KR101785293B1 - Method for automatic analysis of vascular structures in 2d xa images using 3d cta images, recording medium and device for performing the method - Google Patents
Method for automatic analysis of vascular structures in 2d xa images using 3d cta images, recording medium and device for performing the method Download PDFInfo
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Abstract
The automatic vascular structure analysis method in the 2D XA image using the 3D CTA image information performs rigid body matching on the blood vessels of the 2D XA (X-ray Angiogram) image and the 3D CTA (Computed Tomography Angiography) image of the same subject ; Generating a blood vessel graph structure for a blood vessel of the 2D XA image and a blood vessel of the 3D CTA image; Comparing a blood vessel graph structure of the blood vessel of the 2D XA image with a blood vessel graph structure of the 3D CTA image to set a region of interest in the blood vessel of the 2D XA image; And interpreting the ROI using the 3D CTA image information. Therefore, it is possible to improve the accuracy of the vascular structure analysis in the 2D XA image by comparing the similarity using the 3D CTA image information, and it is possible to perform the accurate operation using the same.
Description
The present invention relates to a method of analyzing an automatic vascular structure in a 2D XA image using 3D CTA image information, a recording medium and an apparatus for performing the same, and more particularly, to a method and apparatus for analyzing a vascular structure of a 2D XA (X- The present invention relates to an automatic vascular structure analysis technique using information of 3D CTA (Computed Tomography Angiography) images based on matching technology for automatic identification.
In the medical field, one-dimensional images (EKG), two-dimensional images (X-ray, ultrasound images, etc.), and three-dimensional images (CT, MRI, PET, etc.) are used for diagnosis and treatment. In addition, 3D (2D + t) and 4D (3D + t) images that move with time as well as still images are used. Advances in imaging technology in these medical fields have the advantage of improving diagnostic and therapeutic accuracy, but they are disadvantageous in that they are difficult for physicians to identify individually because of the large data volume of the images.
In order to overcome these shortcomings, researches on computer assisted technology have been actively carried out. In particular, with the recent rapid increase in patients with coronary artery disease, there is a growing need for computer assisted techniques to aid in the diagnosis and treatment of this disease. Coronary artery disease is a disease that occurs when the blood vessels become narrowed or closed due to waste products in the coronary arteries and can not meet the metabolic demand of the myocardium. Recently, it has been rapidly increasing due to Western diet, life expectancy and lack of exercise .
A representative treatment for coronary artery disease is stent implantation which alleviates stenosis by inserting a metal mesh network into the lesion. Stent implantation is a non - invasive treatment modality that uses minimal incision, anesthesia, and invasive manipulation, so it has the advantage of less physical, mental, and economic burden on the patient.
However, the 3D structure of the procedure depends on the 2D XA (X-ray Angiogram) image and is dependent on the medical staff's intuition and tactile feedback, so it is difficult to guarantee the accuracy of the procedure. In addition, there is a disadvantage in that complications such as inserting or dislodging the position of the insertion device in the body may occur.
In order to overcome these drawbacks, it is necessary to study the procedure assistive technology that automatically processes and uses a lot of information such as 2D + t XA (X-ray Angiogram) and 3D + t CTA (Computed Tomography Angiography)
Accordingly, it is an object of the present invention to provide a method for analyzing an automatic vascular structure in a 2D XA image using 3D CTA image information.
It is another object of the present invention to provide a recording medium on which a computer program for performing an automatic vascular structure analysis method in a 2D XA image using the 3D CTA image information is recorded.
It is still another object of the present invention to provide an apparatus for performing an automatic vascular structure analysis method in a 2D XA image using the 3D CTA image information.
According to an embodiment of the present invention for realizing the object of the present invention, there is provided a method of analyzing an automatic vascular structure in a 2D XA image using 3D CTA image information, Performing a rigid body matching on a blood vessel of a CTA (Computed Tomography Angiography) image; Generating a blood vessel graph structure for a blood vessel of the 2D XA image and a blood vessel of the 3D CTA image; Comparing a blood vessel graph structure of the blood vessel of the 2D XA image with a blood vessel graph structure of the 3D CTA image to set a region of interest in the blood vessel of the 2D XA image; And interpreting the ROI using the 3D CTA image information.
In an embodiment of the present invention, the step of interpreting the ROI using the 3D CTA image information may include interpolating the ROI using the 3D CTA image information, Generating a model; And measuring the degree of similarity with the 3D CTA image, which is rigid body matched with the blood vessel graph structural model of the number of all the above cases.
In an embodiment of the present invention, the step of interpreting the ROI using the 3D CTA image information further comprises generating a number of all possible cases of the sub-region of the ROI, .
