CN112785580B - Method and device for determining vascular flow velocity - Google Patents
Method and device for determining vascular flow velocity Download PDFInfo
- Publication number
- CN112785580B CN112785580B CN202110120550.7A CN202110120550A CN112785580B CN 112785580 B CN112785580 B CN 112785580B CN 202110120550 A CN202110120550 A CN 202110120550A CN 112785580 B CN112785580 B CN 112785580B
- Authority
- CN
- China
- Prior art keywords
- blood vessel
- image
- contrast agent
- coronary angiography
- determining
- Prior art date
- Legal status (The legal status 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 status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 63
- 230000002792 vascular Effects 0.000 title claims abstract description 21
- 210000004204 blood vessel Anatomy 0.000 claims abstract description 183
- 238000002586 coronary angiography Methods 0.000 claims abstract description 92
- 239000002872 contrast media Substances 0.000 claims abstract description 76
- 230000017531 blood circulation Effects 0.000 claims abstract description 38
- 238000012549 training Methods 0.000 claims description 28
- 238000013528 artificial neural network Methods 0.000 claims description 27
- 238000004364 calculation method Methods 0.000 claims description 20
- 210000004351 coronary vessel Anatomy 0.000 claims description 10
- 238000009499 grossing Methods 0.000 claims description 8
- 244000181917 Rubus leucodermis Species 0.000 claims description 6
- 235000011036 Rubus leucodermis Nutrition 0.000 claims description 6
- 235000003942 Rubus occidentalis Nutrition 0.000 claims description 6
- 238000002583 angiography Methods 0.000 claims description 6
- 230000015572 biosynthetic process Effects 0.000 claims description 4
- 239000003795 chemical substances by application Substances 0.000 claims description 4
- 238000003786 synthesis reaction Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 abstract description 5
- 238000004590 computer program Methods 0.000 description 16
- 238000010586 diagram Methods 0.000 description 14
- 230000008569 process Effects 0.000 description 9
- 238000012545 processing Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 8
- 238000004891 communication Methods 0.000 description 7
- 238000004422 calculation algorithm Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 230000003321 amplification Effects 0.000 description 4
- 238000013135 deep learning Methods 0.000 description 4
- 238000003199 nucleic acid amplification method Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 238000003709 image segmentation Methods 0.000 description 3
- 238000002595 magnetic resonance imaging Methods 0.000 description 3
- 238000007781 pre-processing Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000000747 cardiac effect Effects 0.000 description 2
- 238000002591 computed tomography Methods 0.000 description 2
- 238000013136 deep learning model Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 210000000709 aorta Anatomy 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010247 heart contraction Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000002601 radiography Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 230000002087 whitening effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, 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
- A61B5/026—Measuring blood flow
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
- G06T2207/30104—Vascular flow; Blood flow; Perfusion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/15—Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Surgery (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Quality & Reliability (AREA)
- Physiology (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Public Health (AREA)
- Cardiology (AREA)
- Animal Behavior & Ethology (AREA)
- Molecular Biology (AREA)
- Veterinary Medicine (AREA)
- Radiology & Medical Imaging (AREA)
- Artificial Intelligence (AREA)
- Hematology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
Embodiments of the present application provide a method, apparatus, computer-readable medium, and electronic device for determining a vascular flow rate. The method for determining the vascular flow rate comprises the following steps: based on the coronary angiography image, predicting position deformation information of blood vessels in the coronary angiography image at the next acquisition time, then based on the position deformation information and the blood vessels in the coronary angiography image, determining a blood vessel image corresponding to the next acquisition time, predicting the position of a contrast agent in the blood vessels, determining the position of the contrast agent corresponding to the next time, determining the blood flow velocity in the blood vessels based on the positions of the contrast agent corresponding to a plurality of times in the blood vessels, and determining the blood flow velocity by performing forward wave tracking on the contrast agent by predicting the position of the blood vessels corresponding to the next acquisition time, thereby improving the accuracy and efficiency of blood flow velocity detection.
Description
Technical Field
The present application relates to the field of image processing technology, and in particular, to a method, an apparatus, a computer readable medium, and an electronic device for determining a blood vessel flow rate.
Background
In many blood flow velocity determination methods, the related art generally detects a blood flow velocity based on a center line by image segmentation and a center line extraction step. However, in practical applications, the vessel length calculation accuracy is sensitive to both, and is easily affected by image segmentation and the center line position. In addition, the image segmentation is used as a key step in the method, is the step with the greatest difficulty, and the precision is often influenced by various factors so as to be difficult to achieve an ideal condition; the extraction of the central line can deviate from the actual situation due to the adverse factors such as deviation, insufficient smoothness and the like, so that the calculation accuracy of the blood vessel length is affected, and the problem of inaccurate determination of the blood flow velocity is caused.
Disclosure of Invention
Embodiments of the present application provide a method, an apparatus, a computer readable medium, and an electronic device for determining a blood vessel flow velocity, so that a contrast agent may be subjected to a forward wave tracking to determine a blood flow velocity at least to a certain extent, thereby improving accuracy and efficiency of blood flow velocity detection.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned in part by the practice of the application.
