CN114469153A - Angiography device and equipment based on CT (computed tomography) image and computer readable medium - Google Patents
Angiography device and equipment based on CT (computed tomography) image and computer readable medium Download PDFInfo
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Abstract
The invention relates to an angiography device, equipment and computer storage medium based on CT images, wherein the angiography device comprises: the three-dimensional image acquisition module is used for acquiring a modeling file output by the CT equipment; a marking module for marking the position of the selected suspected lesion in the modeling file; the projection angle calculation module is used for eliminating three-dimensional occlusion caused by blood vessels around the suspected lesion and calculating at least one non-occlusion projection angle; the contrast position calculation module is used for calculating a recommended contrast position of an image intensifier in the angiography machine according to the non-shielding projection angle; and the rotation driving module is used for driving the angiography machine to rotate to the recommended angiography position.
Description
Technical Field
The present invention relates to the field of medical image processing technologies, and in particular, to an angiography device, method, apparatus, and computer readable medium based on CT images.
Background
Cardiovascular and cerebrovascular diseases are the general names of cardiovascular and cerebrovascular diseases, and a global disease survey report published by the world health organization in 2014 mentions that the mortality rate of the cardiovascular and cerebrovascular diseases is the first of non-infectious diseases in the world, and the high morbidity, high mortality, high disability rate and high recurrence rate of the cardiovascular and cerebrovascular diseases form great threats to the health of Chinese people, so that the pandemic trend of the cardiovascular and cerebrovascular diseases is restrained.
The vascular intervention operation is an important means for treating the current vascular diseases, and has the advantages of small wound, quick recovery, few complications and the like. The safety and effectiveness of interventional operation in treating cardiovascular and cerebrovascular diseases such as myocardial infarction, intracranial aneurysm, acute thrombus occlusion and recanalization, cerebrovascular stenosis and the like are accepted by the medical field.
Intraoperative X-ray contrast imaging (2D DSA) is a common imaging modality in vascular interventional procedures, while Coronary Angiography (CAG) is the "gold standard" for diagnosing coronary heart disease, and is also the basic work of Percutaneous Coronary Intervention (PCI). Through an angiography system DSA, a proper projection position and an appropriate projection angle are selected, two frames of X-ray images shot before and after the injection of contrast agent are digitally input into an image computer, and bone and soft tissue images on the angiography image are eliminated through subtraction, enhancement and re-imaging processes to obtain a clear pure blood vessel image, so that real blood vessel shape and form information can be provided. The angiography system DSA comprises a frame, a C-shaped arm, an X-ray tube assembly, an image intensifier, a patient examination bed, a high-voltage generator, a monitor, a suspension system of the monitor and the image intensifier, and the like, wherein the X-ray tube assembly and the image intensifier are respectively arranged at the lower end and the upper end of the C-shaped arm and are parallel to each other. In coronary angiography, the commonly used projection angles are: RAO (right oblique, intensifier in the upper right of the patient); LAO (left anterior oblique, enhancer on upper left of patient); CRA (head position, intensifier near patient's head); CAU (foot position, intensifier near patient's foot); AP (in position, with the augmentor directly against the patient's sternum); LAT (lateral, with the intensifier on the side of the patient).
The left coronary angiography generally has 6 body positions, blood vessels exposed at different angles are different, the blood vessels are separated in position, and the optimal observation body position of the target blood vessel is found by selecting different body positions. When the catheter is in place, the head-left shoulder-spider-foot-liver-right shoulder is rotated clockwise one turn for projection. Wherein, the rotation angles of the C-shaped arm and the image intensifier when the head is righted are respectively as follows: AP + CRA30, LAO30 degree + CRA20 degree at left shoulder position, LAO45 degree + CAU30 degree at spider position, AP + CAU30 degree at foot position, RAO30 degree + CAU20 degree at liver position, RAO30 degree + CRA20 degree at right shoulder position.
The right coronary artery has few branches, so the commonly used projection positions are the left anterior oblique position and the head position. The anterior left oblique position is LAO45, and the head position is AP + CRA 20.
By rotation of the C-arm, the image intensifier and the X-ray tube, the transmission angle can be determined. However, the angiography system DSA can only provide 2D image information, cannot provide three-dimensional anatomical information, and is difficult to accurately determine the position of the vascular stenosis, and a doctor needs to imagine 3D structural information of an internal tissue according to experience under the guidance of a 2D image, and seriously depends on the experience of the doctor, thereby easily causing errors of a catheter insertion path and a stent placement pose; even if the coronary artery 2D graph is shot at a standard angle, the occlusion problem of blood vessels often occurs, so that a real focus can be occluded and cannot be clearly checked, and a doctor needs to adjust the shooting angle according to experience, so that the judgment of the position of a lesion is inaccurate, and the operation time and the success rate are influenced. In addition, the catheter position is tracked in real time by repeatedly injecting contrast medium and performing X-ray irradiation for obtaining the position of the interventional object in the body, which not only reduces the operation efficiency, but also causes radiation exposure of doctors and patients, thereby bringing about potential hidden troubles.
