CN114049282B - Coronary artery construction method, device, terminal and storage medium - Google Patents
Coronary artery construction method, device, terminal and storage medium Download PDFInfo
- Publication number
- CN114049282B CN114049282B CN202210015014.5A CN202210015014A CN114049282B CN 114049282 B CN114049282 B CN 114049282B CN 202210015014 A CN202210015014 A CN 202210015014A CN 114049282 B CN114049282 B CN 114049282B
- Authority
- CN
- China
- Prior art keywords
- coronary artery
- cloud data
- point cloud
- tissue density
- point
- 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
- 210000004351 coronary vessel Anatomy 0.000 title claims abstract description 272
- 238000010276 construction Methods 0.000 title claims abstract description 14
- 210000000115 thoracic cavity Anatomy 0.000 claims abstract description 26
- 238000003325 tomography Methods 0.000 claims abstract description 17
- 238000004364 calculation method Methods 0.000 claims description 56
- 238000000034 method Methods 0.000 claims description 51
- 230000015654 memory Effects 0.000 claims description 25
- 238000012545 processing Methods 0.000 claims description 19
- 230000011218 segmentation Effects 0.000 claims description 19
- 210000001519 tissue Anatomy 0.000 description 84
- 238000010586 diagram Methods 0.000 description 16
- 210000004204 blood vessel Anatomy 0.000 description 15
- 238000002591 computed tomography Methods 0.000 description 13
- 238000005516 engineering process Methods 0.000 description 13
- 238000004891 communication Methods 0.000 description 8
- 238000000605 extraction Methods 0.000 description 8
- 230000017531 blood circulation Effects 0.000 description 5
- 230000008439 repair process Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 208000029078 coronary artery disease Diseases 0.000 description 4
- 201000010099 disease Diseases 0.000 description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 210000000056 organ Anatomy 0.000 description 3
- 208000031481 Pathologic Constriction Diseases 0.000 description 2
- 238000002583 angiography Methods 0.000 description 2
- 230000036770 blood supply Effects 0.000 description 2
- 210000000988 bone and bone Anatomy 0.000 description 2
- 238000003759 clinical diagnosis Methods 0.000 description 2
- 239000002872 contrast media Substances 0.000 description 2
- 238000002059 diagnostic imaging Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 238000012805 post-processing Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 208000037804 stenosis Diseases 0.000 description 2
- 230000036262 stenosis Effects 0.000 description 2
- 238000001356 surgical procedure Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000000038 chest Anatomy 0.000 description 1
- 238000002586 coronary angiography Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000002526 effect on cardiovascular system Effects 0.000 description 1
- 230000000004 hemodynamic effect Effects 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000002595 magnetic resonance imaging Methods 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- 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
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
The embodiment of the application provides a coronary artery construction method, a terminal and a storage medium, wherein the coronary artery construction method comprises the following steps: calculating a dynamic threshold value based on first point cloud data of the coronary artery reconstructed by the collected multiple thoracic cavity tomography images and based on the first point cloud data; determining a predicted location of calcified plaque on the first point cloud data based on a dynamic threshold; determining a coronary artery segment where the predicted position is located on the first point cloud data, and generating a cross section along the position of a central point in the coronary artery segment; reconstructing a coronary vessel flow-path region in a cross-section; and constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area.
Description
Technical Field
The present application relates to, but not limited to, the technical field of medical image processing, and in particular, to a method, an apparatus, a terminal and a storage medium for constructing coronary arteries.
Background
In the identification processing technology of medical images, post-processing analysis of digital images such as Computed Tomography (CT) images and Magnetic Resonance Images (MRI) has been widely applied in clinical diagnosis of disease, especially for coronary artery diseases, doctors generally accept to calculate a coronary Flow Reserve (FFR) to measure the degree of disease, and then provide appropriate suggestions to patients, such as whether to cure the coronary artery disease through surgery, and the calculation of FFR is to distribute the branched blood supply through a coronary artery model. Therefore, how to accurately and efficiently extract the coronary artery in the medical image has important significance on FFR calculation.
In the related art, a method for reconstructing coronary arteries is to use a deep learning method or a machine learning method such as region growing, filtering processing or morphology processing to reconstruct coronary arteries from CT images by using centerline guidance, or directly use spatial correlation information to reconstruct coronary arteries, or use measurement to create simulation model matching, and the like. However, in the above method, there is at least an artifact due to calcified plaque, resulting in an inaccurate problem of the finally constructed coronary artery.
Disclosure of Invention
The embodiment of the application provides a coronary artery construction method, a coronary artery construction device, a coronary artery construction terminal and a storage medium, and aims to solve the problem that in the related art, due to artifacts caused by calcified plaques, the finally constructed coronary artery is inaccurate.
The technical scheme of the embodiment of the application is realized as follows:
in a first aspect, an embodiment of the present application provides a method for constructing a coronary artery, the method including:
calculating a dynamic threshold value based on first point cloud data of a coronary artery reconstructed by a plurality of acquired thoracic cavity tomography images and based on the first point cloud data;
determining a predicted location of calcified plaque on the first point cloud data based on the dynamic threshold;
determining, on the first point cloud data, a coronary artery segment in which the predicted location is located, and generating a cross-section along a location of a center point within the coronary artery segment;
reconstructing a coronary vessel flow-path region in the cross-section;
and constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area.
In a second aspect, the present application provides a coronary artery constructing apparatus, including:
the processing module is used for calculating a dynamic threshold value based on first point cloud data of the coronary artery reconstructed by the collected multiple thoracic cavity tomography images and based on the first point cloud data;
a determination module to determine a predicted location of calcified plaque on the first point cloud data based on the dynamic threshold;
the determining module is further configured to determine, on the first point cloud data, a coronary artery segment where the predicted position is located;
a generating module for generating a cross-section along a location of a central point within the coronary segment;
a reconstruction module for reconstructing a coronary vessel flow-path region in the cross-section;
and the construction module is used for constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area.
In a third aspect, an embodiment of the present application provides a terminal, where the terminal includes: a processor, a memory, and a communication bus;
the communication bus is used for realizing communication connection between the processor and the memory;
the processor is used for executing the coronary artery construction program stored in the memory so as to realize the steps of the coronary artery construction method.
In a third aspect, the present embodiments provide a storage medium storing one or more programs, which are executable by one or more processors to implement the steps of the above-mentioned coronary artery construction method.