According to one aspect of embodiments of the present application, there is provided a method of determining a vascular flow rate, comprising: acquiring a coronary angiography image; based on the coronary angiography image, predicting position deformation information corresponding to a blood vessel in the coronary angiography image at the next acquisition time; determining a blood vessel image corresponding to the next acquisition time based on the position deformation information and the blood vessels in the coronary angiography image; based on the blood vessel image, predicting the position of the contrast agent in the blood vessel, and determining the position of the contrast agent corresponding to the next moment; a flow rate of blood flow in a blood vessel is determined based on the locations of contrast agent in the blood vessel corresponding to a plurality of times.
According to one aspect of embodiments of the present application, there is provided an apparatus for determining a flow rate of a blood vessel, comprising: an acquisition unit for acquiring a coronary angiography image; the deformation unit is used for predicting position deformation information corresponding to a blood vessel in the coronary angiography image at the next acquisition moment based on the coronary angiography image; the blood vessel unit is used for determining a blood vessel image corresponding to the next acquisition time based on the position deformation information and blood vessels in the coronary angiography image; a contrast unit, configured to predict a position of a contrast agent in a blood vessel based on the blood vessel image, and determine a position of the contrast agent corresponding to the next time; and the flow rate unit is used for determining the blood flow velocity in the blood vessel based on the positions of the contrast agent in the blood vessel corresponding to a plurality of moments.
In some embodiments of the present application, based on the foregoing, the apparatus for determining a vascular flow rate further includes: the identification unit is used for identifying the blood vessel type in the coronary angiography image based on the blood vessel classification network obtained through pre-training; the blood vessel classification network is obtained by training a neural network based on a coronary image sample; the vessel types include left anterior descending branch, left circumflex branch, and right coronary artery.
In some embodiments of the present application, based on the foregoing solution, the deformation unit includes: the deformation model unit is used for acquiring a deformation prediction model corresponding to the blood vessel type based on the blood vessel type; the deformation prediction model is obtained by training a neural network through coronary image samples corresponding to each blood vessel type; the model prediction unit is used for inputting the coronary angiography image into the deformation prediction model for prediction and outputting position deformation information corresponding to the blood vessel in the coronary angiography image at the next acquisition time.
In some embodiments of the present application, based on the foregoing solution, the imaging unit is configured to obtain a front wave tracking model corresponding to the blood vessel type based on the blood vessel type; the front wave tracking model is obtained by training a neural network through angiography agent samples corresponding to each blood vessel type; and inputting the blood vessel image into the front wave tracking model for prediction, and outputting the position of the contrast agent corresponding to the next moment.
In some embodiments of the present application, based on the foregoing solution, the device for determining a blood vessel flow rate is further configured to perform a multi-scale white cap operation on the coronary angiography image based on a set structural operator, and extract a bright region in the coronary angiography image; performing multi-scale black cap operation on the coronary angiography image based on a set structural operator, and extracting a dark region in the coronary angiography image; and performing image synthesis based on the bright area and the dark area to generate an enhanced coronary angiography image.
In some embodiments of the present application, based on the foregoing, the flow rate unit includes: a curve unit for generating a position curve of the contrast agent based on the positions of the contrast agent in the blood vessel corresponding to the plurality of times; and a slope unit for determining a blood flow velocity in the blood vessel based on a slope of the position linear curve.
In some embodiments of the present application, based on the foregoing solution, the curve unit is configured to convert, based on the positions of the contrast agents corresponding to the plurality of moments in time in the blood vessel, to a three-dimensional space length through spatial resolution and projection ratio of the image; and generating a position curve corresponding to the three-dimensional space length by taking time as an abscissa axis based on the three-dimensional space length corresponding to the multiple moments.
In some embodiments of the present application, based on the foregoing scheme, the slope unit includes: the smoothing unit is used for carrying out smoothing processing on the position curve and generating a position linear curve based on time; the interval unit is used for identifying the position linear curve and determining a speed calculation interval in the position linear curve; and a curve slope unit for determining a blood flow velocity in the blood vessel based on the curve slope in the velocity calculation section.
In some embodiments of the present application, based on the foregoing scheme, the interval unit is configured to obtain the local velocity by a least square method based on a slope of the position linear curve; and expanding to obtain a speed calculation interval corresponding to the target time based on the target time corresponding to the maximum speed in the local speeds.
According to an aspect of the embodiments of the present application, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a method of determining a vascular flow rate as described in the above embodiments.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; and a storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method of determining vascular flow rate as described in the above embodiments.
According to an aspect of embodiments of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from the computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the method of determining a vascular flow rate provided in the various alternative implementations described above.