The preoperative CT or MR global three-dimensional imaging can be used for describing the 3D structure of an anatomical tissue, evaluating and analyzing the focus and providing a basis for the diagnosis of a patient and the determination of a treatment scheme; if the medical images of various modes are registered and fused, richer and more comprehensive anatomical structure information of blood vessels and peripheral organs can be provided for the diagnosis and treatment process of doctors, and the operation precision is improved; however, the current preoperative images still cannot be effectively used in the operation, and references such as tissue deformation information and position and shape information of an interventional object cannot be provided in the operation, so that the difficulty in designing an interventional treatment scheme is improved.
Therefore, how to effectively utilize various novel medical image information such as CTA, MRA, DSA and the like, realize real-time high-precision fusion, reduce the radiation to doctors and patients in the process of vascular interventional therapy, reduce the operation difficulty of doctors, improve the success rate of operations and become a technical problem which needs to be solved urgently at present.
Disclosure of Invention
The invention discloses an angiography device based on CT images, and aims to solve the technical problems in the prior art.
The invention adopts the following technical scheme: an angiographic apparatus based on CT images, comprising:
-a stereo image acquisition module for acquiring a modeling file output by the CT device;
-a marking module for marking the location of a suspected lesion clicked on in the modeling file;
-a projection angle calculation module for excluding three-dimensional occlusions due to blood vessels present around the suspected lesion and calculating at least one non-occluded projection angle;
-a contrast position calculation module for calculating a recommended contrast position of an image intensifier in an angiographic apparatus based on said unobstructed projection angle;
-a rotation driving module for driving the rotation of the angiographic camera to the recommended imaging position.
As a preferred technical solution, the modeling file is a preoperative medical image output by the CT device through DICOM standard.
Preferably, the position of the lesion includes a coordinate parameter of the lesion in a three-dimensional coordinate system.
As a preferred technical solution, in the projection angle calculation module, all undesired angles that may affect the vessel observation due to three-dimensional occlusion are excluded by a depth cache algorithm, and at least one non-occlusion projection angle is calculated by a filtered back-projection reconstruction algorithm.
Preferably, the recommended imaging position includes a rotation angle of a C-arm in the angiography machine and a rotation angle of the image intensifier.
As a preferred technical solution, the method further comprises:
-a model reconstruction module for reconstructing a three-dimensional model of the target vessel from the 3D U-Net framework in the full convolution neural network FCN;
-a registration module for registering the three-dimensional model with an intraoperative two-dimensional image acquired by rotation of the angiographic camera to the recommended contrast position;
-a determining module for determining whether the registered images meet an observation expectation;
-an optimization module for modifying the position of the angiographic apparatus and returning the position to the projection angle calculation module for optimizing the unobstructed projection angle.
In a preferred technical solution, in the model building module, a full convolution neural network FCN with a plurality of convolution layers as a main structure is used to identify the structure of the target organ, and then a 3D U-Net framework is used to automatically segment and reconstruct the target organ.
As a preferred technical solution, in the registration module, a global registration method is used to perform global transformation on the reconstructed three-dimensional model, and the reconstructed three-dimensional model is aligned with the intraoperative two-dimensional image as a whole, and then registration between the reconstructed three-dimensional model and the intraoperative two-dimensional image is achieved through non-rigid local deformation.
As a preferred solution, the observation is expected to mean that the blood vessels at the lesion site are clear and free of occlusion.
The invention also provides an angiography apparatus based on the CT image, which comprises:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the functions implemented by the apparatus as in any one of the above.
As a preferred technical scheme, the equipment further comprises an interface, a display screen and an operation panel of the DICOM standard;
the interface is used for importing a modeling file output by the CT equipment;
the display screen is used for displaying the modeling file and the recommended radiography position;
the operation panel is provided with keys for clicking the position of the suspected lesion on the modeling file, selecting the recommended radiography position and controlling the angiography machine to rotate to the recommended radiography position.
As a preferred technical solution, the operation panel is further configured to actively input rotation angle parameters of a C-arm and an image intensifier in the angiography machine, and control the angiography machine to rotate to a corresponding angle.
The invention also provides a computer-readable medium, in which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of:
acquiring a modeling file output by CT equipment;
marking the position of the suspected lesion clicked in the modeling file;
eliminating three-dimensional occlusion caused by blood vessels existing around the suspected lesion, and calculating at least one non-occlusion projection angle;
calculating a recommended radiography position of an image intensifier in the angiography machine according to the non-shielding projection angle;
driving the angiography machine to rotate to the recommended angiography position.