The application of the embodiment of the application realizes the following beneficial effects: based on the medical coronary artery model, the dynamic threshold is improved to identify calcified plaques in the artery model, the blood vessel flow passage area of the coronary artery section with the calcified plaques is repaired, more accurate basis is provided for establishing an accurate three-dimensional model for the coronary artery, and accurate blood flow distribution is further provided for calculating the FFR.
According to the method, the device, the terminal and the storage medium for constructing the coronary artery, the dynamic threshold value is calculated through the first point cloud data of the coronary artery reconstructed based on the collected multiple thoracic cavity tomography images and based on the first point cloud data; determining a predicted location of calcified plaque on the first point cloud data based on a dynamic threshold; determining a coronary artery segment where the predicted position is located on the first point cloud data, and generating a cross section along the position of a central point in the coronary artery segment; reconstructing a coronary vessel flow-path region in a cross-section; constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area; that is to say, the method and the device adaptively calculate a dynamic threshold corresponding to the thoracic tomography image based on first point cloud data of the coronary artery, further accurately identify the calcified plaque of the three-dimensional coronary artery model red based on the dynamic threshold, and repair a blood vessel flow channel region of a coronary artery segment with the calcified plaque, so as to establish an accurate three-dimensional model for the coronary artery based on the repaired blood vessel flow channel region; therefore, the problem that the finally constructed coronary artery is inaccurate due to artifacts caused by calcified plaques in the related technology is solved, accurate modeling of the coronary artery is improved, accurate blood flow distribution is provided for FFR calculation, and meanwhile, the scheme has good robustness and expandability.
Drawings
Fig. 1 is a schematic flow chart of a method for constructing coronary arteries according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of reconstructed first point cloud data of a coronary artery according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of another method for constructing coronary arteries according to an embodiment of the present disclosure;
fig. 4 is a schematic flow chart of another method for constructing coronary artery according to the embodiment of the present application;
FIG. 5 is a schematic diagram of a predicted location of calcified plaque determined based on a dynamic threshold according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a manually selected location of calcified plaque in a coronary artery provided by an embodiment of the present application;
fig. 7 is a schematic flow chart of a method for constructing coronary arteries according to another embodiment of the present disclosure;
FIG. 8 is a schematic illustration of a point on the central axis of a coronary artery provided by an embodiment of the present application;
FIG. 9 is a schematic illustration of a cross-section generated in a coronary artery segment as provided by an embodiment of the present application;
FIG. 10 is a schematic diagram of a cross-sectional level set segmentation result provided by an embodiment of the present application;
FIG. 11 is a schematic flow chart illustrating another method for constructing coronary arteries according to another embodiment of the present disclosure;
FIG. 12 is a diagram illustrating a level set segmentation result shown in a three-dimensional space according to an embodiment of the present disclosure;
FIG. 13 is a schematic diagram illustrating the result of traversing all target points in the coronary vessel flow-path region in order of distance from the entry point according to an embodiment of the present application;
fig. 14 is a schematic diagram of a coronary artery model obtained based on a repaired blood vessel flow passage region according to an embodiment of the present application;
fig. 15 is a schematic structural diagram of a coronary artery constructing apparatus according to an embodiment of the present disclosure;
fig. 16 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
In the related technology, along with the outbreak of image big data, the image processing technology has the advantages of high processing precision, good reproducibility, high flexibility, strong universality and the like, and plays an increasingly important role in analyzing and identifying the shape of an object by depending on the image processing technology in various fields such as military industry, agriculture, medical treatment and the like. The main idea is to determine the object by key point positioning, and the rendering of an object shape must not leave the extraction of its skeleton.
Generally, the process of acquiring the skeleton of the image is a process of 'thinning' the image, which can effectively reflect the connectivity and topology of the original object shape. The current bone extraction algorithm is to repeat iterative computation from the boundary, uniformly strip the boundary of the graph layer by layer until the innermost one-dimensional bone is left.
The image skeleton extraction technology is a very important transformation in image analysis and shape description, and is an important topological description in image geometry. The skeleton extraction technology is applied to the shape analysis, the feature extraction, the pattern recognition and the like of an image target. The curve skeleton extraction algorithm has been a research hotspot in the fields of virtual navigation, form matching, fingerprint identification, medical image processing and the like.
In the field of medical imaging, various technologies such as color ultrasound cardiovascular imaging, magnetic resonance imaging MRI, Digital Subtraction Angiography (DSA), morphological technology, machine learning technology, and the like are emerging in succession. The digitization degree of the medical image is higher and higher, and the variety is more diversified. Medical imaging techniques have not only provided reconstructive models of various human organs, but also demonstrated hemodynamic changes through time-series blood flow velocity fields. The development of these techniques has greatly improved the efficiency and accuracy of doctor's diagnosis, while also reducing unnecessary surgical risks to the patient.
In the identification processing technology of medical images, post-processing analysis of digital images such as Computed Tomography (CT) images and Magnetic Resonance Images (MRI) has been widely applied in clinical diagnosis of disease, especially for coronary artery diseases, doctors generally accept to calculate a coronary Flow Reserve (FFR) to measure the degree of disease, and then provide appropriate suggestions to patients, such as whether to cure the coronary artery disease through surgery, and the calculation of FFR is to distribute the branched blood supply through a coronary artery model. Therefore, how to accurately and efficiently extract the coronary artery in the medical image has important significance on FFR calculation.
In the related art, a method for reconstructing coronary arteries is to use a deep learning method or a machine learning method such as region growing, filtering processing or morphology processing to reconstruct coronary arteries from CT images by using centerline guidance, or directly use spatial correlation information to reconstruct coronary arteries, or use measurement to create simulation model matching, and the like. However, in the above method, at least, the artifact caused by the calcified plaque and/or the contrast agent insufficiency caused by the blood vessel stenosis exist, which leads to the inaccuracy of the finally constructed coronary artery model, and if the constructed coronary artery model can not have the silence calcified plaque and the resulting blood vessel stenosis, at least, the calculation of the FFR is greatly influenced.
The embodiment of the present application provides a method for constructing a coronary artery, the method being applied to a terminal, and referring to fig. 1, the method includes:
In an embodiment of the present invention, the plurality of thoracic cavity tomographic images may be a plurality of CT images obtained by CT technology, where the CT images may be coronary artery CT images obtained by intravenously injecting a suitable contrast medium and then scanning coronary arteries with a multi-row spiral CT machine. The plurality of thoracic tomography images may also be angiographic images obtained based on coronary angiography techniques; the plurality of thoracic cavity tomographic images may also be angiography images obtained based on an X-ray technology, and it is seen that, in the embodiment of the present application, the plurality of acquired thoracic cavity tomographic images may be determined based on any medical image information capable of representing coronary arteries, and a data source of the plurality of acquired thoracic cavity tomographic images is not specifically limited in the embodiment of the present application, so that the method for constructing coronary arteries provided by the present application may be implemented.