According to the technical scheme provided by the embodiments of the application, based on the coronary angiography image, the position deformation information of the blood vessel in the coronary angiography image corresponding to the next acquisition time is predicted, then, based on the position deformation information and the blood vessel in the coronary angiography image, the blood vessel image corresponding to the next acquisition time is determined, the position of the contrast agent in the blood vessel is predicted, the position of the contrast agent corresponding to the next time is determined, so that the blood flow velocity in the blood vessel is determined based on the positions of the contrast agent corresponding to a plurality of times in the blood vessel, and the blood flow velocity is determined by performing forward wave tracking on the contrast agent by predicting the position corresponding to the next acquisition time.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 shows a schematic diagram of an exemplary system architecture to which the technical solutions of embodiments of the present application may be applied;
FIG. 2 schematically illustrates a flow chart of a method of determining a vascular flow rate according to one embodiment of the present application;
FIG. 3 schematically illustrates a schematic view of coronary angiography image enhancement according to one embodiment of the present application;
FIG. 4 schematically illustrates a schematic diagram of predicting vessel types based on a neural network, according to one embodiment of the present application;
FIG. 5 schematically illustrates a schematic of predicting contrast agent locations based on a neural network, according to one embodiment of the present application;
FIG. 6 schematically illustrates a schematic of contrast agent lengths corresponding to multi-frame coronary angiography, according to one embodiment of the application;
FIG. 7 schematically illustrates a schematic diagram of a location curve according to one embodiment of the present application;
FIG. 8 schematically illustrates a schematic diagram of a method of determining vascular flow velocity according to one embodiment of the present application;
FIG. 9 schematically illustrates a block diagram of an apparatus for determining vascular flow velocity according to one embodiment of the present application;
fig. 10 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present application. One skilled in the relevant art will recognize, however, that the aspects of the application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Fig. 1 illustrates an exemplary system frame to which the technical solution according to the embodiments of the present application may be applied, as shown in fig. 1, the system frame may include a medical image acquisition device 101, a network 102, a server 103, and a terminal device 104. The acquiring device 101 in this embodiment is used for acquiring medical images of the aorta, and may be an electronic computed tomography (Computed Tomography, CT) apparatus, a magnetic resonance imaging (Magnetic Resonance Imaging, MRI) apparatus, or the like, which is not limited herein; the network 104 in this embodiment is used to provide a communication link between the terminal device and the server 103, and may include various connection types, such as a wired communication link, a wireless communication link, or a bluetooth, 5G network, etc., which are not limited herein, and is used to transmit the acquired medical image to the blood vessel detection device; the terminal device 104 in this embodiment may be one or more of a smart phone, a tablet computer, and a portable computer 104, and of course, may also be a desktop computer, etc., which is not limited herein.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, the server 103 may be a server cluster formed by a plurality of servers.
It should be noted that the server 103 in this embodiment may have the same function as the terminal device 104, that is, determine the blood vessel flow rate. Specifically, by acquiring a coronary angiography image; based on the coronary angiography image, predicting position deformation information corresponding to a blood vessel in the coronary angiography image at the next acquisition time; determining a blood vessel image corresponding to the next acquisition time based on the position deformation information and blood vessels in the coronary angiography image; based on the blood vessel image, predicting the position of the contrast agent in the blood vessel, and determining the position of the contrast agent corresponding to the next moment; the flow velocity of blood flow in the blood vessel is determined based on the positions of the contrast agent in the blood vessel corresponding to the plurality of times.
According to the scheme, the position deformation information of the blood vessel in the coronary angiography image at the next acquisition time is predicted based on the coronary angiography image, then the blood vessel image corresponding to the next acquisition time is determined based on the position deformation information and the blood vessel in the coronary angiography image, the position of the contrast agent in the blood vessel is predicted, the position of the contrast agent corresponding to the next time is determined, the blood flow velocity in the blood vessel is determined based on the positions of the contrast agent corresponding to a plurality of times in the blood vessel, and the blood flow velocity is determined by performing forward wave tracking on the contrast agent when the position of the blood vessel corresponding to the next acquisition time is obtained through prediction, so that the accuracy and the efficiency of blood flow velocity detection are improved.
The implementation details of the technical solutions of the embodiments of the present application are described in detail below:
fig. 2 shows a flow chart of a method of determining a vascular flow rate, which may be performed by a server, which may be the server shown in fig. 1, according to one embodiment of the present application. Referring to fig. 2, the method for determining the blood vessel flow rate at least includes steps S210 to S250, which are described in detail as follows:
in step S210, a coronary angiography image is acquired.
In an embodiment of the present application, the acquired coronary angiography image may be an original two-dimensional coronary angiography medical image sequence, and the region photographed by the image is a region where the coronary artery of the subject is located.
In an embodiment of the present application, after acquiring the coronary angiographic image, the method further includes: performing multi-scale white cap operation on the coronary angiography image based on a set structural operator, and extracting a bright region in the coronary angiography image; performing multi-scale black cap operation on the coronary angiography image based on a set structural operator, and extracting a dark region in the coronary angiography image; image synthesis is performed based on the bright and dark regions, generating a coronary angiography image after enhancement. The blood vessel region is enhanced in the image enhancement mode, the background noise is restrained, and the contrast ratio of the blood vessel and the background is increased.
As shown in fig. 3, white cap operation is used to extract bright areas:
wherein,representing an open operation for smoothing the bright area;
the black cap operation is used to extract dark areas:
BHT(x,y)＝f·B(x,y)-f(x,y)
wherein,representing a closed operation for smoothing dark areas; />Representing expansion calculation->Representing corrosion operation; b includes B0, B1, B2,. Bn, respectively, for representing structural calculations of different sizesAnd the sub-extraction module is used for extracting features of different scales.
In the embodiment, the image enhancement is realized by carrying out an algorithm of combining the white cap and the black cap based on the structural operators with different sizes, so that the vascular structure is clearer and clearer, and the background noise is suppressed.
In an embodiment of the present application, after acquiring the coronary angiographic image, the method further includes: based on a blood vessel classification network obtained through pre-training, identifying the blood vessel type in the coronary angiography image; the blood vessel classification network is obtained by training a neural network based on a coronary image sample; vessel types include left anterior descending branch, left circumflex branch, and right coronary artery.