The technical scheme adopted by the invention can achieve the following beneficial effects: the angiography device based on the CT image can evaluate and analyze the focus through the image acquired by preoperative CT equipment, simultaneously uses a neural network algorithm to eliminate three-dimensional occlusion around the suspected focus, calculates a plurality of non-occlusion projection angles and recommended radiography positions of a plurality of angiography machines, simultaneously judges whether the image is clear or not and has no occlusion according to an intraoperative two-dimensional image acquired when the angiography machine moves to the recommended radiography position, carries out fine adjustment if the position is not ideal, and returns the more optimal position after fine adjustment to the neural network algorithm to optimize the algorithm, thereby ensuring that the subsequently output recommended radiography position is more accurate and applicable. The invention can enable doctors to judge and check the focus position, check the tissue deformation information and the position and shape information of an intervention object more accurately when carrying out vascular intervention operations such as PCI and the like, reduce the angle of an angiography machine and the injection frequency of a contrast medium in the operation, enable the doctors to evaluate and analyze the focus by combining the 2D image in the operation under the guidance of the 3D image before the operation, provide richer and more comprehensive anatomical structure information of blood vessels and peripheral organs for the diagnosis and treatment process of the doctors, improve the operation precision and success rate, greatly shorten the operation duration of the intervention operation, and reduce the radiation exposure risk of the doctors and patients.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below to form a part of the present invention, and the exemplary embodiments and the description thereof illustrate the present invention and do not constitute a limitation of the present invention. In the drawings:
fig. 1 is a block diagram of an angiographic apparatus based on CT images according to the present invention, as disclosed in embodiment 1;
FIG. 2 is a block diagram of an angiographic apparatus based on CT images according to the present invention, as disclosed in embodiment 2;
FIG. 3 is a block diagram of an angiographic apparatus based on CT images according to the present invention, as disclosed in embodiment 3;
fig. 4 is a schematic view of a usage status of an angiography apparatus based on CT images according to embodiment 5 of the present invention.
Description of reference numerals:
the angiography device 100, 100', 100 ″ based on the CT image, the stereoscopic image acquisition module 110, the marking module 120, the projection angle calculation module 130, the angiography position calculation module 140, the rotation driving module 150, the model reconstruction module 160, the registration module 170, the judgment module 180, and the optimization module 190; an angiography device 200 based on CT images, a display screen 210, an operation panel 220; an angiographic camera 300, a C-arm 310, an image intensifier 320, and a display 330.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. In the description of the present invention, it is noted that the term "or" is generally employed in its sense including "and/or" unless the content clearly dictates otherwise.
It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the problems existing in the prior art, an embodiment of the present application provides an angiography device based on CT images, including: the three-dimensional image acquisition module is used for acquiring a modeling file output by the CT equipment; a marking module for marking the position of the selected suspected lesion in the modeling file; the projection angle calculation module is used for eliminating three-dimensional occlusion caused by blood vessels around the suspected lesion and calculating at least one non-occlusion projection angle; the contrast position calculation module is used for calculating a recommended contrast position of an image intensifier in the angiography machine according to the non-shielding projection angle; and the rotation driving module is used for driving the angiography machine to rotate to the recommended angiography position.
Example 1
This embodiment 1 provides an angiography device 100 based on CT images, which eliminates three-dimensional occlusion around a suspected lesion, and calculates a plurality of non-occluded projection angles and a plurality of recommended angiography positions of an angiography machine, so as to reduce the frequency of adjusting the angle of the angiography machine and injecting a contrast medium during an operation, so that a doctor performs evaluation and analysis on the lesion in combination with a 2D image during the operation under the guidance of a preoperative 3D image, provide richer and more comprehensive anatomical structure information of a blood vessel and peripheral organs for the doctor's diagnosis and treatment process, and improve the accuracy and success rate of the operation.
In a preferred embodiment, the CT image based angiography apparatus 100 includes a stereo image acquisition module 110, a labeling module 120, a projection angle calculation module 130, an angiography position calculation module 140, and a rotation driving module 150 according to fig. 1.
Preferably, the stereo image acquiring module 110 is configured to acquire a modeling file output by the CT apparatus, where the modeling file is a preoperative medical image output by the CT apparatus according to the DICOM standard and may be used to describe anatomical tissues, such as 3D structures of cardiovascular and cerebrovascular systems. It should be noted that dicom (digital Imaging and Communications in medicine) is a digital Imaging and Communications in medicine standard, by which people can establish an interface on Imaging equipment to complete the input/output of image data, and the equipment includes not only CT, MR, nuclear medicine and ultrasound examination, but also CR, film digitizing system, video acquisition system, HIS/RIS information management system, etc. Therefore, in the information network system adopting DICOM standard, all DICOM devices can be connected and operated with each other according to DICOM network upper layer protocol. Preferably, in this embodiment, the modeling file may be output in the form of an optical disc, or may be transmitted to other operating devices through the DICOM standard.