In the embodiment of the application, the first point cloud data of the coronary artery may be data obtained by the terminal performing three-dimensional reconstruction based on a plurality of acquired thoracic cavity tomographic images to obtain an initial coronary artery model and based on the initial coronary artery model. Here, the first point cloud data includes a tissue density value corresponding to each target point in the region where the coronary artery is located.
Here, the point cloud data may be a two-dimensional array of H rows and 3 columns (H is the number of points included in the point cloud data) in a storage form of the spatial coordinates (x, y, z). In the embodiment of the application, firstly, after the terminal acquires the point cloud data, the point cloud array can be converted into a binary three-dimensional matrix with a certain size.
In an embodiment of the application, the dynamic threshold is used for predicting the position of the calcified plaque on the first point cloud data.
In the embodiment of the application, a terminal acquires a plurality of acquired thoracic cavity tomograms, and three-dimensional reconstruction is performed on the basis of the thoracic cavity tomograms to obtain an initial coronary artery model; further, the terminal generates first point cloud data of the reconstructed coronary artery based on the initial coronary artery model, and calculates a dynamic threshold value for predicting a position of a calcified plaque on the first point cloud data based on the first point cloud data.
In one achievable application scenario, referring to fig. 2, fig. 2 shows a schematic diagram of the reconstructed first point cloud data of the coronary artery. Firstly, the terminal acquires a plurality of acquired thoracic cavity tomographic images, and arranges the thoracic cavity tomographic images into a three-dimensional form according to a spatial sequence to obtain a three-dimensional Image3 d; secondly, the terminal processes the three-dimensional Image3d to obtain a reconstructed initial coronary artery model; further, the terminal acquires first point cloud data of the reconstructed coronary artery, namely a reconstruction result of the coronary artery based on the initial coronary artery model. Finally, the terminal calculates a dynamic threshold value for predicting the position of the calcified plaque on the first point cloud data in the region where the Coronary artery is located based on the first point cloud data Coronary.
And 102, determining a predicted position of the calcified plaque on the first point cloud data based on the dynamic threshold.
And 103, determining a coronary artery segment where the predicted position is located on the first point cloud data, and generating a cross section along the position of a central point in the coronary artery segment.
In the embodiment of the present application, the coronary artery segment is a coronary artery segment corresponding to a position where a calcified plaque is located, and it should be noted that the coronary artery includes a right coronary artery proximal end, a right coronary artery middle segment, a right coronary artery distal segment, a right coronary artery posterior descending-artery (PDA), a Left trunk (LM), a Left anterior descending branch proximal segment, a Left anterior descending branch middle segment, a Left anterior descending branch distal end, a first diagonal branch, a second diagonal branch, a Left circumflex branch proximal segment, an obtuse limbal branch, and a Left circumflex branch distal end.
In the embodiment of the application, the terminal determines the predicted position of the calcified plaque on the first point cloud data based on the dynamic threshold under the condition that the dynamic threshold is calculated based on the first point cloud data of the coronary artery reconstructed by the collected multiple thoracic cavity tomography images; further, the terminal determines a coronary artery segment where the predicted position is located on the first point cloud data, namely determines a coronary artery segment where the calcified plaque is located from a plurality of coronary artery segments, and generates a cross section along the position of the central point in the coronary artery segment.
In the embodiment of the present application, reconstructing the coronary artery blood vessel flow channel region in the cross section may be understood as dividing the calcified plaque region in the coronary artery and the blood vessel flow channel region in the coronary artery in the cross section, and reconstructing the coronary artery blood vessel flow channel region in the cross section based on the divided blood vessel flow channel region in the coronary artery.
And 105, constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area.
In this embodiment, the second point cloud data may be understood as point cloud data after the repair of the blood vessel flow channel in the coronary artery.
In the embodiment of the application, under the condition that the terminal reconstructs the coronary artery blood vessel flow passage region in the cross section, second point cloud data of the coronary artery is constructed based on the reconstructed coronary artery blood vessel flow passage region, namely, a target coronary artery model after the blood vessel flow passage is repaired is constructed according to the second point cloud data.
According to the coronary artery construction method provided by the embodiment of the application, the dynamic threshold value is calculated through the first point cloud data of the coronary artery reconstructed based on the collected multiple thoracic cavity tomography images and based on the first point cloud data; determining a predicted location of calcified plaque on the first point cloud data based on a dynamic threshold; determining a coronary artery segment where the predicted position is located on the first point cloud data, and generating a cross section along the position of a central point in the coronary artery segment; reconstructing a coronary vessel flow-path region in a cross-section; constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area; that is to say, the method and the device adaptively calculate a dynamic threshold corresponding to the thoracic tomography image based on first point cloud data of the coronary artery, further accurately identify the calcified plaque of the three-dimensional coronary artery model red based on the dynamic threshold, and repair a blood vessel flow channel region of a coronary artery segment with the calcified plaque, so as to establish an accurate three-dimensional model for the coronary artery based on the repaired blood vessel flow channel region; therefore, the problem that the finally constructed coronary artery is inaccurate due to artifacts caused by calcified plaques in the related technology is solved, accurate modeling of the coronary artery is improved, accurate blood flow distribution is provided for FFR calculation, and meanwhile, the scheme has good robustness and expandability.
The embodiment of the present application provides a method for constructing a coronary artery, the method being applied to a terminal, and referring to fig. 3, the method includes:
and step 201, reconstructing first point cloud data of the coronary artery based on the acquired multiple thoracic cavity tomography images.
In the present embodiment, the tissue density value may be understood as an absorption rate of X-rays in the tissue or organ in the region of the coronary artery. Tissue density values may, in turn, be referred to as CT values in Hounsfield Units (HU). Note that, the plaque in the coronary artery can be classified into a soft plaque, a fibrous plaque, and a calcified plaque by classifying the plaque based on the CT value of the tissue or organ, and the soft plaque and the fibrous plaque are collectively referred to as a non-calcified plaque.
And 204, selecting a threshold calculation formula corresponding to the tissue density mean value, and calculating a dynamic threshold.
