wall of the at least one stenosis position is restored to a normal state, so as to achieve the repair of the lesion at the at least one stenosis position, and after the repair The second coronary artery model can be obtained.
可选地,所述用户交互模块与所述冠脉模型获取模块相连,用于显示所述第二冠脉模型。其中,所述第二冠脉模型由所述冠脉模型获取模块采用算法自动获取。用户通过观察所述第二冠脉模型,使用所述用户交互模块提供的工具输入相应的模型编辑指令。所述冠脉模型生成模块根据用户输入的模型编辑指令对所述第二冠脉模型进行编辑。例如,当用户观察到所述第二冠脉模型的边界不准确时,可以通过鼠标等输入设备,利用所述用户交互模块提供的画笔、橡皮擦等工具输入模型编辑指令(例如可以通过使用鼠标点击相应的工具图标,并以拖动、点击或框选等方式输入所述模型编辑指令),以对所述第二冠脉模型的边界进行调整。Optionally, the user interaction module is connected to the coronary artery model obtaining module, and is configured to display the second coronary artery model. Wherein, the second coronary artery model is automatically acquired by the coronary artery model acquiring module using an algorithm. By observing the second coronary artery model, the user inputs corresponding model editing instructions using the tool provided by the user interaction module. The coronary artery model generation module edits the second coronary artery model according to the model editing instruction input by the user. For example, when the user observes that the boundary of the second coronary artery model is inaccurate, an input device such as a mouse can be used to input model editing instructions by using tools such as a brush and an eraser provided by the user interaction module (for example, by using a mouse Click the corresponding tool icon, and input the model editing instruction by dragging, clicking or box selection) to adjust the boundary of the second coronary artery model.
于本发明的一实施例中,所述用户交互模块还用于接收用户输入的自动修复指令,以触发所述冠脉模型获取模块对所述第一冠脉模型和/或所述医学图像进行自动修复。例如,当用户通过鼠标点击某一狭窄位置C的方式输入所述自动修复指令时,所述模拟修复模块根据该自动修复指令开始对该狭窄位置C的狭窄病变进行修复。In an embodiment of the present invention, the user interaction module is further configured to receive an automatic repair instruction input by the user, so as to trigger the coronary artery model acquisition module to perform the first coronary artery model and/or the medical image. Automatic repair. For example, when the user inputs the automatic repair instruction by clicking a certain stenosis position C with the mouse, the simulated repair module starts to repair the stenotic lesion at the stenosis position C according to the automatic repair instruction.
基于以上对所述冠脉血流储备分数获取系统的描述,本发明还提供一种冠脉血流储备分数获取方法,所述冠脉血流储备分数可以采用图1所示冠脉血流量储备分数获取系统实现。请参阅图6,于本发明的一实施例中,所述冠脉血流储备分数获取方法包括:Based on the above description of the system for obtaining fractional coronary blood flow reserve, the present invention also provides a method for obtaining fractional coronary blood flow reserve. The score acquisition system is implemented. Referring to FIG. 6, in an embodiment of the present invention, the method for obtaining fractional coronary blood flow reserve includes:
S61,获取患者的医学图像,所述医学图像包括患者的心脏区域。S61. Acquire a medical image of a patient, where the medical image includes a heart region of the patient.
S62,根据所述医学图像获取患者的第一冠脉模型和第二冠脉模型;其中,所述第一冠脉模型是指患者的实际冠脉模型,所述第二冠脉模型是指对患者冠脉血管的至少一处病变进行修复以后获取的冠脉模型。S62, obtaining a first coronary artery model and a second coronary artery model of the patient according to the medical image; wherein, the first coronary artery model refers to the actual coronary artery model of the patient, and the second coronary artery model refers to the Coronary artery model obtained after at least one lesion of the coronary artery of the patient is repaired.
S63,根据所述第一冠脉模型和所述第二冠脉模型获取患者的冠脉血流储备分数。S63: Obtain the coronary blood flow reserve fraction of the patient according to the first coronary artery model and the second coronary artery model.
基于以上对所述冠脉血流储备分数获取方法的描述,本发明还提供一种计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器执行时实现图6所示冠脉血流储备分数获取方法。Based on the above description of the method for obtaining the fractional coronary blood flow reserve, the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, realizes the coronary artery shown in FIG. 6 . Methods of obtaining fractional blood flow reserve.