Preferably, the marking module 120 is configured to mark the position of the suspected lesion clicked by the physician in the modeling file. Because the modeling file is a medical image output by the DICOM standard, and the stored content of the modeling file comprises the CT value of a patient, information of a CT machine, a layer thickness, a timestamp, basic information of the patient and the like, a Dicom file browser can be applied to mark the modeling file, and common Dicom file browsers comprise ImageJ,3Dslicer, ITK-SNAP, pydicom, simITK, dicommead, MIMICS and the like; further, in this embodiment, the doctor may observe the positions of one or more suspected lesions in the blood vessel in the modeling document, and then mark the positions separately, where the positions include coordinate parameters of the lesions in a three-dimensional coordinate system when marking the positions of the lesions, and store the coordinate parameters of the positions of the multiple lesions one by one.
In other embodiments, artificial intelligence may also be used to detect and determine the location of the lesion. For example, the method of deep learning is used for detecting the aneurysm on the TOF-MRA image, and the accuracy is improved by 9.3% compared with that of the original base. In the detection of small aneurysms, the detection accuracy can be greatly improved by machine learning. Using a computer-aided method of detecting aneurysms, a convolutional neural network is used to detect each pixel separately inside or outside the aneurysm, which can result in a sensitivity of 70%.
Preferably, the projection angle calculation module 130 is configured to eliminate a three-dimensional occlusion caused by a blood vessel around the suspected lesion and calculate an occlusion-free projection angle. In a preferred embodiment, all undesired angles that may affect the vessel view due to three-dimensional occlusion may be excluded by the depth caching algorithm. It should be further noted that a depth-buffer method (depth-buffer method) is a commonly used object space algorithm for determining the visibility of the surface of an object, and compares the depths of all the surfaces in a scene at each pixel position on a projection surface. When the depth caching algorithm is used, the blood vessel marked with the focus position in the modeling file is projected onto a plane, the depth of the projection point is compared with the depth value of the corresponding position in the depth caching file, whether the focus position is blocked or not is determined, whether all possible projection surfaces are blocked or not is calculated one by one, and all unexpected angles which may influence the blood vessel observation due to three-dimensional blocking are eliminated. Preferably, in order to improve the operation efficiency, the fine adjustment of the angle can be performed near six positions of the contrast medium commonly used for the left coronary artery and two positions of the contrast medium commonly used for the right coronary artery, so as to obtain a better projection angle more quickly.
Further, the non-occluded projection angle can be calculated by a filtered back-projection reconstruction algorithm. The filtering back projection reconstruction algorithm (FBP) is a space domain processing technology based on Fourier transform theory, and is characterized by that before back projection the projection under every collected projection angle is undergone the process of convolution treatment so as to improve the shape artifact resulted from point spread function and obtain good quality of reconstructed image. When the algorithm is carried out, firstly, carrying out one-dimensional Fourier transform on projection data of residual angles excluding all unexpected angles which may influence the observation of blood vessels due to three-dimensional occlusion, and then carrying out convolution operation on the projection data and a filter function to obtain projection data subjected to convolution filtering in each direction; then, carrying out back projection on the matrix units along all directions, namely, evenly distributing the matrix units to each matrix unit according to the original paths of the matrix units, and overlapping to obtain the CT value of each matrix unit; and obtaining a tomographic image of the scanned object after proper processing, selecting two, four, six or more clearest images according to requirements, recording projection angles corresponding to the clearest images, and taking the projection angles as non-shielding projection angles.
Preferably, the contrast position calculation module 140 is configured to calculate a recommended contrast position of the image intensifier in the angiographic apparatus according to the non-occlusion projection angle, and under the premise that the non-occlusion projection angle is known, the rotation angle of the C-arm and the rotation angle of the image intensifier in the angiographic apparatus may be reversely deduced to serve as the recommended contrast position, that is, the contrast posture.
Further, the four positions of the contrast medium commonly used for the left corona in the PCI operation at present are LAO30 ° + CRA30 °, RAO30 ° + CRA30 °, RAO20 ° + CAU20 °, LAO40 ° + CAU30 °; when the contrast position calculation module 140 is used to perform actual calculation, the obtained recommended contrast position of the left coronary artery is: LAO32 ° + CRA36 °, RAO28 ° + CRA31 °, RAO20 ° + CAU25 °, LAO41 ° + CAU31 °; or LAO30 ° + CRA33 °, RAO29 ° + CRA33 °, RAO19 ° + CAU21 °, LAO43 ° + CAU33 °; or LAO32 ° + CRA36 °, RAO28 ° + CRA31 °, RAO20 ° + CAU25 °, LAO41 ° + CAU31 °. In the prior art, when a physician operates, the conventional procedure is to rotate the angiography machine to a standard angiography position, and further fine-tune the angiography machine if the image is blocked during the operation until the image is clear and has no blocking position. In the embodiment, the contrast position calculating module 140 further provides at least three recommended contrast positions in each standard contrast position, and in consideration of the operation efficiency, the recommended contrast position close to the standard contrast position is preferentially selected when the recommended contrast position is selected.