The vessel types in this embodiment include the left anterior descending branch (Left Anterior Descending, LAD), the left circumflex branch (Left circumflex branch, LCX), the right coronary artery (Right coronary artery, RCA), and the like. In addition, in order to more accurately measure the blood flow velocity corresponding to each blood vessel, in this embodiment, for each type of blood vessel, there is a neural network model corresponding to each blood vessel, so as to determine the blood vessel deformation information corresponding to the next acquisition time, or predict the position of the contrast agent.
As shown in fig. 4, the flow of the classification task training in this embodiment is: step 1: the 2D original coronary angiography image is passed through a deep neural network to obtain a prediction result; step 2: comparing the prediction result with the manual label, and feeding back to the neural network; step 3: the neural network is updated to evolve toward reducing the prediction error. By using a large amount of data, the iterative process is repeated thousands of times in this embodiment, and the final prediction result is close to the manual standard. The network structure of the scheme is low in complexity and light in magnitude, and the coronary artery full graph is directly classified, so that an end-to-end algorithm processing mode is realized, and the method is quick, simple and high in precision.
Furthermore, before training the neural network, the medical image samples can be amplified and preprocessed, so that the complexity of training the samples is increased, and the training precision of the neural network is improved. The amplification of angiography images comprises data preprocessing and data amplification, and the data preprocessing and data amplification work is performed on original coronary angiography medical image data, so that the accuracy and the robustness of deep learning classification, registration and tracking algorithms are improved. The data preprocessing process includes whitening and the like, and in practical cases, different medical image devices are adopted by all hospitals, so that the same deep learning model has larger algorithm result difference. The original medical image and the like are whitened, namely, gray values of pixels of the image are linearly mapped from 0 to 255 to [0,1], so that the robustness of the deep learning model is improved.
The number of samples of the medical image data is small, and the data samples are required to be artificially added during the deep learning training to improve the robustness of the algorithm result. Compared with the prior art that uses a simple sample copying method to amplify the data sample, in the case of data amplification of the original coronary angiography, the following method is adopted in this embodiment: the method for amplifying data such as image translation, rotation, mirroring, brightness change, scaling and the like improves algorithm results, and particularly, the method for amplifying data, which is added with noise obeying normal distribution, has obvious classification improving effect.
In addition, in the embodiment, when determining the blood vessel type, the automatic main branch blood vessel classification can be performed by using the judgment reference based on the experience angle, wherein the judgment reference based on the experience angle comprises the body position angle commonly used in coronary angiography. Specifically includes a left front oblique position (Left Anterior oblique, LAO), a right front oblique position (Right Anterior oblique, RAO), a front rear position (AP), a foot position (CAU) or a head position (CRA). Specifically, the main blood vessel of the current coronary angiography is judged through the common experience angle of the coronary angiography: and reading two shooting angles LAO/RAO and CRA/CAU of the coronary angiography recorded in the DICOM data, and selecting the combination closest to the experience angle as the final classification basis. A drawback of this method is that when one wants to pay attention to a certain vessel, the physician may not follow an empirical angle, but choose the contrast angle arbitrarily, in which case the accuracy of the method may be affected.
In step S220, based on the coronary angiography image, positional deformation information of the blood vessel in the coronary angiography image corresponding to the next acquisition time is predicted.
In an embodiment of the present application, predicting, based on a coronary angiography image, positional deformation information corresponding to a blood vessel in the coronary angiography image at a next acquisition time includes: based on the blood vessel type, acquiring a deformation prediction model corresponding to the blood vessel type; the deformation prediction model is obtained by training a neural network through coronary image samples corresponding to each blood vessel type; and inputting the coronary angiography image into a deformation prediction model for prediction, and outputting position deformation information corresponding to the blood vessel in the coronary angiography image at the next acquisition time.
In an embodiment of the present application, the position deformation information in the present embodiment is used to represent the information of the change of the position and shape of the blood vessel from the first acquisition time to the next acquisition time, which is fine-tuned due to the influence of the heart beat and the blood flow pressure during the heart beat.
As shown in fig. 5, the method of calculating the length of the main blood vessel based on the front wave tracking method of deep learning is divided into two main parts: an image registration section and a front wave tracking regression section. The image registration part is used for determining position deformation information, and the front wave tracking regression part is used for determining the position of the contrast agent, and the specific explanation is as follows:
The previous frame image of coronary angiography to be tracked is used as the input of a model, end-to-end training is carried out, the output of a network is a deformation field with the size consistent with that of the angiography image and the channel number of 2, so that the shape of a blood vessel changes due to heart beating when the previous frame image is displayed in the current image, and therefore, the tracking task has more definite position information.
Specifically, the deformation field in this embodiment includes a displacement parameter of each pixel in the X and Y directions, where the displacement parameter may be the number of pixels.
The training process for the deep neural network 1 in fig. 5 is as follows: step 1: the 2D image is registered with the neural network to obtain a prediction result; step 2: comparing the prediction result with the template image and feeding back to the registration neural network; step 3: the registration neural network is updated to evolve toward reducing the prediction error. In this embodiment, a large amount of data is used, and the iterative process is repeated thousands of times, so that the final prediction result is close to the template image. The image registration method provided by the scheme is an unsupervised learning method, does not need manual labeling, and saves the labeling time cost.
In step S230, a blood vessel image corresponding to the next acquisition time is determined based on the positional deformation information and the blood vessels in the coronary angiography image.