In the embodiment of the present invention, the step of analyzing the region of interest using the 3D CTA image information may include deriving a blood vessel graph structure closest to the 3D CTA image among the blood vessel graph structural models of all the cases .
In the embodiment of the present invention, the step of analyzing the ROI using the 3D CTA image information may further include the step of assuming uncertainty information when the similarity threshold is smaller than a predetermined threshold value.
In the embodiment of the present invention, the step of analyzing the region of interest using the 3D CTA image information may be performed by comparing the graph structure of the blood vessel of the 2D XA image with the graph structure of the blood vessel of the 3D CTA image .
In the embodiment of the present invention, the step of setting the region of interest in the blood vessel of the 2D XA image may include: a circulation structure in the blood vessel of the 2D XA image; a structure that does not exist in the blood vessel structure of the 3D CTA image; It is possible to set at least one of the structure of the blood vessel graph and the structure of the image to be the region of interest.
A computer program for performing an automatic vascular structure analysis method on a 2D XA image using 3D CTA image information is recorded in a computer-readable storage medium according to an embodiment of the present invention for realizing the above- have.
According to another embodiment of the present invention, there is provided an apparatus for analyzing an automatic vascular structure in a 2D XA image using 3D CTA image information, A rigid body matching unit for performing rigid body matching on a blood vessel of a 3D CTA (Computed Tomography Angiography) image; A blood vessel graph structure generating unit for generating a blood vessel graph structure for blood vessels of the 2D XA image and the 3D CTA image; An interest region setting unit for comparing a blood vessel graph structure of the blood vessel of the 2D XA image with a blood vessel graph structure of the 3D CTA image to set a region of interest in the blood vessel of the 2D XA image; And an ROI analyzing unit for analyzing the ROI using the 3D CTA image information.
In an embodiment of the present invention, the analyzing unit may include a model generating unit for generating a blood vessel graph structural model of all possible cases using the blood vessel information extracted from the 2D XA image in the region of interest; A similarity measuring unit for measuring the similarity of the blood vessel graph structural model of all the above cases to the 3D CTA image which is rigidly matched; And a derivation unit for deriving a blood vessel graph structure closest to the 3D CTA image among the blood vessel graph structural models in all of the above cases.
According to the automatic vascular structure analysis method in the 2D XA image, the structure of the blood vessel is analyzed through the similarity comparison using the 3D CTA image information of the same patient to supplement the limited 2D XA image information. Therefore, it is possible to improve the accuracy of the analysis of the vascular structure in the 2D XA image, and it is possible to perform accurate operation using the same. In addition, it can be used as an assistive technology to help the medical staff perform image processing automatically in advance with a lot of medical data which is increasing day by day.
1 is a block diagram of an apparatus for analyzing an automatic vascular structure in a 2D XA image according to an embodiment of the present invention.
FIG. 2 is a detailed block diagram of a region-of-interest analyzer of the automatic vascular structure analyzer in the 2D XA image of FIG. 1; FIG.
3 is a conceptual diagram for explaining a simulation technique for rigid body matching.
4 is a diagram for explaining the creation of a blood vessel graph structure in a 2D XA image.
FIG. 5 is a diagram for explaining the generation of a blood vessel graph structure in a 3D CTA image. FIG.
6 is a diagram for explaining setting of a region of interest in a blood vessel of a 2D XA image.
FIGS. 7 to 10 are views showing a blood vessel graph structure model generated for analyzing the region of interest shown in FIG.
11 is a diagram showing an example of all possible vessel graph structures of the region of interest of FIG.
12 is a flowchart of a method for analyzing an automatic vascular structure in a 2D XA image according to an embodiment of the present invention.
13 is a detailed flowchart of a step of analyzing a region of interest using 3D CTA image information of FIG.
The following detailed description of the invention refers to the accompanying drawings, which illustrate, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the present invention are different, but need not be mutually exclusive. For example, certain features, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in connection with an embodiment. It is also to be understood that the position or arrangement of the individual components within each disclosed embodiment may be varied without departing from the spirit and scope of the invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is to be limited only by the appended claims, along with the full scope of equivalents to which such claims are entitled, if properly explained. In the drawings, like reference numerals refer to the same or similar functions throughout the several views.
Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.
1 is a block diagram of an apparatus for analyzing an automatic vascular structure in a 2D XA image according to an embodiment of the present invention. FIG. 2 is a detailed block diagram of a region-of-interest analyzer of the automatic vascular structure analyzer in the 2D XA image of FIG. 1; FIG.