When the contrast position calculation module 140 is used to perform actual calculation, the obtained recommended contrast position of the right coronary artery is: LAO42 °, AP + CRA21 °; or LAO40 °, AP + CRA30 °; or LAO45 °, CRA29 °. During the operation, the recommended contrast position is selected as above, and the recommended contrast position close to the standard contrast body position is preferentially selected.
Preferably, the rotation driving module 150 is used for driving the C-arm and the image intensifier in the angiography machine to rotate to the recommended angiography position for angiography.
In this example, the specific application method of the present invention is as follows:
when carrying out blood vessel interventional operations such as PCI and the like, a CT modeling file of a diseased blood vessel of a patient is obtained through a CT device, the modeling file is input into a stereo image obtaining module 110 in an angiography device based on a CT image through a DICOM standard, then the position of a suspected focus is marked in the modeling file through a marking module 120, the position contains the coordinate parameters of the lesion in the three-dimensional coordinate system, the coordinate parameters of the positions of a plurality of lesions are stored one by one, and the projection angle calculation module 130 is used, the three-dimensional occlusion around the suspected focus is eliminated through a depth cache algorithm, an unobstructed projection angle is calculated through a filtered wave back projection reconstruction algorithm, the contrast position calculation module 140 calculates the recommended contrast position of an image intensifier in the angiography machine according to the obtained unobstructed projection angle, and then the C-shaped arm and the image intensifier in the angiography machine are driven to rotate by corresponding angles to the recommended angiography position through the rotation driving module 150 for angiography.
Compared with the prior art, the embodiment can enable doctors to accurately judge and check the focus position, check the tissue deformation information and the position and shape information of an intervention object by combining images before operation when carrying out blood vessel intervention operations such as PCI, reduce the angle of an angiography machine and the injection frequency of contrast agents in the operation, improve the operation precision and success rate, greatly shorten the operation duration of the intervention operation, and reduce the radiation exposure risk of doctors and patients.
Example 2
As shown in fig. 2, the present embodiment provides an angiography apparatus 100' based on CT images, which further includes a model reconstruction module 160, a registration module 170, a determination module 180, and an optimization module 190 based on the structure of embodiment 1.
Preferably, the model reconstruction module 160 uses a full convolution neural network FCN with multi-layer convolution layers as a main structure to identify the structure of the target organ, then uses a 3D U-Net framework to automatically segment the target organ, and reconstructs a three-dimensional model of the target blood vessel. The full convolution network FCN can classify images at a pixel level, images of any size are input into the FCN, the images can be restored to the original image size through feature extraction of convolution layers and upsampling of convolution layers, and an end-to-end network structure is formed. U-Net is also one of the more common image segmentation networks. By using the image segmentation algorithm in the field of deep learning, the error can be greatly reduced, and the efficiency and the accuracy of organ structure identification are improved.
Preferably, the registration module 170 is configured to register the three-dimensional model with an intraoperative two-dimensional image acquired by rotating the angiography machine to the recommended angiography location; specifically, the blood vessels are segmented from the intraoperative two-dimensional image, and the three-dimensional model reconstructed by the model reconstruction module 160 is combined, and the blood vessels are used as the internal features for registration. Firstly, global transformation is carried out on the reconstructed three-dimensional model by using a global registration method, the three-dimensional model is integrally aligned with the intraoperative two-dimensional image, and then registration of the three-dimensional model and the intraoperative two-dimensional image is realized through non-rigid local deformation.
Preferably, the judging module 180 is configured to judge whether the registered image meets observation expectations, that is, whether blood vessels at the focus are clear and free of occlusion in the intraoperative two-dimensional image captured by the doctor during an operation.
The optimization module 190 is configured to modify the rotation angles of the C-arm and the image intensifier in the angiography machine and return the modified angles to the projection angle calculation module 130, so as to optimize the non-occlusion projection angle.