In an embodiment of the present application, after determining the position deformation information corresponding to the next acquisition time, a blood vessel image corresponding to the next acquisition time is predicted based on the position deformation information and a blood vessel in the coronary angiography image, where the blood vessel image is mainly reflected on the position, shape, and other attributes of the blood vessel. The specific prediction mode can be based on the blood vessel position in the coronary angiography image, the position deformation information is added, and then a blood vessel image corresponding to the next acquisition time is obtained, wherein the specific blood vessel position, blood vessel shape and other information are included.
In step S240, the position of the contrast agent in the blood vessel is predicted based on the blood vessel image, and the position of the contrast agent corresponding to the next time is determined.
In an embodiment of the present application, predicting a position of a contrast agent in a blood vessel based on a blood vessel image, determining a position of the contrast agent corresponding to a next time includes: based on the blood vessel type, acquiring a front wave tracking model corresponding to the blood vessel type; the front wave tracking model is obtained by training a neural network through angiography agent samples corresponding to each blood vessel type; and then inputting the blood vessel image into a front wave tracking model for prediction, and outputting the position of the contrast agent corresponding to the next moment.
Specifically, in the process of performing front wave tracking based on a deep learning mode, the front wave tracking can be processed through a trained neural network. In the processing process, the result of registering the image to be tracked in the last step and the previous frame is used as the input of a front wave tracking network, and the output of the network is the current front wave position of the contrast agent.
In addition, in the embodiment of the present application, in the training process of the front wave tracking model, that is, the deep neural network 2 in fig. 5, a specific training process is as follows: step 1: the registration result is combined with the image to be tracked and is input into a front wave tracking network to track the front wave; step 2: comparing the predicted result with the true value and feeding back to the front wave tracking network; step 3: the front tracking network is updated to evolve toward reducing the prediction error. In the embodiment of the application, the iteration process is repeated thousands of times by using a large amount of data, and the final prediction result is close to the manual annotation. The scheme has the advantages of low network structure complexity, light magnitude, rapidness, simplicity and high precision.
In step S250, a blood flow velocity in the blood vessel is determined based on the positions of the contrast agent in the blood vessel corresponding to the plurality of times.
As shown in fig. 6, in the present embodiment, the positions of the contrast agent in the blood vessel are acquired based on a plurality of times, for example, the coronary angiography acquired at each frame in fig. 6, and the positions of the contrast agent therein, for example, gray dots in the figure, are determined by performing the forward wave tracking based on the coronary angiography corresponding to each frame. And then the position of the contrast agent front wave point on each frame of image is obtained. The corresponding length is then determined based on the location of the contrast agent, after which the flow velocity of the blood flow in the blood vessel is determined based on the length.
Determining a blood flow velocity in a blood vessel based on positions of the contrast agent corresponding to a plurality of times, including steps S251 to S252:
s251: a location curve of the contrast agent is generated based on the locations of the contrast agent in the blood vessel corresponding to the plurality of times.
As shown in fig. 7, in the present embodiment, based on the positions of the contrast agent in the blood vessel corresponding to a plurality of times, the spatial resolution and the projection ratio of the image are converted into a three-dimensional space length; the main blood vessel pixel length formed by connecting the contrast agent front wave points obtained frame by frame is converted into the actual three-dimensional space length through the spatial resolution and the projection ratio of the image, and a position curve corresponding to the three-dimensional space length is generated by taking time as an abscissa axis. Wherein the horizontal axis is the corresponding acquisition time of each coronary angiography image, and the vertical axis is the position of the front wave point of the contrast agent, namely the flow length of the contrast agent.
S252: the flow rate of blood flow in the blood vessel is determined based on the slope of the positional linear curve.
In one embodiment of the present application, after determining the location curve of the contrast agent, determining the flow rate of blood flow in the blood vessel based on the slope of the location linear curve comprises: since the coronary artery shows the situation that the shape and the length change continuously along with the time in the radiography due to the change of the cardiac cycle, the position curve can be subjected to curve fitting, and the noise of the length change can be reduced as much as possible in a smooth manner; identifying the position linear curve and determining a speed calculation interval in the position linear curve; the flow velocity of blood flow in the blood vessel is determined based on the slope of the curve in the velocity calculation interval.
Optionally, in this embodiment, the location linear curve may be further identified, and a speed calculation interval therein may be determined, including: acquiring local speed by a least square method based on the slope of the position linear curve; and expanding and obtaining a speed calculation section corresponding to the target time based on the target time corresponding to the maximum speed in the local speeds. In the embodiment, the local velocity is grabbed by a least square method, the slope of a fitting curve is adjusted, the maximum point position of the velocity is automatically obtained, the maximum point position is used as the center to be expanded for half of a cardiac cycle, and the fitting velocity of the least square method in the expanded region is automatically given.
Optionally, in this embodiment, the extension area may be manually adjusted to reach the expected calculation area, and the least square fitting speed value may be displayed in real time. By the manual adjustment method, good interaction experience can be provided, and operation complexity is reduced.
The following describes embodiments of the apparatus of the present application that may be used to perform the method of determining vascular flow in the above-described embodiments of the present application. It will be appreciated that the apparatus may be a computer program (including program code) running in a computer device, for example the apparatus being an application software; the device can be used for executing corresponding steps in the method provided by the embodiment of the application. For details not disclosed in the device embodiments of the present application, please refer to the embodiment of the method for determining a blood vessel flow rate described in the present application.