In the embodiment of the application, after the terminal acquires the tissue density value corresponding to each target point in the first point cloud data, the terminal calculates the mean value of all the tissue density values, and the mean value is used as the tissue density mean value of the region where the coronary artery is located; further, the terminal selects a threshold calculation formula corresponding to the tissue density mean value, and calculates a dynamic threshold for predicting the position of the calcified plaque on the first point cloud data.
In this embodiment of the present application, referring to fig. 4, the step 204 selects a threshold calculation formula corresponding to the tissue density mean value, and calculates the dynamic threshold, which may be implemented by the following steps:
step A1, selecting a threshold value calculation formula corresponding to the tissue density value range based on the tissue density value range to which the tissue density mean value belongs.
In the embodiment of the present application, step a1 selects the threshold calculation formula corresponding to the tissue density value range based on the tissue density value range to which the tissue density mean value belongs, and may be implemented as follows:
and if the tissue density mean value is less than or equal to the first parameter, selecting a first threshold calculation formula.
And if the tissue density mean value is larger than the first parameter and smaller than the second parameter, selecting a second threshold calculation formula.
And if the tissue density mean value is larger than or equal to the second parameter, selecting a third threshold calculation formula.
Wherein, the first threshold value is calculated by the formula,
(ii) a The second threshold value is calculated by the formula,
(ii) a The third threshold value is calculated by the formula,
。
wherein,
in order to be a dynamic threshold value, the threshold value is,
the mean value of the tissue density is shown as the average value,
is a first parameter of the plurality of parameters,
is a second parameter that is a function of,
as a third parameter of the number of the first and second parameters,
as the fourth parameter, the first parameter is,
is the third parameter, and is the third parameter,
are all positive numbers.
In the embodiment of the application, the first parameter, the second parameter, the third parameter, the fourth parameter and the fifth parameter are obtained by analyzing and processing a large amount of case data to obtain the empirical threshold range.
And step A2, calculating the dynamic threshold value based on the tissue density mean value and the threshold value calculation formula.
In the embodiment of the present application, the threshold calculation formula includes a first threshold calculation formula, a second threshold calculation formula, and a third threshold calculation formula.
In a practical application scenario, the first parameter is used
The second parameter
The third parameter
Fourth parameter
Fifth parameter
The description is given for the sake of example. Terminal is based on first point cloud data of multiple thorax tomography image reconsitution's coronary artery of gathering
And then, acquiring the tissue density value corresponding to each target point in the first point cloud data, calculating the mean value of all the tissue density values, and determining the mean value as the tissue density mean value of the region where the coronary artery is located. Here, the tissue density mean value can be calculated by the following formula:
wherein,
the mean value of the tissue density is shown as the average value,
representing a function of the mean value of all tissue density values in the calculation matrix,
is the first point cloud data.
Further, the terminal determines the mean tissue density
The tissue density value range is determined whether the tissue density mean value is less than or equal to the first parameter
Or whether the mean tissue density is greater than the first parameter
Less than the second parameter
Or judging whether the tissue density mean value is greater than or equal to the second parameter
(ii) a If the terminal determines that the tissue density mean value is less than or equal to the first parameter, a first threshold calculation formula is selected
(ii) a If the terminal determines that the tissue density mean value is larger than the first parameter and smaller than the second parameter, a second threshold calculation formula is selected
(ii) a If the terminal determines that the tissue density mean value is larger than or equal to the second parameter, a third threshold calculation formula is selected
. In this way, a dynamic threshold is dynamically selected according to the tissue density mean value in the coronary artery, so that the position of the calcified plaque on the first point cloud data can be accurately and quickly predicted; meanwhile, the method for selecting the dynamic threshold is realized under the completely automatic process without manual operation in the whole processIntervening or other operations, and avoids using complex calculation methods such as deep learning, which leads to uncontrollable and unstable results. Meanwhile, the method has the advantages of high calculation speed, high accuracy, reliability and expandability, and is easy to apply to various fields.
Here, referring to fig. 5, fig. 5 is a schematic view showing a predicted location of a calcified plaque determined based on a dynamic threshold.
In other embodiments of the present application, the terminal may also predict the location of the plaque by using a pre-specified or locally selected threshold. In one case, the terminal manually selects a corresponding position according to the reconstructed first point cloud data of the coronary artery and the shape, contour and high-probability position where calcified plaque is likely to appear in the three-dimensional space, and obtains the three-dimensional coordinates of the position. Referring to fig. 6, fig. 6 shows three-dimensional coordinates such as (206, 358, 157) of the position of a distinct raised calcified plaque in the right of the manually selected coronary arteries.
In the embodiment of the present application, referring to fig. 7, in step 206, determining a coronary artery segment where the predicted position is located on the first point cloud data may be implemented by the following steps:
and step B1, determining a central line point set and segmentation information of the coronary artery based on the first point cloud data.
Wherein the centerline point set comprises points on a central axis of the first point cloud data.
In the embodiment of the present application, the central line point set may be understood as a point on the central axis of the coronary artery directly extracted by using a coronary artery skeleton extraction algorithm, and the central line point set may also be understood as a point on the central axis of all point cloud data representing the coronary artery in a tree structure, for example, a point on the central axis of the coronary artery is directly extracted by using a three-dimensional central line extraction algorithm in a script language, such as Python language, as shown in fig. 8, where fig. 8 is a schematic diagram of a point on the central axis of the coronary artery. A point on the central axis of the coronary artery may also be referred to herein as a keel point making up the skeleton.
In the embodiment of the present application, the segmentation information of the coronary artery is that the coronary artery is named by medical nomenclature to achieve the purpose of segmentation.
And step B2, based on the segmentation information, determining the position of the central point closest to the predicted position as the coronary artery segment of the predicted position from the central line point set.
In the embodiment of the application, after the terminal determines the center line point set and the segment information of the coronary artery based on the first point cloud data, based on the segment information, the terminal determines, from the center line point set, a coronary artery segment where the central point closest to the predicted position is located as the predicted position, that is, the terminal locates the coronary artery segment where the calcified plaque currently exists. Referring to fig. 5, fig. 5 is a schematic diagram illustrating the determination of the predicted positions of calcified plaque by dynamic threshold, where there are three predicted positions P1, P2 and P3 on the first point cloud data, and the three predicted positions are respectively in the left anterior descending branch and the right coronary artery.