本发明所述的冠脉血流储备分数计算方法的保护范围不限于本实施例列举的步骤执行顺序,凡是根据本发明的原理所做的现有技术的步骤增减、步骤替换所实现的方案都包括在本发明的保护范围内。The protection scope of the coronary blood flow fraction calculation method of the present invention is not limited to the execution sequence of the steps listed in this embodiment, and any solution implemented by adding or subtracting steps and replacing steps in the prior art according to the principles of the present invention All are included in the protection scope of the present invention.
本发明还提供一种冠脉血流储备分数获取系统,所述冠脉血流储备分数获取系统可以实现本发明所述的冠脉血流储备分数获取方法,但本发明所述的冠脉血流储备分数获取方法的实现装置包括但不限于本实施例列举的冠脉血流储备分数获取系统的结构,凡是根据本发明的原理所做的现有技术的结构变形和替换,都包括在本发明的保护范围内。The present invention also provides a system for obtaining fractional coronary blood flow reserve, which can realize the method for obtaining fractional coronary blood flow reserve of the present invention, but the The devices for realizing the fractional flow reserve acquisition method include, but are not limited to, the structures of the coronary blood flow fractional fractional acquisition system listed in this embodiment. within the scope of protection of the invention.
本发明所述冠脉血流储备分数获取系统能够根据患者的医学图像获取患者的第一冠脉模型,并根据所述患者的医学图像或者所述第一冠脉模型获取患者的第二冠脉模型。其中,所述第一冠脉模型是指患者的实际冠脉模型,所述第二冠脉模型是指对患者冠脉血管的至少一处病变进行修复以后获取的冠脉模型。根据所述第一冠脉模型可以获取冠脉存在狭窄病变的情况下、血管所供心肌区域能够获得的最大血流量Q S,根据所述第二冠脉模型可以获取同一区域理论上正常情况下所能获得的最大血流量Q N,基于Q S和Q N即可直接获取患者的冠脉血流储备分数。因此,本发明所述冠脉血流储备分数获取系统在获取冠脉血流储备分数时并不采用
Figure PCTCN2021137375-appb-000060
来代替
Figure PCTCN2021137375-appb-000061
因而不会引入额外的误差,故,本发明所述冠脉血流储备分数获取系统获取的冠脉血流储备分数相较于现有技术具有更高的准确度,尤其是在冠脉的狭窄位置距离主动脉较远时,本发明所述冠脉血流储备分数获取系统的优势更加明显。
The system for obtaining fractional coronary blood flow reserve of the present invention can obtain the patient's first coronary model according to the patient's medical image, and obtain the patient's second coronary model according to the patient's medical image or the first coronary model. Model. Wherein, the first coronary artery model refers to the actual coronary artery model of the patient, and the second coronary artery model refers to the coronary artery model obtained after repairing at least one lesion of the coronary artery of the patient. According to the first coronary artery model, the maximum blood flow Q S that can be obtained in the myocardial region supplied by the blood vessel under the condition of coronary stenosis and lesions can be obtained, and according to the second coronary artery model, the theoretical normal condition of the same region can be obtained. The maximum blood flow Q N that can be obtained, the coronary blood flow reserve fraction of the patient can be directly obtained based on Q S and Q N . Therefore, the system for obtaining the fractional coronary blood flow reserve of the present invention does not adopt the method for obtaining the fractional coronary blood flow reserve.
Figure PCTCN2021137375-appb-000060
to replace
Figure PCTCN2021137375-appb-000061
Therefore, no additional errors will be introduced. Therefore, the fractional coronary blood flow reserve obtained by the system for obtaining the fractional coronary blood flow reserve of the present invention has higher accuracy than the prior art, especially in the case of coronary stenosis. When the location is far away from the aorta, the advantages of the system for obtaining fractional coronary blood flow reserve of the present invention are more obvious.
综上所述,本发明有效克服了现有技术中的种种缺点而具高度产业利用价值。To sum up, the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.
上述实施例仅例示性说明本发明的原理及其功效,而非用于限制本发明。任何熟悉此技术的人士皆可在不违背本发明的精神及范畴下,对上述实施例进行修饰或改变。因此,举凡所属技术领域中具有通常知识者在未脱离本发明所揭示的精神与技术思想下所完成的一切等 效修饰或改变,仍应由本发明的权利要求所涵盖。The above-mentioned embodiments merely illustrate the principles and effects of the present invention, but are not intended to limit the present invention. Anyone skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed in the present invention should still be covered by the claims of the present invention.