In this example, the specific application method of the present invention is as follows:
when carrying out blood vessel interventional operations such as PCI and the like, acquiring a CT modeling file of a diseased blood vessel of a patient through CT equipment, inputting the modeling file into a stereo image acquisition module 110 in an angiography device based on a CT image through a DICOM standard, transmitting the modeling file to a model reconstruction module 160 by the stereo image acquisition module 110, identifying the structure of a target organ by the model reconstruction module 160 through a full convolution neural network (FCN), automatically segmenting the target organ by adopting a 3D U-Net frame, and reconstructing a three-dimensional model of the target blood vessel; then, the marking module 120 is used for clicking the position of the suspected lesion in the three-dimensional model, the position contains the coordinate parameters of the lesion in the three-dimensional coordinate system, the coordinate parameters of the positions of a plurality of lesions are stored one by one, the projection angle calculation module 130 is used for eliminating the three-dimensional occlusion around the suspected lesion through the depth cache algorithm, the non-occlusion projection angle is calculated through the filtered wave back projection reconstruction algorithm, the contrast position calculation module 140 calculates the recommended contrast positions of at least two angiographs, namely recommended contrast position 1 and recommended contrast position 2, in each projection position according to the obtained non-occlusion projection angle, then the rotation driving module 150 is used for driving the C-shaped arm and the image intensifier in the angiograph to rotate to the optimal contrast position 1 for angiograph, at the moment, the registration module 170 is used for registering the three-dimensional model and the intraoperative two-dimensional image obtained by rotating the angiograph to the recommended contrast position 1, the judging module 180 judges whether the registered image meets the observation expectation, rotates the angiographic apparatus to the recommended angiographic position 2 for performing angiographic re if the registered image does not meet the observation expectation, performs fine adjustment on the angiographic apparatus if the registered image does not meet the observation expectation, and returns the angle parameters of the rotation of the C-arm and the image intensifier in the fine-adjusted angiographic apparatus to the projection angle calculating module 130 through the optimizing module 190 to optimize the subsequent non-occlusion projection angle.
After the above steps are performed, the position of the next suspected lesion is clicked in the three-dimensional model, and the above steps are repeated.
Compared with the prior art, the embodiment can evaluate and analyze the focus through the image acquired by the CT equipment before the operation, simultaneously use the neural network algorithm to eliminate the three-dimensional occlusion around the suspected focus, calculate a plurality of non-occlusion projection angles and a plurality of recommended radiography positions of the angiography machine, simultaneously judge whether the image is clear or not and has no occlusion according to the two-dimensional image in the operation acquired when the radiography machine moves to the recommended radiography position, fine tune the image if the position is not ideal, and return the more optimal position after fine tune to the neural network algorithm to optimize the algorithm, thereby ensuring that the subsequently output recommended radiography position is more accurate and applicable.
Example 3
Referring to fig. 3, the embodiment provides an angiography device 100 ″ based on CT images, which includes a stereo image acquisition module 110, a labeling module 120, a projection angle calculation module 130, an angiography position calculation module 140, a rotation driving module 150, a determination module 180, and an optimization module 190.
In this example, the specific application method of the present invention is as follows:
when carrying out vascular interventional operations such as PCI and the like, a CT modeling file of a diseased vessel of a patient is obtained through a CT device, the modeling file is input into a stereo image obtaining module 110 in an angiography device based on a CT image through a DICOM standard, then a suspected focus position is clicked in the modeling file through a marking module 120, the coordinate parameter of the focus position in a three-dimensional coordinate system is contained in the suspected focus position, the coordinate parameters of a plurality of focus positions are stored one by one, a projection angle calculating module 130 is used for eliminating three-dimensional occlusion around the suspected focus through a depth caching algorithm, a non-occlusion projection angle is calculated through a filtered wave back projection reconstruction algorithm, an angiography position calculating module 140 calculates recommended angiography positions, namely recommended angiography position 1 and recommended angiography position 2, of at least two angiography machines in each projection position according to the obtained non-occlusion projection angle, and then a C-shaped arm and an image enhancement module in the angiography machine are driven to rotate to the maximum extent through a rotation driving module 150 The optimal radiography position 1 is used for performing angiography, the judging module 180 is used for judging whether the intraoperative two-dimensional image acquired by the angiography machine at the recommended radiography position 1 meets observation expectation, if not, the angiography machine is rotated to the recommended radiography position 2 for performing angiography again, if not, the angiography machine is finely adjusted, and the angle parameters of the rotation of the C-shaped arm and the image intensifier in the finely adjusted angiography machine are returned to the projection angle calculating module 130 through the optimizing module 190 so as to optimize the subsequent non-shielding projection angle.
After the above steps are performed, the position of the next suspected lesion is clicked in the modeling file, and the above steps are repeated.
Example 4
In one possible design, an embodiment of the present invention provides an angiography method based on CT images, which mainly operates as follows:
obtaining a modeling file of a patient blood vessel output by CT equipment through a DICOM standard; then, the position of the suspected focus is clicked and marked in the modeling file, the position comprises the coordinate parameters of the focus in a three-dimensional coordinate system, and the coordinate parameters of the positions of a plurality of focuses are stored one by one; removing three-dimensional occlusion caused by blood vessels around the suspected lesion by a depth cache algorithm, and calculating at least one non-occlusion projection angle by a filtered wave back projection reconstruction algorithm; according to the obtained non-shielding projection angle, calculating recommended radiography positions of at least two angiograms in each projection position, namely a recommended radiography position 1 and a recommended radiography position 2, then driving a C-shaped arm and an image enhancement module in the angiograms to rotate to the optimal radiography position 1 for angiography, judging whether an intraoperative two-dimensional image acquired by the angiograms in the recommended radiography position 1 meets observation expectation or not, if not, rotating the angiograms to the recommended radiography position 2 for angiography again, if not, finely adjusting the angiograms, and re-recording and feeding back angle parameters of the rotation of the C-shaped arm and the image enhancement module in the finely adjusted angiograms to a filtering back-projection reconstruction algorithm to optimize the subsequent non-shielding projection angle.
Example 5
In one possible design, as shown in fig. 4, an embodiment of the present invention provides an angiography apparatus 200 based on CT images, where the apparatus 200 includes: one or more processors and storage for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the functions implemented by the apparatus as in any of embodiments 1-3. Further, the apparatus 200 is provided with an interface of DICOM standard, a display screen 210 and an operation panel 220.
In a preferred embodiment, the angiography device 200 based on CT image is installed on the system controller side of the angiography machine 300 and connected thereto, the image outputted from the CT device can be directly imported into the device 200 through the DICOM standard interface, the modeling file can be displayed on the display screen 210 thereof, and the button on the operation panel 220 can be used to click the modeling file to mark the position of the suspected lesion; after the recommended contrast position is calculated, it is displayed on the display screen 210. At this time, the C-arm 310 and the image intensifier 320 are rotated to the standard contrast body position by operating the system controller of the angiographic apparatus 300, and then the recommended contrast position is selected by clicking a button on the operation panel 220. In this embodiment, the recommended contrast position of the left crown in each standard position obtained by calculation is: LAO32 ° + CRA36 °, RAO28 ° + CRA31 °, RAO20 ° + CAU25 °, LAO41 ° + CAU31 °; or LAO30 ° + CRA33 °, RAO29 ° + CRA33 °, RAO19 ° + CAU21 °, LAO43 ° + CAU33 °; or LAO32 ° + CRA36 °, RAO28 ° + CRA31 °, RAO20 ° + CAU25 °, LAO41 ° + CAU31 °. Since three recommended contrast positions are provided in each of the standard contrast positions, the recommended contrast position close to the standard contrast position may be preferentially selected when selecting the recommended contrast position, for the sake of operation efficiency.
In this embodiment, the recommended contrast position of the right coronary artery obtained by calculation is: LAO42 °, AP + CRA21 °; or LAO40 °, AP + CRA30 °; or LAO45 °, CRA29 °. During the operation, the recommended contrast position is selected as above, and the recommended contrast position close to the standard contrast body position is preferentially selected.
After selecting the corresponding recommended radiography position, the C-arm 310 and the image intensifier 320 are driven to rotate to the recommended radiography position, and the display 330 of the angiographic apparatus 300 is observed, and it is determined whether the intraoperative two-dimensional image acquired by the angiographic apparatus 300 at the recommended radiography position meets the observation expectation, if not, the angiographic apparatus 300 is controlled to rotate to another recommended radiography position for re-radiography by clicking a key on the operation panel 220, and if not, the rotational angle parameters of the C-arm 310 and the image intensifier 320 are actively input on the operation panel 220 to fine-tune the angiographic apparatus 300, so as to optimize the subsequent calculation of the unobstructed projection angle.
Example 6
In one possible design, an embodiment of the present invention provides a computer-readable medium storing a computer program, which when executed by a processor implements the functions implemented by the apparatus according to any one of embodiments 1 to 3 or the angiography method according to embodiment 4.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments. The embodiment of the device corresponds to the embodiment of the method, so that the description of the embodiment of the device is relatively simple, and the related description can refer to the description of the embodiment of the method.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (13)
1. An angiographic apparatus based on CT images, comprising:
-a stereo image acquisition module for acquiring a modeling file output by the CT device;
-a marking module for marking the location of a suspected lesion clicked on in the modeling file;
-a projection angle calculation module for excluding three-dimensional occlusions due to blood vessels present around the suspected lesion and calculating at least one non-occluded projection angle;
-a contrast position calculation module for calculating a recommended contrast position of an image intensifier in an angiographic apparatus based on said unobstructed projection angle;
-a rotation driving module for driving the rotation of the angiographic camera to the recommended imaging position.
2. The angiography apparatus according to claim 1, wherein the modeling file is a preoperative medical image outputted by the CT device through DICOM standard.
3. The angiography device according to claim 1, wherein the location of the lesion comprises a coordinate parameter of the lesion in a three-dimensional coordinate system.
4. The angiography apparatus according to claim 1, wherein in the projection angle calculation module, all undesired angles which may affect the vessel view due to three-dimensional occlusion are excluded by a depth buffer algorithm, and at least one non-occluded projection angle is calculated by a filtered back-projection reconstruction algorithm.
5. The angiography device of claim 1, wherein the recommended angiography position comprises a rotation angle of a C-arm and a rotation angle of the image intensifier in the angiography machine.
6. The angiographic device according to any one of claims 1-5, further comprising:
-a model reconstruction module for reconstructing a three-dimensional model of the target vessel from the 3D U-Net framework in the full convolution neural network FCN;
-a registration module for registering the three-dimensional model with an intraoperative two-dimensional image acquired by rotation of the angiographic camera to the recommended contrast position;
-a determining module for determining whether the registered images meet an observation expectation;
-an optimization module for modifying the position of the angiographic apparatus and returning the position to the projection angle calculation module for optimizing the unobstructed projection angle.
7. The angiography device according to claim 6, wherein in the model building module, the full convolution neural network FCN with the multi-layer convolution layer as the main structure is used for identifying the structure of the target organ, and then the 3D U-Net framework is used for automatically segmenting and reconstructing the target organ.
8. The angiography device according to claim 6, wherein in the registration module, the reconstructed three-dimensional model is globally transformed by a global registration method and is globally aligned with the intraoperative two-dimensional image, and the registration of the two is realized by non-rigid local deformation.
9. The angiographic device of claim 6 wherein the view is intended to be a clear and unobstructed view of the vessel at the lesion site.
10. An angiography apparatus based on a CT image, the apparatus comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the functions implemented by the apparatus of any of claims 1-9.
11. The angiographic apparatus according to claim 10, wherein said apparatus further comprises a DICOM standard interface, display screen and operating panel;
the interface is used for importing a modeling file output by the CT equipment;
the display screen is used for displaying the modeling file and the recommended radiography position;
the operation panel is provided with keys for clicking the position of the suspected lesion on the modeling file, selecting the recommended radiography position and controlling the angiography machine to rotate to the recommended radiography position.
12. The angiography device of claim 11, wherein the operation panel is further configured to actively input rotation angle parameters of a C-arm and an image intensifier of the angiography machine, and control the angiography machine to rotate to a corresponding angle.
13. A computer-readable medium, in which a computer program is stored, which program, when executed by a processor, is adapted to carry out the method of:
acquiring a modeling file output by CT equipment;
marking the position of the suspected lesion clicked in the modeling file;
eliminating three-dimensional occlusion caused by blood vessels existing around the suspected lesion, and calculating at least one non-occlusion projection angle;
calculating a recommended radiography position of an image intensifier in the angiography machine according to the non-shielding projection angle;
driving the angiography machine to rotate to the recommended angiography position.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117224348A (en) * | 2023-11-16 | 2023-12-15 | 北京唯迈医疗设备有限公司 | Method and system for automatically adjusting contrast position based on interventional contrast surgery |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040125103A1 (en) * | 2000-02-25 | 2004-07-01 | Kaufman Arie E. | Apparatus and method for volume processing and rendering |
| CN201683906U (en) * | 2008-12-04 | 2010-12-29 | 北京集翔多维信息技术有限公司 | Real time blood vessel shaping imaging system based on CT 3D rebuilding and angiography |
| CN102945328A (en) * | 2012-11-22 | 2013-02-27 | 北京理工大学 | Simulation method for X ray angiography image based on graphic processing unit (GPU) parallel computation |
| CN106327479A (en) * | 2016-09-23 | 2017-01-11 | 西安电子科技大学 | Apparatus and method for identifying blood vessels in angiography-assisted congenital heart disease operation |
| CN109890462A (en) * | 2016-08-29 | 2019-06-14 | 爱可瑞公司 | Online angle Selection in rotation imaging and tracking system |
| CN111297448A (en) * | 2020-02-24 | 2020-06-19 | 东软医疗系统股份有限公司 | Puncture positioning method, device and system |
-
2022
- 2022-01-19 CN CN202210061792.8A patent/CN114469153B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040125103A1 (en) * | 2000-02-25 | 2004-07-01 | Kaufman Arie E. | Apparatus and method for volume processing and rendering |
| CN201683906U (en) * | 2008-12-04 | 2010-12-29 | 北京集翔多维信息技术有限公司 | Real time blood vessel shaping imaging system based on CT 3D rebuilding and angiography |
| CN102945328A (en) * | 2012-11-22 | 2013-02-27 | 北京理工大学 | Simulation method for X ray angiography image based on graphic processing unit (GPU) parallel computation |
| CN109890462A (en) * | 2016-08-29 | 2019-06-14 | 爱可瑞公司 | Online angle Selection in rotation imaging and tracking system |
| CN106327479A (en) * | 2016-09-23 | 2017-01-11 | 西安电子科技大学 | Apparatus and method for identifying blood vessels in angiography-assisted congenital heart disease operation |
| CN111297448A (en) * | 2020-02-24 | 2020-06-19 | 东软医疗系统股份有限公司 | Puncture positioning method, device and system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117224348A (en) * | 2023-11-16 | 2023-12-15 | 北京唯迈医疗设备有限公司 | Method and system for automatically adjusting contrast position based on interventional contrast surgery |
| CN117224348B (en) * | 2023-11-16 | 2024-02-06 | 北京唯迈医疗设备有限公司 | System for automatically adjusting radiography position |
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