As shown in fig. 8, in the above-mentioned scheme, based on a coronary angiography image, position deformation information corresponding to a blood vessel in the coronary angiography image at a next acquisition time is predicted based on a machine learning method, then, based on the position deformation information and the blood vessel in the coronary angiography image, a blood vessel image corresponding to the next acquisition time is determined, a front wave tracking of a contrast agent is performed based on the machine learning method, a position of the contrast agent in the blood vessel is predicted, a position of the contrast agent corresponding to the next time is determined, a time blood vessel length curve is automatically generated based on the positions of the contrast agent in the blood vessel corresponding to a plurality of times, and an automatic smoothing, an automatic speed selecting calculation space and a manual adjustment calculation interval are performed on the curve to determine a blood flow velocity in the blood vessel. The blood flow velocity is determined by performing front wave tracking on the contrast agent by predicting the position of the blood vessel corresponding to the next acquisition time, so that the accuracy and the efficiency of blood flow velocity detection are improved.
Fig. 9 shows a block diagram of an apparatus for determining vascular flow velocity according to one embodiment of the present application.
Referring to fig. 9, an apparatus 900 for determining a vascular flow rate according to one embodiment of the present application includes:
an acquisition unit 910 for acquiring a coronary angiography image; a deformation unit 920, configured to predict position deformation information corresponding to a blood vessel in the coronary angiography image at a next acquisition time based on the coronary angiography image; a blood vessel unit 930, configured to determine a blood vessel image corresponding to the next acquisition time based on the position deformation information and a blood vessel in the coronary angiography image; a contrast unit 940, configured to predict a location of a contrast agent in a blood vessel based on the blood vessel image, and determine a location of the contrast agent corresponding to the next time; a flow rate unit 950 for determining a flow rate of blood flow in a blood vessel based on positions of contrast agent in the blood vessel corresponding to a plurality of moments.
In some embodiments of the present application, based on the foregoing, the apparatus 900 for determining a vascular flow rate further includes: the identification unit is used for identifying the blood vessel type in the coronary angiography image based on the blood vessel classification network obtained through pre-training; the blood vessel classification network is obtained by training a neural network based on a coronary image sample; the vessel types include left anterior descending branch, left circumflex branch, and right coronary artery.
In some embodiments of the present application, based on the foregoing solution, the deformation unit 920 includes: the deformation model unit is used for acquiring a deformation prediction model corresponding to the blood vessel type based on the blood vessel type; the deformation prediction model is obtained by training a neural network through coronary image samples corresponding to each blood vessel type; the model prediction unit is used for inputting the coronary angiography image into the deformation prediction model for prediction and outputting position deformation information corresponding to the blood vessel in the coronary angiography image at the next acquisition time.
In some embodiments of the present application, based on the foregoing solution, the imaging unit 940 is configured to obtain a front wave tracking model corresponding to the blood vessel type based on the blood vessel type; the front wave tracking model is obtained by training a neural network through angiography agent samples corresponding to each blood vessel type; and inputting the blood vessel image into the front wave tracking model for prediction, and outputting the position of the contrast agent corresponding to the next moment.
In some embodiments of the present application, based on the foregoing solution, the apparatus 900 for determining a vascular flow rate is further configured to perform a multi-scale white cap operation on the coronary angiography image based on a set structural operator, and extract a bright region in the coronary angiography image; performing multi-scale black cap operation on the coronary angiography image based on a set structural operator, and extracting a dark region in the coronary angiography image; and performing image synthesis based on the bright area and the dark area to generate an enhanced coronary angiography image.
In some embodiments of the present application, based on the foregoing, the flow rate unit 950 includes: a curve unit for generating a position curve of the contrast agent based on the positions of the contrast agent in the blood vessel corresponding to the plurality of times; and a slope unit for determining a blood flow velocity in the blood vessel based on a slope of the position linear curve.
In some embodiments of the present application, based on the foregoing solution, the curve unit is configured to convert, based on the positions of the contrast agents corresponding to the plurality of moments in time in the blood vessel, to a three-dimensional space length through spatial resolution and projection ratio of the image; and generating a position curve corresponding to the three-dimensional space length by taking time as an abscissa axis based on the three-dimensional space length corresponding to the multiple moments.
In some embodiments of the present application, based on the foregoing scheme, the slope unit includes: the smoothing unit is used for carrying out smoothing processing on the position curve and generating a position linear curve based on time; the interval unit is used for identifying the position linear curve and determining a speed calculation interval in the position linear curve; and a curve slope unit for determining a blood flow velocity in the blood vessel based on the curve slope in the velocity calculation section.
In some embodiments of the present application, based on the foregoing scheme, the interval unit is configured to obtain the local velocity by a least square method based on a slope of the position linear curve; and expanding to obtain a speed calculation interval corresponding to the target time based on the target time corresponding to the maximum speed in the local speeds.
Fig. 10 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
It should be noted that, the computer system 1000 of the electronic device shown in fig. 10 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 10, the computer system 1000 includes a central processing unit (Central Processing Unit, CPU) 1001 that can perform various appropriate actions and processes, such as performing the method described in the above embodiment, according to a program stored in a Read-Only Memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access Memory (Random Access Memory, RAM) 1003. In the RAM 1003, various programs and data required for system operation are also stored. The CPU 1001, ROM 1002, and RAM 1003 are connected to each other by a bus 1004. An Input/Output (I/O) interface 1005 is also connected to bus 1004.