Claims (10)

  1. 一种冠脉血流储备分数获取系统,其特征在于,所述冠脉血流储备分数获取系统包括:A system for obtaining fractional coronary blood flow reserve, characterized in that the system for obtaining fractional coronary blood flow reserve comprises:
    医学图像获取模块,用于获取患者的医学图像,所述医学图像包括患者的心脏区域;a medical image acquisition module for acquiring a medical image of a patient, the medical image including the patient's heart region;
    冠脉模型获取模块,与所述医学图像获取模块相连,用于根据所述医学图像获取患者的第一冠脉模型和第二冠脉模型;其中,所述第一冠脉模型是指患者的实际冠脉模型,所述第二冠脉模型是指对患者冠脉血管的至少一处病变进行修复以后获取的冠脉模型;The coronary artery model acquisition module is connected to the medical image acquisition module, and is used for acquiring the first coronary artery model and the second coronary artery model of the patient according to the medical image; wherein, the first coronary artery model refers to the patient's The actual coronary artery model, the second coronary artery model refers to the coronary artery model obtained after repairing at least one lesion of the coronary artery of the patient;
    血流储备分数获取模块,与所述冠脉模型获取模块相连,用于根据所述第一冠脉模型和所述第二冠脉模型获取患者的冠脉血流储备分数。The blood flow reserve fraction acquisition module is connected to the coronary artery model acquisition module, and is used for acquiring the coronary blood flow reserve fraction of the patient according to the first coronary artery model and the second coronary artery model.
  2. 根据权利要求1所述的冠脉血流储备分数获取系统,其特征在于,所述冠脉模型获取模块包括第一冠脉模型获取子模块和第二冠脉模型获取子模块;The system for obtaining fractional coronary blood flow reserve according to claim 1, wherein the coronary model obtaining module comprises a first coronary model obtaining sub-module and a second coronary model obtaining sub-module;
    所述第一冠脉模型获取子模块与所述医学图像获取模块相连,用于对所述医学图像进行分割以获取所述第一冠脉模型;The first coronary artery model acquisition sub-module is connected to the medical image acquisition module, and is used for segmenting the medical image to acquire the first coronary artery model;
    所述第二冠脉模型获取子模块与所述第一冠脉模型获取子模块相连,用于对所述第一冠脉模型中患者冠脉血管的至少一处病变进行模拟修复,以获取所述第二冠脉模型;或者,The second coronary artery model acquisition sub-module is connected with the first coronary artery model acquisition sub-module, and is used to simulate and repair at least one lesion of the coronary artery of the patient in the first coronary artery model, so as to obtain all the lesions. the second coronary model described above; or,
    所述第二冠脉模型获取子模块与所述医学图像获取模块相连,用于对所述医学图像中患者冠脉血管的至少一处病变进行模拟修复,并对模拟修复以后的医学图像进行分割以获取所述第二冠脉模型。The second coronary artery model acquisition sub-module is connected to the medical image acquisition module, and is used for simulating repair of at least one lesion of the coronary artery of the patient in the medical image, and segmenting the medical image after the simulating repair to obtain the second coronary artery model.
  3. 根据权利要求2所述的冠脉血流储备分数获取系统,其特征在于,所述第二冠脉模型获取子模块包括:The system for obtaining fractional coronary blood flow reserve according to claim 2, wherein the second coronary model obtaining sub-module comprises:
    第一病变参数获取单元,与所述第一冠脉模型获取子模块相连,用于获取所述第一冠脉模型中与冠脉狭窄相关的病变参数;a first lesion parameter acquisition unit, connected to the first coronary artery model acquisition sub-module, for acquiring the lesion parameters related to coronary stenosis in the first coronary artery model;
    第一模拟修复单元,与所述第一病变参数获取单元相连,用于根据所述病变参数对所述第一冠脉模型的狭窄处进行修改以获取所述第二冠脉模型。The first simulated repair unit is connected to the first lesion parameter acquisition unit, and is configured to modify the stenosis of the first coronary artery model according to the lesion parameter to acquire the second coronary artery model.
  4. 根据权利要求2所述的冠脉血流储备分数获取系统,其特征在于,所述第二冠脉模型获取子模块包括:The system for obtaining fractional coronary blood flow reserve according to claim 2, wherein the second coronary model obtaining sub-module comprises:
    第二病变参数获取单元,与所述图像获取模块相连,用于获取所述医学图像中与冠脉狭窄相关的病变参数;a second lesion parameter acquisition unit, connected to the image acquisition module, for acquiring lesion parameters related to coronary stenosis in the medical image;
    第二模拟修复单元,与所述第二病变参数获取单元相连,用于根据所述病变参数对所述医学图像中患者冠脉血管的狭窄处进行修改,以获取所述模拟修复以后的医学图像;The second simulated repair unit is connected to the second lesion parameter acquisition unit, and is configured to modify the stenosis of the patient's coronary blood vessel in the medical image according to the lesion parameter, so as to obtain the medical image after the simulated repair ;
    图像分割单元,与所述第二模拟修复单元相连,用于对所述模拟修复以后的医学图像进行分割,以获取所述第二冠脉模型。The image segmentation unit is connected to the second simulated repair unit, and is used for segmenting the medical image after the simulated repair to obtain the second coronary artery model.
  5. 根据权利要求1所述的冠脉血流储备分数获取系统,其特征在于,所述冠脉模型获取模块包括第三冠脉模型获取子模块和第四冠脉模型获取子模块;The system for obtaining fractional coronary blood flow reserve according to claim 1, wherein the coronary model obtaining module comprises a third coronary model obtaining sub-module and a fourth coronary model obtaining sub-module;
    所述第三冠脉模型获取子模块与所述医学图像获取模块相连,用于对所述医学图像进行分割以获取所述第一冠脉模型;The third coronary artery model acquisition sub-module is connected to the medical image acquisition module, and is used for segmenting the medical image to acquire the first coronary artery model;
    所述第四冠脉模型获取子模块与所述第三冠脉模型获取子模块相连,用于对所述第一冠脉模型中患者冠脉血管的至少一处病变进行虚拟治疗,以获取所述第二冠脉模型;或者The fourth coronary artery model acquisition sub-module is connected with the third coronary artery model acquisition sub-module, and is used to perform virtual treatment on at least one lesion of the coronary artery of the patient in the first coronary artery model, so as to obtain all the lesions. the second coronary model described above; or
    所述第四冠脉模型获取子模块与所述医学图像获取模块相连,用于对所述医学图像中患者冠脉血管的至少一处病变进行虚拟治疗,并对虚拟治疗以后的医学图像进行分割以获取所述第二冠脉模型。The fourth coronary artery model acquisition sub-module is connected to the medical image acquisition module, and is used to perform virtual treatment on at least one lesion of the coronary blood vessel of the patient in the medical image, and segment the medical image after the virtual treatment to obtain the second coronary artery model.
  6. 根据权利要求1所述的冠脉血流储备分数获取系统,其特征在于,所述血流储备分数获取模块包括:The system for obtaining fractional coronary blood flow reserve according to claim 1, wherein the fractional blood flow reserve obtaining module comprises:
    实际血流量获取单元,与所述冠脉模型获取模块相连,用于根据所述第一冠脉模型获取一目标供血区域的实际最大血流量;an actual blood flow acquisition unit, connected to the coronary artery model acquisition module, and configured to acquire the actual maximum blood flow of a target blood supply area according to the first coronary artery model;
    理想血流量获取单元,与所述冠脉模型获取模块相连,用于根据所述第二冠脉模型获取所述目标供血区域的理想最大血流量;an ideal blood flow acquisition unit, connected to the coronary artery model acquisition module, and configured to acquire the ideal maximum blood flow of the target blood supply region according to the second coronary artery model;
    储备分数获取单元,与所述实际血流量获取单元和所述理想血流量获取单元相连,用于根据所述目标供血区域的实际最大血流量和理想最大血流量获取所述冠脉血流储备分数。a fractional reserve acquisition unit, connected to the actual blood flow acquisition unit and the ideal blood flow acquisition unit, and configured to acquire the coronary blood flow reserve fraction according to the actual maximum blood flow and the ideal maximum blood flow of the target blood supply area .
  7. 根据权利要求1所述的冠脉血流储备分数获取系统,其特征在于,所述血流储备分数获取模块包括:The system for obtaining fractional coronary blood flow reserve according to claim 1, wherein the fractional blood flow reserve obtaining module comprises:
    实际压力获取单元,与所述冠脉模型获取模块相连,用于根据所述第一冠脉模型获取心肌最大充血状态下一目标位置的实际平均压力,其中,所述目标位置是指冠脉的狭窄远端;The actual pressure acquisition unit is connected to the coronary artery model acquisition module, and is used for acquiring the actual average pressure of the target position in the maximum myocardial hyperemia state according to the first coronary artery model, wherein the target position refers to the coronary artery. narrow distal end;
    理想压力获取单元,与所述冠脉模型获取模块相连,用于根据所述第二冠脉模型获取心肌最大充血状态下所述目标位置的理想平均压力;an ideal pressure acquisition unit, connected to the coronary artery model acquisition module, and configured to acquire the ideal average pressure of the target position under the maximum myocardial hyperemia state according to the second coronary artery model;
    储备分数获取单元,与所述实际压力获取单元和所述理想压力获取单元相连,用于根 据所述目标位置的实际平均压力和理想平均压力获取所述冠脉血流储备分数。A fractional reserve acquisition unit, connected to the actual pressure acquisition unit and the ideal pressure acquisition unit, and configured to acquire the coronary blood flow reserve fraction according to the actual average pressure and the ideal average pressure at the target position.
  8. 根据权利要求1所述的冠脉血流储备分数获取系统,其特征在于,所述冠脉血流储备分数获取系统还包括:The system for obtaining fractional coronary blood flow reserve according to claim 1, wherein the system for obtaining fractional coronary blood flow reserve further comprises:
    用户交互模块,与所述医学图像获取模块和/或所述冠脉模型获取模块相连,用于显示所述医学图像、所述第一冠脉模型和/或所述第二冠脉模型,并用于获取用户输入的模型生成指令和/或模型编辑指令;所述冠脉模型获取模块根据所述模型生成指令获取所述第二冠脉模型,和/或所述冠脉模型获取模块根据所述模型编辑指令对所述第一冠脉模型和/或所述第二冠脉模型进行编辑。A user interaction module, connected with the medical image acquisition module and/or the coronary artery model acquisition module, for displaying the medical image, the first coronary artery model and/or the second coronary artery model, and using to obtain the model generation instruction and/or model editing instruction input by the user; the coronary artery model acquisition module acquires the second coronary artery model according to the model generation instruction, and/or the coronary artery model acquisition module acquires the coronary artery model according to the The model editing instruction edits the first coronary artery model and/or the second coronary artery model.
  9. 一种冠脉血流储备分数获取方法,其特征在于,所述冠脉血流储备分数获取方法包括:A method for obtaining fractional coronary blood flow reserve, wherein the method for obtaining fractional coronary blood flow reserve comprises:
    获取患者的医学图像,所述医学图像包括患者的心脏区域;obtaining a medical image of the patient, the medical image including the patient's heart region;
    根据所述医学图像获取患者的第一冠脉模型和第二冠脉模型;其中,所述第一冠脉模型是指患者的实际冠脉模型,所述第二冠脉模型是指对患者冠脉血管的至少一处病变进行修复以后获取的冠脉模型;Obtain the first coronary artery model and the second coronary artery model of the patient according to the medical image; wherein, the first coronary artery model refers to the actual coronary artery model of the patient, and the second coronary artery model refers to the coronary artery model of the patient. Coronary artery model obtained after at least one lesion of the blood vessel is repaired;
    根据所述第一冠脉模型和所述第二冠脉模型获取患者的冠脉血流储备分数。The coronary blood flow reserve fraction of the patient is obtained according to the first coronary artery model and the second coronary artery model.
  10. 一种计算机可读存储介质,其上存储有计算机程序,其特征在于:该计算机程序被处理器执行时实现权利要求9所述的冠脉血流储备分数获取方法。A computer-readable storage medium on which a computer program is stored, characterized in that: when the computer program is executed by a processor, the method for obtaining fractional coronary blood flow reserve according to claim 9 is implemented.
PCT/CN2021/137375 2021-01-26 2021-12-13 Coronary fractional flow reserve obtaining system and method, and medium WO2022160973A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110105368.4A CN112950537A (en) 2021-01-26 2021-01-26 Coronary blood flow reserve fraction acquisition system, method and medium
CN202110105368.4 2021-01-26

Publications (1)

Publication Number Publication Date
WO2022160973A1 true WO2022160973A1 (en) 2022-08-04

Family

ID=76237184

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/137375 WO2022160973A1 (en) 2021-01-26 2021-12-13 Coronary fractional flow reserve obtaining system and method, and medium

Country Status (2)

Country Link
CN (1) CN112950537A (en)
WO (1) WO2022160973A1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117058136A (en) * 2022-11-23 2023-11-14 杭州脉流科技有限公司 System and computer device for estimating post-operative coronary fractional flow reserve based on pre-operative coronary angiography images

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112950537A (en) * 2021-01-26 2021-06-11 上海友脉科技有限责任公司 Coronary blood flow reserve fraction acquisition system, method and medium
CN113397579A (en) * 2021-07-23 2021-09-17 上海友脉科技有限责任公司 Hemodynamics analysis device, method, medium, and electronic device
CN113408152B (en) * 2021-07-23 2023-07-25 上海友脉科技有限责任公司 Coronary artery bypass grafting simulation system, method, medium and electronic equipment
CN114882099A (en) * 2022-04-22 2022-08-09 中国人民解放军陆军第九五〇医院 Coronary blood vessel blood supply amount analysis device and operation method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106659399A (en) * 2014-05-05 2017-05-10 西门子保健有限责任公司 Method and system for non-invasive functional assessment of coronary artery stenosis using flow computations in diseased and hypothetical normal anatomical models
US20180089829A1 (en) * 2015-05-12 2018-03-29 Singapore Health Services Pte Ltd Medical image processing methods and systems
CN109846500A (en) * 2019-03-15 2019-06-07 浙江大学 A kind of method and apparatus of determining coronary flow reserve score
CN111134651A (en) * 2019-12-09 2020-05-12 杭州脉流科技有限公司 Method, device and system for calculating fractional flow reserve based on intracavity images and computer storage medium
CN112950537A (en) * 2021-01-26 2021-06-11 上海友脉科技有限责任公司 Coronary blood flow reserve fraction acquisition system, method and medium

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8315812B2 (en) * 2010-08-12 2012-11-20 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
US10162932B2 (en) * 2011-11-10 2018-12-25 Siemens Healthcare Gmbh Method and system for multi-scale anatomical and functional modeling of coronary circulation
US10130266B2 (en) * 2014-06-30 2018-11-20 Siemens Healthcare Gmbh Method and system for prediction of post-stenting hemodynamic metrics for treatment planning of arterial stenosis

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106659399A (en) * 2014-05-05 2017-05-10 西门子保健有限责任公司 Method and system for non-invasive functional assessment of coronary artery stenosis using flow computations in diseased and hypothetical normal anatomical models
US20180089829A1 (en) * 2015-05-12 2018-03-29 Singapore Health Services Pte Ltd Medical image processing methods and systems
CN109846500A (en) * 2019-03-15 2019-06-07 浙江大学 A kind of method and apparatus of determining coronary flow reserve score
CN111134651A (en) * 2019-12-09 2020-05-12 杭州脉流科技有限公司 Method, device and system for calculating fractional flow reserve based on intracavity images and computer storage medium
CN112950537A (en) * 2021-01-26 2021-06-11 上海友脉科技有限责任公司 Coronary blood flow reserve fraction acquisition system, method and medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117058136A (en) * 2022-11-23 2023-11-14 杭州脉流科技有限公司 System and computer device for estimating post-operative coronary fractional flow reserve based on pre-operative coronary angiography images
CN117058136B (en) * 2022-11-23 2024-01-09 杭州脉流科技有限公司 Computer device for estimating postoperative coronary blood flow reserve fraction based on preoperative coronary angiography image

Also Published As

Publication number Publication date
CN112950537A (en) 2021-06-11

Similar Documents

Publication Publication Date Title
WO2022160973A1 (en) Coronary fractional flow reserve obtaining system and method, and medium
US11816837B2 (en) Vascular characteristic determination with correspondence modeling of a vascular tree
CN108186038B (en) System for calculating coronary blood flow reserve fraction based on arteriography image
US10460204B2 (en) Method and system for improved hemodynamic computation in coronary arteries
US10803995B2 (en) Method and system for non-invasive functional assessment of coronary artery stenosis using flow computations in diseased and hypothetical normal anatomical models
US10134129B2 (en) Method and system for hemodynamic computation in coronary arteries
JP6396468B2 (en) Local FFR estimation and visualization to improve functional stenosis analysis
WO2016001017A1 (en) Apparatus for determining a fractional flow reserve value
US11017531B2 (en) Shell-constrained localization of vasculature
CN116090364A (en) Method for obtaining coronary blood flow reserve fraction based on CTA image and readable storage medium
Glaßer et al. Combined visualization of wall thickness and wall shear stress for the evaluation of aneurysms
JP2015097724A (en) Blood vessel analysis device and blood vessel analysis program
Van Walsum et al. Guide wire reconstruction and visualization in 3DRA using monoplane fluoroscopic imaging
CN110706770B (en) Cardiac data processing apparatus, cardiac data processing method, and computer-readable storage medium
Kousera et al. Patient-specific coronary stenoses can be modeled using a combination of OCT and flow velocities to accurately predict hyperemic pressure gradients
KR20140120236A (en) Integrated analysis method of matching myocardial and cardiovascular anatomy informations
Wels et al. Intuitive and accurate patient-specific coronary tree modeling from cardiac computed-tomography angiography
CN115115735A (en) Rapid calculation system and method for endothelium dynamic strain based on multi-phase coronary CT radiography
CN114664455A (en) Coronary artery blood flow reserve fraction calculation method and device
Tsompou et al. Validation study of a novel method for the 3D reconstruction of coronary bifurcations
Cong et al. Quantitative analysis of deformable model-based 3-D reconstruction of coronary artery from multiple angiograms
Cong et al. Energy back-projective composition for 3-D coronary artery reconstruction
JP2015217113A (en) Blood vessel analysis device, medical image diagnostic device, blood vessel analysis method, and blood vessel analysis program
Son et al. Reconstruction of blood vessel model with adventitia from CT and IVUS images for FSI analysis
Andrikos et al. A new method for the 3D reconstruction of coronary bifurcations pre and post the angioplasty procedure using the QCA

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21922563

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21922563

Country of ref document: EP

Kind code of ref document: A1