In the embodiment of the application, the terminal determines a center line point set and segmentation information of the coronary artery based on the first point cloud data, and after determining a position of a center point closest to the predicted position as a coronary artery segment where the predicted position is located from the center line point set based on the segmentation information, a plurality of cross sections are generated along the position of the center point in the coronary artery segment, and the range of the cross sections includes the cross section range of the coronary artery at the predicted position. Referring to fig. 9, a in fig. 9 shows a schematic view of a cross-section generated in a coronary artery segment, B in fig. 9 shows a schematic view of a cross-section generated in a coronary artery segment defining a region in which a coronary artery is located, and C in fig. 9 shows a schematic view of a longitudinal section generated in a coronary artery segment.
And step 207, removing the target point corresponding to the calcified plaque in the cross section to obtain the reconstructed coronary artery blood vessel flow channel region.
In the embodiment of the application, a terminal determines a coronary artery segment where a predicted position is located on first point cloud data, generates a cross section along the position of a central point in the coronary artery segment, and then performs image segmentation on each cross section by using a Distance Regularized Level Set (DRLSE) method to obtain a calcified plaque area and a coronary artery blood vessel flow channel area; further, the target point of the area where the calcified plaque is located in the cross section is removed, and a reconstructed coronary artery blood vessel flow passage area, namely the reconstructed coronary artery blood vessel flow passage area in the cross section, is obtained. Referring to fig. 10, a in fig. 10 shows a cross-sectional level set segmentation result, and B in fig. 10 shows a level set segmentation result of a region where a defined coronary artery is located.
And 208, constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area.
In this embodiment of the application, referring to fig. 11, the step 208 is to construct second point cloud data of a coronary artery based on a coronary artery blood vessel flow channel region, and may be implemented by the following steps:
and step C1, traversing all target points in the coronary artery blood vessel flow passage area according to the sequence from near to far away from the entry point by taking the position of the entry point in the coronary artery blood vessel flow passage area as a starting point to obtain the target point cloud data of the closed area of the coronary artery segment.
And C2, replacing the point cloud data corresponding to the coronary artery segment of the first point cloud data with target point cloud data to obtain second point cloud data.
In the embodiment of the present application, the coronary artery blood vessel flow passage region is the reconstructed coronary artery blood vessel flow passage region.
In the embodiment of the application, a terminal determines an entry point in a coronary artery blood vessel flow passage area, and traverses all target points in the coronary artery blood vessel flow passage area according to a sequence from near to far away from the entry point by taking the position of the entry point as a starting point to obtain target point cloud data of a closed area of a coronary artery segment; further, the terminal acquires point cloud data corresponding to a coronary artery segment of the first point cloud data, replaces the point cloud data corresponding to the coronary artery segment with target point cloud data to obtain second point cloud data, and further constructs a target coronary artery model after vessel channel repair based on the second point cloud data, as shown in fig. 12, 13 and 14, fig. 12 is a schematic diagram of a level set segmentation result shown in a three-dimensional space, fig. 13 is a schematic diagram of a result of traversing all target points in a coronary artery vessel channel region according to a sequence from an entry point, fig. 14 is a schematic diagram of the second point cloud data of a coronary artery closed region, and fig. 14 is a schematic diagram of the target coronary artery model obtained based on the repaired vessel channel region.
As can be seen from the above, in the embodiment of the application, the terminal calculates the tissue density mean value of the coronary artery based on the first point cloud data of the coronary artery, adaptively calculates the dynamic threshold corresponding to the thoracic tomography image based on the range to which the tissue density mean value belongs, and further accurately identifies the red calcified plaque of the three-dimensional coronary artery model based on the dynamic threshold, further, generates a plurality of cross sections along the position of the central point for the coronary artery segment with the calcified plaque, segments the calcified plaque region and the blood vessel flow channel region in the cross sections, removes the target point corresponding to the calcified plaque region in the cross sections to repair the blood vessel flow channel region of the coronary artery, and further establishes an accurate three-dimensional model for the coronary artery based on the repaired blood vessel flow channel region; therefore, the problem that the finally constructed coronary artery is inaccurate due to artifacts caused by calcified plaques in the related technology is solved, accurate modeling of the coronary artery is improved, accurate blood flow distribution is provided for FFR calculation, and meanwhile, the scheme has good robustness and expandability.
It should be noted that, for the descriptions of the same steps and the same contents in this embodiment as those in other embodiments, reference may be made to the descriptions in other embodiments, which are not described herein again.
Based on the foregoing embodiments, the present application provides a coronary artery constructing apparatus, which may be used to implement a coronary artery constructing method provided in correspondence with fig. 1, 3 to 4, 7 and 11, and as shown in fig. 15, the coronary artery constructing apparatus 15 includes:
the processing module 1501 is configured to calculate a dynamic threshold based on first point cloud data of the coronary artery reconstructed from the acquired multiple thoracic cavity tomographic images and based on the first point cloud data;
a determination module 1502 for determining a predicted location of a calcified plaque on the first point cloud data based on a dynamic threshold;
a determining module 1502, configured to determine, on the first point cloud data, a coronary artery segment where the predicted position is located;
a generating module 1503 for generating a cross section along a position of a center point within the coronary segment;
a reconstruction module 1504 for reconstructing a coronary vessel flow-path region in a cross-section;
a construction module 1505 is used for constructing second point cloud data of coronary artery based on the coronary artery blood vessel flow passage area.
In other embodiments of the present application, the processing module 1501 is further configured to obtain a tissue density value corresponding to each target point in the first point cloud data; determining the mean value of all tissue density values, namely the mean value of the tissue density of the region where the coronary artery is located; and selecting a threshold value calculation formula corresponding to the tissue density mean value, and calculating the dynamic threshold value.
In other embodiments of the present application, the processing module 1501 is further configured to select a threshold calculation formula corresponding to the tissue density value range based on the tissue density value range to which the tissue density mean value belongs; and calculating the dynamic threshold value based on the tissue density mean value and a threshold value calculation formula.
In other embodiments of the present application, the processing module 1501 is further configured to select a first threshold calculation formula if the tissue density mean is less than or equal to the first parameter; if the tissue density mean value is larger than the first parameter and smaller than the second parameter, selecting a second threshold calculation formula; if the tissue density mean value is larger than or equal to the second parameter, selecting a third threshold calculation formula; wherein the first threshold value calculation formulaIn order to realize the purpose,
(ii) a The second threshold value is calculated by the formula,
(ii) a The third threshold value is calculated by the formula,
(ii) a Wherein,
in order to be a dynamic threshold value, the threshold value is set,
the mean value of the tissue density is shown as the average value,
is a first parameter of the plurality of parameters,
as the second parameter, the parameter is,
as the third parameter, the parameter is,
as the fourth parameter, the first parameter is,
is the third parameter, and is the third parameter,
are all positive numbers.