The following components are connected to the I/O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output portion 1007 including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and a speaker; a storage portion 1008 including a hard disk or the like; and a communication section 1009 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the internet. The drive 1010 is also connected to the I/O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed on the drive 1010 as needed, so that a computer program read out therefrom is installed into the storage section 1008 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 1009, and/or installed from the removable medium 1011. When executed by a Central Processing Unit (CPU) 1001, the computer program performs various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by means of software, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from the computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the methods provided in the various alternative implementations described above.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, in accordance with embodiments of the present application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (8)
1. A method of determining a vascular flow rate, comprising:
acquiring a coronary angiography image;
based on a blood vessel classification network obtained through pre-training, identifying the blood vessel type in the coronary angiography image, wherein the blood vessel classification network is obtained through training a neural network based on a coronary angiography image sample; the vessel type comprises a left anterior descending branch, a left circumflex branch and a right coronary artery;
based on the blood vessel type, acquiring a deformation prediction model corresponding to the blood vessel type; the deformation prediction model is obtained by training a neural network through coronary image samples corresponding to each blood vessel type;
inputting the coronary angiography image into the deformation prediction model for prediction, and outputting position deformation information corresponding to a blood vessel in the coronary angiography image at the next acquisition moment;
determining a blood vessel image corresponding to the next acquisition time based on the position deformation information and the blood vessels in the coronary angiography image;
based on the blood vessel image, predicting the position of the contrast agent in the blood vessel, and determining the position of the contrast agent corresponding to the next moment;
a flow rate of blood flow in a blood vessel is determined based on the locations of contrast agent in the blood vessel corresponding to a plurality of times.
2. The method of claim 1, wherein predicting the location of the contrast agent in the vessel based on the vessel image, determining the location of the corresponding contrast agent at the next time, comprises:
acquiring a front wave tracking model corresponding to the blood vessel type based on the blood vessel type; the front wave tracking model is obtained by training a neural network through angiography agent samples corresponding to each blood vessel type;
and inputting the blood vessel image into the front wave tracking model for prediction, and outputting the position of the contrast agent corresponding to the next moment.
3. The method of claim 1, wherein after the acquiring the coronary angiography image, further comprising:
performing multi-scale white cap operation on the coronary angiography image based on a set structural operator, and extracting a bright region in the coronary angiography image;
performing multi-scale black cap operation on the coronary angiography image based on a set structural operator, and extracting a dark region in the coronary angiography image;
and performing image synthesis based on the bright area and the dark area to generate an enhanced coronary angiography image.
4. The method of claim 1, wherein determining a flow rate of blood flow in the vessel based on locations of contrast agent corresponding to a plurality of moments in time comprises:
Generating a position curve of the contrast agent based on the positions of the contrast agent in the blood vessel corresponding to the plurality of moments;
a flow rate of blood flow in the blood vessel is determined based on a slope of the positional linear curve.
5. The method of claim 4, wherein generating a location profile of the contrast agent based on the locations of the contrast agent in the blood vessel for a plurality of time instants, comprises:
converting to a three-dimensional space length through the spatial resolution and the projection ratio of the image based on the positions of the contrast agent in the blood vessel corresponding to the plurality of moments;
and generating a position curve corresponding to the three-dimensional space length by taking time as an abscissa axis based on the three-dimensional space length corresponding to the multiple moments.
6. The method of claim 4, wherein determining a blood flow rate in the vessel based on a slope of the positional linear curve comprises:
smoothing the position curve to generate a position linear curve based on time;
identifying the position linear curve, and determining a speed calculation interval in the position linear curve;
determining a blood flow velocity in the blood vessel based on a slope of the curve in the velocity calculation interval.
7. The method of claim 6, wherein identifying the location linear curve, determining a velocity calculation interval therein, comprises:
Acquiring local speed by a least square method based on the slope of the position linear curve;
and expanding to obtain a speed calculation interval corresponding to the target time based on the target time corresponding to the maximum speed in the local speeds.