The automatic vascular structure analyzer 10 (hereinafter, referred to as device) in the 2D XA image according to the present invention can automatically calculate the vein structure of a 2D XA (X-ray angiogram) image using the 3D CTA (Computed Tomography Angiography) .
Referring to FIG. 1, an
The rigid
The
Referring to FIG. 2, the
A detailed description of each configuration will be described later.
The
The
The
The
The
The rigid
3, the rigid
Even if the 2D XA image is completely the same as when the 2D XA image is acquired using Dicom information, errors may be caused by the protocol of the imaging device, the patient's posture, the heartbeat, and the breathing. In order to reduce this error, it is possible to perform precision matching by performing rigid transformation using an optimization technique.
The blood vessel graph
The 2D XA image is a projected image that shows the X-rays through the actual heart. As the depth information disappears during the projection process, overlapping blood vessels cause problems such as inconspicuous parts or distortions have. This causes the problem that it is difficult to grasp the structure of the blood vessels. In order to solve this problem, we try to understand the structure of 2D XA image by using the topology information of 2D CTA image.
Since the 2D XA image has a very complicated structure due to overlapped or obscured vascular structures, it is necessary to reduce the complexity of the vascular structure for easier analysis. For this purpose, the structure of complex blood vessels is created as a graph structure.
The blood vessel graph structure represents a blood vessel in the form of a branch of a tree. Referring to FIG. 4, a blood vessel graph structure representing a connection point of a blood vessel in a 2D XA image is created. Referring to FIG. 5, a blood vessel graph representing a connection and branching of blood vessels in a 3D CTA image Create a structure.
When the blood vessel graph structure of the blood vessel of the 2D XA image and the blood vessel graph structure of the blood vessel of the 3D CTA image are respectively generated, the
The blood vessel graph structure generated by the blood vessel graph
The ROI analyzing unit 190 analyzes the ROI using the 3D CTA image information. That is, the ROI analyzing unit 190 performs a similarity comparison process between the blood vessels. The ROI is a relatively simple method by comparing the blood vessel graph structure generated from the 3D CTA image and the blood vessel graph structure generated from the 2D XA image It can be interpreted as.
However, in most cases, problems that are difficult to interpret can be issued. In order to analyze the ROI, the
Referring to FIGS. 7 to 10, examples of a blood vessel graph structure model generated for analyzing a region of interest are shown.
Referring to FIG. 7, it is assumed that a branch point exists in the ROI of FIG. 6, and a blood vessel graph structural model is generated in a case where the end point of the blood vessel B or a part of the blood vessel is hidden by the blood vessel A and is not visible.
Referring to FIG. 8, it is assumed that a branch point exists in the ROI of FIG. 6, and a blood vessel graph structural model is generated in a case where the end point of the blood vessel A or a part of the blood vessel is hidden from the blood vessel B and is not visible.
Referring to FIG. 9, it is assumed that a branch point does not exist in the ROI of FIG. 6, and a blood vessel graph structure model is generated in a case where a contact point occurs when a blood vessel A is very close to a blood vessel B, will be.
Referring to FIG. 10, it is assumed that the ROI of FIG. 6 does not include a branch point, and a blood vessel graph structure model is generated in the case where a point of intersection of the blood vessel A and the blood vessel B occurs and a fake branch point occurs.
Referring to FIG. 11, there is shown a set of vessel graph structure models that additionally generates a model to which the number of all possible cases of the sub-region of the ROI of FIG. 6 is applied.
The
The similarity comparison is performed on the 2D vessel adjacent to the 3D vessel using the rigid body matching result performed in the rigid
The
Since the vascular structure of the 2D XA image can not be completely grasped even when the 3D CTA image information is used, it is assumed that the vascular structure of the 2D XA image is uncertain if the similarity degree measurement result is smaller than the preset similarity threshold value. Value.
As described above, the present invention uses different styles, unlike the prior art, which uses a single modality as an assistive technology using only 3D data. In other words, to complement 2D XA images with severe loss and distortion of information, it is possible to perform accurate vascular structure analysis by comparing similarities using 3D CTA images of the same patient.
12 is a flowchart of a method for analyzing an automatic vascular structure in a 2D XA image according to an embodiment of the present invention. 13 is a detailed flowchart of a step of analyzing a region of interest using 3D CTA image information of FIG.
The automatic vascular structure analysis method in the 2D XA image according to the present embodiment can be performed in substantially the same configuration as that of the
Referring to FIG. 12, a method for analyzing an automatic vascular structure in a 2D XA image according to the present embodiment is a method for analyzing a blood vessel of a 2D XA (X-ray Angiogram) image and a 3D CTA (Computed Tomography Angiography) Matching is performed (step S10).
In the present invention, the 3D CTA image, which is another modality in the analysis of the 2D XA image, is referred to. Therefore, in order to match between different forms, the matching space must be preceded first.
For this, referring to FIG. 3, the 3D CTA image is created by creating a virtual simulation environment in the same way as when the 2D XA image was acquired using the Dicom information obtained from the C-arm, which is the XA imaging device, .
Even if the 2D XA image is completely the same as when the 2D XA image is acquired using Dicom information, errors may be caused by the protocol of the imaging device, the patient's posture, the heartbeat, and the breathing. In order to reduce this error, it is possible to perform precision matching by performing rigid transformation using an optimization technique.
When the rigid body matching is completed, each blood vessel graph structure is created for the blood vessels of the 2D XA image and the 3D CTA image (step S30).
The 2D XA image is a projected image that shows the X-rays through the actual heart. As the depth information disappears during the projection process, overlapping blood vessels cause problems such as inconspicuous parts or distortions have. This causes the problem that it is difficult to grasp the structure of the blood vessels. In order to solve this problem, we try to understand the structure of 2D XA image by using the topology information of 2D CTA image.
Since the 2D XA image has a very complicated structure due to overlapped or obscured vascular structures, it is necessary to reduce the complexity of the vascular structure for easier analysis. For this purpose, the structure of complex blood vessels is created as a graph structure.
The blood vessel graph structure represents a blood vessel in the form of a branch of a tree. Referring to FIG. 4, a blood vessel graph structure representing a connection point of a blood vessel in a 2D XA image is created. Referring to FIG. 5, a blood vessel graph representing a connection and branching of blood vessels in a 3D CTA image Create a structure.
When the blood vessel graph structure of the blood vessel of the 2D XA image and the blood vessel graph structure of the blood vessel of the 3D CTA image are respectively generated, the blood vessel graph structure of the blood vessel of the 2D XA image is compared with the blood vessel graph structure of the blood vessel of the 3D CTA image , A region of interest (ROI) is set in the blood vessel of the 2D XA image (step S50).
The generated blood vessel graph structure utilizes the fact that it is easy to grasp the relationship between each node and edges and to easily compare the graphs. For example, when a circulation structure occurs in the blood vessels of the 2D XA image, a structure that does not exist in the graph structure generated from the 3D CTA image occurs, and a structure other than the graph structure of the blood vessel of the 3D CTA image occurs And designates the corresponding region as an ROI (see FIG. 6).
If the ROI is designated, the ROI is interpreted using the 3D CTA image information (step S70). That is, the designated ROI may be interpreted in a relatively simple manner by comparing the blood vessel graph structure generated from the 3D CTA image and the blood vessel graph structure generated from the 2D XA image.
However, in most cases, problems that are difficult to interpret can be issued. In order to analyze such ROI, referring to FIG. 13, all the blood vessel graph structural models that can be changed by assuming the number of all cases that can be changed using the blood vessel and blood vessel information extracted from the 2D XA image are generated Step S71). In addition, since the structural transformation of the ROI can affect the sub-region, a model to which all the changeable number of sub-regions of the ROI are applied can be additionally generated (Step S72).
Referring to FIGS. 7 to 10, examples of a blood vessel graph structural model generated for analyzing a region of interest are shown. Referring to FIG. 11, a model to which all possible cases of the ROI sub- And a set of additional vessel graph structure models.
The degree of similarity to the 3D CTA images, which are rigid body matched to the blood vessel graph structural models of all the above cases, is measured (step S73). That is, similarity comparison with the 2D XA image information is performed using the 3D CTA image information most similar to the optimal solution to derive the most similar solution among the generated all the blood flow graph structural models.
Similarity comparisons are performed on 2D vessels adjacent to 3D vessels using the rigid body matched results performed. Similarity measurement between adjacent vessels is based on the graph structure, and the similarity of blood vessels extracted from 2D XA images and 3D CTA images is measured using additional information such as centerline, ellipse, gradient, intensity, and vesselness value of the blood vessels .
Next, using the measured similarity comparison result, the closest conclusion to the optimal solution of the graph structure is derived (step S74). For example, a blood vessel graph structure model having the highest degree of similarity to the 3D CTA image can be obtained as a result.
Since the vascular structure of the 2D XA image can not be completely grasped even when the 3D CTA image information is used, it is assumed that the vascular structure of the 2D XA image is uncertain if the similarity degree measurement result is smaller than the preset similarity threshold value. Value.
As described above, the present invention uses different styles, unlike the prior art, which uses a single modality as an assistive technology using only 3D data. In other words, to complement 2D XA images with severe loss and distortion of information, it is possible to perform accurate vascular structure analysis by comparing similarities using 3D CTA images of the same patient.
Such an automatic vascular structure analysis method in a 2D XA image can be implemented in an application or can be implemented in the form of program instructions that can be executed through various computer components and recorded in a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, and the like, alone or in combination.
The program instructions recorded on the computer-readable recording medium may be ones that are specially designed and configured for the present invention and are known and available to those skilled in the art of computer software.
Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like.
Examples of program instructions include machine language code such as those generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules for performing the processing according to the present invention, and vice versa.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the present invention as defined by the following claims. You will understand.
The present invention can be utilized as a navigation-based assistive technology that can be displayed in real time during the PCI procedure by using it as a prior art for non-rigid body matching. In addition, it can be used as an assistive technology to help diagnosis and treatment of patients by automatically labeling blood vessels and lesions. Furthermore, the present invention is not limited to cardiovascular, but is applicable to various blood vessels.
10: Automatic Vessel Structure Analysis Device in 2D XA Image
110: Rigid body matching portion
130: blood vessel graph structure generating unit
150: region of interest setting section
170: region of interest analyzer
171:
173:
175:
Claims (10)
Generating a blood vessel graph structure for blood vessels of the 2D XA image and the 3D CTA image in which the blood vessel graph structure generating unit is rigidly matched by the rigid body matching unit;
The ROI setting unit compares the blood vessel graph structure of the blood vessel of the 2D XA image generated by the blood vessel graph structure generating unit with the blood vessel graph structure of the blood vessel of the 3D CTA image to set a region of interest in the blood vessel of the 2D XA image step; And
Analyzing the ROI set by the ROI setting unit using the 3D CTA image information,
Wherein the step of analyzing the ROI set by the ROI setting unit using the 3D CTA image information comprises:
Generating a blood vessel graph structural model of all possible cases by using the blood vessel information extracted from the 2D XA image in the region of interest in the model generating unit of the ROI analyzing unit; And
Wherein the similarity measuring unit of the interest region analyzing unit measures the degree of similarity with the 3D CTA images each of which is rigidly matched with the blood vessel graph structural models of all the cases generated by the model generating unit, Automatic Analysis of Vascular Structure in a 2D XA Image.
Wherein the model generating unit of the ROI analyzing unit further includes a step of generating a blood vessel graph structural model of every number of cases in which the sub-region of the ROI can be changed, the automatic blood vessel structure in the 2D XA image using the 3D CTA image information Interpretation method.
And deriving a blood vessel graph structure closest to the 3D CTA image among the blood vessel graph structural models of all the above cases using the similarity comparison result measured by the similarity measuring unit in the derivation unit of the ROI analyzing unit. Automatic Vascular Structure Analysis Method in 2D XA Image Using 3D CTA Image Information.
If the derivation unit of the ROI analysis unit determines that the similarity comparison result measured by the similarity measurement unit is smaller than a preset similarity threshold value and provides the result as the final result value, Automatic Vessel Structure Analysis Method on 2D XA Image Using Information.
A method for analyzing an automatic vascular structure in a 2D XA image using 3D CTA image information in which a graph structure of a blood vessel of the 2D XA image is compared with a graph structure of a blood vessel of the 3D CTA image.
A 3D CTA image which sets at least one of a circulation structure in the blood vessel of the 2D XA image, a structure not existing in the graph structure of the 3D CTA image, and a structure of the blood vessel of the 3D CTA image, Automatic Vessel Structure Analysis Method on 2D XA Image Using Information.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120323547A1 (en) | 2011-06-20 | 2012-12-20 | Siemens Corporation | Method for intracranial aneurysm analysis and endovascular intervention planning |
| US20150335304A1 (en) | 2012-10-24 | 2015-11-26 | Cath Works Ltd. | Diagnostically useful results in real time |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120323547A1 (en) | 2011-06-20 | 2012-12-20 | Siemens Corporation | Method for intracranial aneurysm analysis and endovascular intervention planning |
| US20150335304A1 (en) | 2012-10-24 | 2015-11-26 | Cath Works Ltd. | Diagnostically useful results in real time |
Non-Patent Citations (1)
| Title |
|---|
| 논문 "수술 중 촬영된 2D XA 영상과 수술전 촬영된 3D CTA 영상의 고속 강제 정합 기법" (멀티미디어학회논문지 제16권 제12호, 2013.12, 1454-1464) |
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| KR20210115223A (en) | 2020-03-12 | 2021-09-27 | 숭실대학교산학협력단 | Method of vessel structures extraction using artificial intelligence technique, recording medium and device for performing the method |
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