In other embodiments of the present application, the determining module 1502 is further configured to determine, from all tissue density values, a location where a target point with a tissue density value greater than a dynamic threshold is located, as a predicted location of a calcified plaque on the first point cloud data.
In other embodiments of the present application, the determining module 1502 is further configured to determine a centerline point set and segmentation information of the coronary artery based on the first point cloud data; the central line point set comprises points on a central line of the first point cloud data; and based on the segmentation information, determining the position of the central point closest to the predicted position as the coronary artery segment of the predicted position from the central line point set.
In other embodiments of the present application, the reconstruction module 1504 is further configured to remove a target point corresponding to a calcified plaque in a cross section to obtain a reconstructed coronary artery blood vessel flow channel region; the constructing module 1505 is further configured to traverse all target points in the coronary artery blood vessel flow channel region according to a sequence from near to far from the entry point by using a position of the entry point in the coronary artery blood vessel flow channel region as a starting point to obtain target point cloud data of a closed region of the coronary artery segment; and replacing the point cloud data corresponding to the coronary artery segment of the first point cloud data with target point cloud data to obtain second point cloud data.
Embodiments of the present application provide a terminal, which may be used to implement a method for constructing a coronary artery according to the embodiments corresponding to fig. 1, 3 to 4, 7 and 11, and as shown in fig. 16, the terminal 16 (the terminal 16 in fig. 16 corresponds to the device 15 for constructing a coronary artery in fig. 15) includes: a processor 1601, a memory 1602, and a communication bus 1603, wherein:
the communication bus 1603 is used to enable communication between the processor 1601 and the memory 1602.
calculating a dynamic threshold value based on first point cloud data of the coronary artery reconstructed by the collected multiple thoracic cavity tomography images and based on the first point cloud data;
determining a predicted location of calcified plaque on the first point cloud data based on a dynamic threshold;
determining a coronary artery segment where the predicted position is located on the first point cloud data, and generating a cross section along the position of a central point in the coronary artery segment;
reconstructing a coronary vessel flow-path region in a cross-section;
and constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area.
In other embodiments of the present application, the processor 1601 is configured to execute the program stored in the memory 1602 to implement the following steps:
acquiring an organization density value corresponding to each target point in the first point cloud data; determining the mean value of all tissue density values, namely the mean value of the tissue density of the region where the coronary artery is located; and selecting a threshold value calculation formula corresponding to the tissue density mean value, and calculating the dynamic threshold value.
In other embodiments of the present application, the processor 1601 is configured to execute the program stored in the memory 1602 to implement the following steps:
selecting a threshold calculation formula corresponding to the tissue density value range based on the tissue density value range to which the tissue density mean value belongs; and calculating the dynamic threshold value based on the tissue density mean value and a threshold value calculation formula.
In other embodiments of the present application, the processor 1601 is configured to execute the program stored in the memory 1602 to implement the following steps:
if the tissue density mean value is less than or equal to the first parameter, selecting a first threshold calculation formula; if the tissue density mean value is larger than the first parameter and smaller than the second parameter, selecting a second threshold calculation formula; if the tissue density mean value is larger than or equal to the second parameter, selecting a third threshold calculation formula; wherein, the first threshold value calculation formula is,
(ii) a The second threshold value is calculated by the formula,
(ii) a The third threshold value is calculated by the formula,
(ii) a Wherein,
in order to be a dynamic threshold value, the threshold value is,
the mean value of the tissue density is shown as the average value,
is a first parameter of the plurality of parameters,
as the second parameter, the parameter is,
as the third parameter, the parameter is,
as the fourth parameter, the first parameter is,
is a fifth parameter that is a function of,
are all positive numbers.
In other embodiments of the present application, the processor 1601 is configured to execute the program stored in the memory 1602 to implement the following steps:
and determining the position of the target point with the tissue density value larger than the dynamic threshold value from all the tissue density values, wherein the target point is the predicted position of the calcified plaque on the first point cloud data.
In other embodiments of the present application, the processor 1601 is configured to execute the program stored in the memory 1602 to implement the following steps:
determining a centerline point set and segmentation information for the coronary artery based on the first point cloud data; the central line point set comprises points on a central line of the first point cloud data; and based on the segmentation information, determining the position of the central point closest to the predicted position as the coronary artery segment of the predicted position from the central line point set.
In other embodiments of the present application, the processor 1601 is configured to execute the program stored in the memory 1602 to implement the following steps:
removing a target point corresponding to the calcified plaque in the cross section to obtain a reconstructed coronary artery blood vessel flow passage area; traversing all target points in the coronary artery blood vessel flow passage region by taking the position of an entry point in the coronary artery blood vessel flow passage region as a starting point according to the sequence from near to far away from the entry point to obtain target point cloud data of a closed region of a coronary artery segment; and replacing the point cloud data corresponding to the coronary artery segment of the first point cloud data with target point cloud data to obtain second point cloud data.
Embodiments of the present application provide a storage medium storing one or more programs executable by one or more processors to perform the steps of:
calculating a dynamic threshold value based on first point cloud data of the coronary artery reconstructed by the collected multiple thoracic cavity tomography images and based on the first point cloud data;
determining a predicted location of calcified plaque on the first point cloud data based on a dynamic threshold;
determining a coronary artery segment where the predicted position is located on the first point cloud data, and generating a cross section along the position of a central point in the coronary artery segment;
reconstructing a coronary vessel flow-path region in a cross-section;
and constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area.
In other embodiments of the present application, the one or more programs are executable by the one or more processors to perform the steps of:
acquiring an organization density value corresponding to each target point in the first point cloud data; determining the mean value of all tissue density values, namely the mean value of the tissue density of the region where the coronary artery is located; and selecting a threshold value calculation formula corresponding to the tissue density mean value, and calculating the dynamic threshold value.
In other embodiments of the present application, the one or more programs are executable by the one or more processors to perform the steps of:
selecting a threshold calculation formula corresponding to the tissue density value range based on the tissue density value range to which the tissue density mean value belongs; and calculating the dynamic threshold value based on the tissue density mean value and a threshold value calculation formula.
In other embodiments of the present application, the one or more programs are executable by the one or more processors to perform the steps of:
if the tissue density mean value is less than or equal to the first parameter, selecting a first threshold calculation formula; if the tissue density mean value is larger than the first parameter and smaller than the second parameter, selecting a second threshold calculation formula; if the tissue density mean value is larger than or equal to the second parameter, selecting a third threshold calculation formula; wherein, the first threshold value calculation formula is,
(ii) a The second threshold value is calculated by the formula,
(ii) a The third threshold value is calculated by the formula,
(ii) a Wherein,
in order to be a dynamic threshold value, the threshold value is,
the mean value of the tissue density is shown as the average value,
is a first parameter of the plurality of parameters,
as the second parameter, the parameter is,
as the third parameter, the parameter is,
as the fourth parameter, the first parameter is,
is the third parameter, and is the third parameter,
are all positive numbers.
In other embodiments of the present application, the one or more programs are executable by the one or more processors to perform the steps of:
and determining the position of the target point with the tissue density value larger than the dynamic threshold value from all the tissue density values, wherein the target point is the predicted position of the calcified plaque on the first point cloud data.
In other embodiments of the present application, the one or more programs are executable by the one or more processors to perform the steps of:
determining a centerline point set and segmentation information for the coronary artery based on the first point cloud data; the central line point set comprises points on a central axis of the first point cloud data; and based on the segmentation information, determining the position of the central point closest to the predicted position as the coronary artery segment of the predicted position from the central line point set.
In other embodiments of the present application, the one or more programs are executable by the one or more processors to perform the steps of:
removing a target point corresponding to the calcified plaque in the cross section to obtain a reconstructed coronary artery blood vessel flow passage area; traversing all target points in the coronary artery blood vessel flow passage region by taking the position of an entry point in the coronary artery blood vessel flow passage region as a starting point according to the sequence from near to far away from the entry point to obtain target point cloud data of a closed region of a coronary artery segment; and replacing the point cloud data corresponding to the coronary artery segment of the first point cloud data with target point cloud data to obtain second point cloud data.
The computer storage medium/Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a magnetic Random Access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read-Only Memory (CD-ROM); but may also be various terminals such as mobile phones, computers, tablet devices, personal digital assistants, etc., that include one or any combination of the above-mentioned memories.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
In addition, all functional units in the embodiments of the present application may be integrated into one processing module, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit. Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media capable of storing program codes, such as a removable Memory device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, and an optical disk.
The features disclosed in the several method or apparatus embodiments provided herein may be combined in any combination to arrive at a new method or apparatus embodiment without conflict.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of constructing a coronary artery, the method comprising:
calculating a dynamic threshold value based on first point cloud data of a coronary artery reconstructed by a plurality of acquired thoracic cavity tomography images and based on the first point cloud data;
determining a predicted location of calcified plaque on the first point cloud data based on the dynamic threshold;
determining, on the first point cloud data, a coronary artery segment in which the predicted location is located, and generating a cross-section along a location of a center point within the coronary artery segment;
removing a target point corresponding to the calcified plaque in the cross section to obtain a reconstructed coronary artery blood vessel flow passage area;
and constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area.
2. The method of claim 1, wherein computing a dynamic threshold based on the first point cloud data comprises:
acquiring a tissue density value corresponding to each target point in the first point cloud data;
determining the mean value of all tissue density values, wherein the mean value is the tissue density mean value of the region where the coronary artery is located;
and selecting a threshold value calculation formula corresponding to the tissue density mean value, and calculating the dynamic threshold value.
3. The method of claim 2, wherein selecting a threshold calculation formula corresponding to the tissue density mean value to calculate the dynamic threshold comprises:
selecting a threshold calculation formula corresponding to the tissue density value range based on the tissue density value range to which the tissue density mean value belongs;
calculating the dynamic threshold based on the tissue density mean and the threshold calculation formula.
4. The method of claim 2, wherein selecting a threshold calculation formula corresponding to the tissue density value range based on the tissue density value range to which the tissue density mean belongs comprises:
if the tissue density mean value is less than or equal to a first parameter, selecting a first threshold calculation formula;
if the tissue density mean value is larger than the first parameter and smaller than a second parameter, selecting a second threshold calculation formula;
if the tissue density mean value is larger than or equal to the second parameter, selecting a third threshold calculation formula;
wherein the first threshold value is calculated by the formula,
(ii) a The second threshold value is calculated by the formula,
(ii) a The third threshold value is calculated by the formula,
；
wherein, the
Is the dynamic thresholdValue of, saidmeanCoronary Is the mean value of the tissue density, the
Is the first parameter, the
Is the second parameter, the
Is a third parameter, said
Is a fourth parameter, said
Is a fifth parameter, said
Are all positive numbers.
5. The method of any one of claims 2 to 4, wherein determining the predicted location of calcified plaque on the first point cloud data based on the dynamic threshold comprises:
determining, from all the tissue density values, a location of a target point with a tissue density value greater than the dynamic threshold as a predicted location of the calcified plaque on the first point cloud data.
6. The method of claim 1, wherein determining the coronary segment at the predicted location on the first point cloud data comprises:
determining a centerline point set and segmentation information for a coronary artery based on the first point cloud data; wherein the centerline point set comprises points on a central axis of the first point cloud data;
and determining the position of the central point closest to the predicted position as the coronary artery segment of the predicted position from the central line point set based on the segmentation information.
7. The method of claim 1, wherein said constructing second point cloud data of said coronary artery based on said coronary vessel flow path region comprises:
traversing all target points in the coronary artery blood vessel flow passage region by taking the position of an entry point in the coronary artery blood vessel flow passage region as a starting point according to the sequence from near to far away from the entry point to obtain target point cloud data of a closed region of the coronary artery segment;
replacing the point cloud data corresponding to the coronary artery segment of the first point cloud data with the target point cloud data to obtain the second point cloud data.
8. A coronary artery construction device, the device comprising:
the processing module is used for acquiring first point cloud data of the coronary artery reconstructed by the multiple thoracic cavity tomography images and calculating a dynamic threshold value based on the first point cloud data;
a determination module to determine a predicted location of calcified plaque on the first point cloud data based on the dynamic threshold;
the determining module is further configured to determine, on the first point cloud data, a coronary artery segment where the predicted position is located;
a generating module for generating a cross-section along a location of a central point within the coronary segment;
the reconstruction module is used for removing a target point corresponding to the calcified plaque in the cross section to obtain a reconstructed coronary artery blood vessel flow passage area;
and the construction module is used for constructing second point cloud data of the coronary artery based on the coronary artery blood vessel flow passage area.
9. A terminal, characterized in that the terminal comprises:
a memory for storing executable instructions;
a processor for executing the executable instructions stored in the memory to implement the method of constructing a coronary artery according to any one of claims 1 to 7.
10. A storage medium having stored thereon executable instructions for causing a processor to perform the method of constructing coronary arteries of any one of claims 1 to 7 when the executable instructions are executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210015014.5A CN114049282B (en) | 2022-01-07 | 2022-01-07 | Coronary artery construction method, device, terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210015014.5A CN114049282B (en) | 2022-01-07 | 2022-01-07 | Coronary artery construction method, device, terminal and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114049282A CN114049282A (en) | 2022-02-15 |
| CN114049282B true CN114049282B (en) | 2022-05-24 |
Family
ID=80213463
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210015014.5A Active CN114049282B (en) | 2022-01-07 | 2022-01-07 | Coronary artery construction method, device, terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114049282B (en) |
Family Cites Families (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110092808A1 (en) * | 2009-10-20 | 2011-04-21 | Magnetecs, Inc. | Method for acquiring high density mapping data with a catheter guidance system |
| CN106447645B (en) * | 2016-04-05 | 2019-10-15 | 天津大学 | Coronary Calcification detection and quantization device and method in enhanced CT image |
| CN107665737A (en) * | 2017-01-23 | 2018-02-06 | 上海联影医疗科技有限公司 | Vascular wall stress-strain state acquisition methods, computer-readable medium and system |
| CN107871318B (en) * | 2017-11-16 | 2018-11-09 | 吉林大学 | A kind of coronary calcification plaque detection method based on model migration |
| CN108960322B (en) * | 2018-07-02 | 2022-01-28 | 河南科技大学 | Coronary artery calcified plaque automatic detection method based on cardiac CT image |
| CN109350089B (en) * | 2018-10-25 | 2022-03-08 | 杭州医学院 | Automatic thyroid region segmentation method based on CT (computed tomography) image |
| CN109288537B (en) * | 2018-11-01 | 2022-08-09 | 杭州晟视科技有限公司 | System, method, apparatus and storage medium for assessing fractional flow reserve |
| US10813612B2 (en) * | 2019-01-25 | 2020-10-27 | Cleerly, Inc. | Systems and method of characterizing high risk plaques |
| US11357573B2 (en) * | 2019-04-25 | 2022-06-14 | International Business Machines Corporation | Optimum treatment planning during coronary intervention by simultaneous simulation of a continuum of outcomes |
| CN110163872A (en) * | 2019-05-14 | 2019-08-23 | 西南科技大学 | A kind of method and electronic equipment of HRMR image segmentation and three-dimensional reconstruction |
| CN111292314B (en) * | 2020-03-03 | 2024-05-24 | 上海联影智能医疗科技有限公司 | Coronary artery segmentation method, device, image processing system and storage medium |
| CN111462267A (en) * | 2020-03-27 | 2020-07-28 | 上海联影医疗科技有限公司 | Method and system for acquiring X-ray projection data |
| CN111862038B (en) * | 2020-07-17 | 2024-05-14 | 中国医学科学院阜外医院 | Plaque detection method, plaque detection device, plaque detection equipment and plaque detection medium |
| CN112684458B (en) * | 2021-03-17 | 2021-05-14 | 中国人民解放军国防科技大学 | Photon point cloud denoising method and system based on laser radar channel line scanning characteristics |
| CN113393427B (en) * | 2021-05-28 | 2023-04-25 | 上海联影医疗科技股份有限公司 | Plaque analysis method, plaque analysis device, computer equipment and storage medium |
| CN113470060B (en) * | 2021-07-08 | 2023-03-21 | 西北工业大学 | Coronary artery multi-angle curved surface reconstruction visualization method based on CT image |
| CN113724377B (en) * | 2021-11-01 | 2022-03-11 | 杭州晟视科技有限公司 | Three-dimensional reconstruction method and device of coronary vessels, electronic equipment and storage medium |
-
2022
- 2022-01-07 CN CN202210015014.5A patent/CN114049282B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN114049282A (en) | 2022-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11501485B2 (en) | System and method for image-based object modeling using multiple image acquisitions or reconstructions | |
| JP6790179B2 (en) | Useful results for real-time diagnostics | |
| CN108122616B (en) | Method for generating individual-specific cardiovascular models and use thereof | |
| CN110782520A (en) | Left atrial shape reconstruction using neural networks based on sparse position measurements | |
| CN108348206B (en) | Collateral flow modeling for non-invasive Fractional Flow Reserve (FFR) | |
| EP3061015A2 (en) | Vascular characteristic determination with correspondence modeling of a vascular tree | |
| CN111476791B (en) | Image processing method, image processing apparatus, and non-transitory computer readable medium | |
| KR20190084380A (en) | Method for extracting vascular structure in 2d x-ray angiogram, computer readable medium and apparatus for performing the method | |
| CN116090364A (en) | Method for obtaining coronary blood flow reserve fraction based on CTA image and readable storage medium | |
| CN112419484A (en) | Three-dimensional blood vessel synthesis method and system, coronary artery analysis system and storage medium | |
| CN112155580B (en) | Method and device for correcting blood flow velocity and microcirculation parameters based on radiography images | |
| CN111627023B (en) | Method and device for generating coronary artery projection image and computer readable medium | |
| CN113706541A (en) | Image processing method and device | |
| CN114049282B (en) | Coronary artery construction method, device, terminal and storage medium | |
| CN113362327B (en) | Region segmentation method, device, terminal and storage medium in chest image | |
| CN111798468B (en) | Image processing method and device, storage medium and electronic terminal | |
| CN113744217A (en) | CT image-oriented coronary artery intelligent auxiliary analysis method and system | |
| CN113658172B (en) | Image processing method and device, computer readable storage medium and electronic device | |
| Stefaniga et al. | An approach of segmentation method using deep learning for CT medical images | |
| CN115760961B (en) | Vascular image processing method, vascular image processing device, server and post-processing image generation system | |
| EP4005472B1 (en) | Method and apparatus for correcting blood flow velocity on the basis of interval time between angiographic images | |
| CN113706568B (en) | Image processing method and device | |
| US20240070855A1 (en) | Method and System of Calculating the Cross-section Area and Included Angle of Three-dimensional Blood Vessel Branch | |
| Thai et al. | Automatic segmentation and implicit surface representation of dynamic cardiac data | |
| Mekonnen et al. | Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation. Diagnostics 2021, 11, 685 |
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 |