8. A device for determining a vascular flow rate, comprising:
an acquisition unit for acquiring a coronary angiography image;
the identification unit is used for identifying the blood vessel type in the coronary angiography image based on a blood vessel classification network obtained through pre-training, wherein the blood vessel classification network is obtained through training a neural network based on a coronary image sample; the vessel type comprises a left anterior descending branch, a left circumflex branch and a right coronary artery;
the deformation unit is used for acquiring a deformation prediction model corresponding to the blood vessel type based on the blood vessel type; the deformation prediction model is obtained by training a neural network through coronary image samples corresponding to each blood vessel type; inputting the coronary angiography image into the deformation prediction model for prediction, and outputting position deformation information corresponding to a blood vessel in the coronary angiography image at the next acquisition moment; the blood vessel unit is used for determining a blood vessel image corresponding to the next acquisition time based on the position deformation information and blood vessels in the coronary angiography image;
A contrast unit, configured to predict a position of a contrast agent in a blood vessel based on the blood vessel image, and determine a position of the contrast agent corresponding to the next time;
and the flow rate unit is used for determining the blood flow velocity in the blood vessel based on the positions of the contrast agent in the blood vessel corresponding to a plurality of moments.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110120550.7A CN112785580B (en) | 2021-01-28 | 2021-01-28 | Method and device for determining vascular flow velocity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110120550.7A CN112785580B (en) | 2021-01-28 | 2021-01-28 | Method and device for determining vascular flow velocity |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112785580A CN112785580A (en) | 2021-05-11 |
| CN112785580B true CN112785580B (en) | 2024-02-02 |
Family
ID=75759501
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110120550.7A Active CN112785580B (en) | 2021-01-28 | 2021-01-28 | Method and device for determining vascular flow velocity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112785580B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114170114B (en) * | 2021-12-14 | 2022-11-15 | 北京柏惠维康科技股份有限公司 | Method and device for enhancing spine CT image and spine surgical robot |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101779967A (en) * | 2009-01-21 | 2010-07-21 | 重庆医科大学超声影像学研究所 | Method for directly measuring blood flowing velocity by using ultrasound microbubble (microsphere) contrast agent |
| JP2015097724A (en) * | 2013-11-20 | 2015-05-28 | 株式会社東芝 | Blood vessel analysis device and blood vessel analysis program |
| CN110448319A (en) * | 2018-05-08 | 2019-11-15 | 博动医学影像科技（上海）有限公司 | Based on radiography image and blood flow velocity calculation method coronarius |
| CN110786842A (en) * | 2019-11-04 | 2020-02-14 | 苏州润迈德医疗科技有限公司 | Method, device, system and storage medium for measuring diastolic blood flow velocity |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9119540B2 (en) * | 2010-09-16 | 2015-09-01 | Siemens Aktiengesellschaft | Method and system for non-invasive assessment of coronary artery disease |
| US11191503B2 (en) * | 2018-07-17 | 2021-12-07 | International Business Machines Corporation | Fluid-injector for a simultaneous anatomical and fluid dynamic analysis in coronary angiography |
-
2021
- 2021-01-28 CN CN202110120550.7A patent/CN112785580B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101779967A (en) * | 2009-01-21 | 2010-07-21 | 重庆医科大学超声影像学研究所 | Method for directly measuring blood flowing velocity by using ultrasound microbubble (microsphere) contrast agent |
| JP2015097724A (en) * | 2013-11-20 | 2015-05-28 | 株式会社東芝 | Blood vessel analysis device and blood vessel analysis program |
| CN110448319A (en) * | 2018-05-08 | 2019-11-15 | 博动医学影像科技（上海）有限公司 | Based on radiography image and blood flow velocity calculation method coronarius |
| CN110786842A (en) * | 2019-11-04 | 2020-02-14 | 苏州润迈德医疗科技有限公司 | Method, device, system and storage medium for measuring diastolic blood flow velocity |
Non-Patent Citations (2)
| Title |
|---|
| 冠脉造影数字跟踪技术测量冠脉血流速度的临床研究;陈兴新;骆秉铨;杨瑞华;陈莉莉;;生物医学工程学杂志(第02期);第295-296页第2-3节 * |
| 冠脉造影数字跟踪法测定冠脉血流速度与Doppler血流速度相关分析;骆秉铨, 陈兴新, 钱菊英, 张义勤, 杨瑞华, 石怀林, 夏项, 葛均波;中国微循环(第03期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112785580A (en) | 2021-05-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10755411B2 (en) | Method and apparatus for annotating medical image | |
| CN107665736B (en) | Method and apparatus for generating information | |
| CN107886508B (en) | Differential subtraction method and medical image processing method and system | |
| US11954864B2 (en) | Medical image segmentation method, image segmentation method, and related apparatus and system | |
| CN112184888A (en) | Three-dimensional blood vessel modeling method and device | |
| US20100004526A1 (en) | Abnormality finding in projection images | |
| KR102228087B1 (en) | Method and apparatus for segmentation of specific cartilage in medical image | |
| CN110956632B (en) | Method and device for automatically detecting pectoralis major region in molybdenum target image | |
| JP2023540910A (en) | Connected Machine Learning Model with Collaborative Training for Lesion Detection | |
| US11684333B2 (en) | Medical image analyzing system and method thereof | |
| US10878564B2 (en) | Systems and methods for processing 3D anatomical volumes based on localization of 2D slices thereof | |
| CN111223158B (en) | Artifact correction method for heart coronary image and readable storage medium | |
| CN112785580B (en) | Method and device for determining vascular flow velocity | |
| CN110136107B (en) | Automatic analysis method based on DSSD and time domain constraint X-ray coronary angiography sequence | |
| CN112801999B (en) | Method and device for determining heart coronary artery dominance | |
| CN116681716B (en) | Method, device, equipment and storage medium for dividing intracranial vascular region of interest | |
| CN116245832B (en) | Image processing method, device, equipment and storage medium | |
| Lainé et al. | Carotid artery wall segmentation in ultrasound image sequences using a deep convolutional neural network | |
| Lin et al. | A geometry-constrained deformable attention network for aortic segmentation | |
| Deuerling-Zheng et al. | Motion compensation in digital subtraction angiography using graphics hardware | |
| US20230103262A1 (en) | Image processing method and device | |
| CN115861189A (en) | Image registration method and device, electronic equipment and storage medium | |
| CN111539926B (en) | Image detection method and device | |
| CN113256651A (en) | Model training method and device, and image segmentation method and device | |
| Giordano et al. | Classification of hemodynamically significant stenoses from dynamic CT perfusion and CTA myocardial territories |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |