36 results about "Coronary artery calcification" patented technology

Imaging systems and methods for viewing medical images of human anatomy and, in particular, to a 3-dimensional imaging system that allows a user to efficiently and accurately detect and view coronary artery calcification as displayed graphically on a computer screen. In one aspect, a method for displaying medical images comprises obtaining an image dataset comprising anatomical image data (step 50), automatically grouping connected components in the image data to form groups of connected components (steps 50-57), and displaying the groups of connected components are distinguishable in the displayed image (58-59). The image dataset may comprise a volume data set and the groups of connected components comprise regions of neighboring voxels that share a similar property. The image dataset may comprise a 2-dimensional data set and the groups of connected components comprise regions of neighboring pixels that share a similar property. Different groups of connected components may be displayed in different colors and / or different opacities or certain groups may not be displayed at all.

The present invention is directed to a method and apparatus of scoring an arterial obstruction. The present invention includes identifying a particular group of pixels corresponding to a calcified region of coronary artery of a patient and based on the satisfaction of a number of thresholds connectivity criteria, determining a calcification score of that calcified region as indicative of the patient's risk of cardiovascular disease including atherosclerosis, heart attack, heart failure, and stroke. The present invention further includes determining the scaling factor unique to a particular imaging slice of data to provide a value of calcium weight and volume that is indicative of the true obstruction and true volume of the calcified region.

The present invention relates to an image processing technique for effectively suppressing noise by providing a fully automatic calcification patch detection, segmentation and quantization apparatus and method based on fuzzy super pixel clustering in CTA data. The technical scheme adopted in the present invention is the method for the enhancement of coronary artery calcification detection and quantification in CT image. The method comprises the following steps: first, using a seed point to automatically choose a low threshold region to grow and obtain a coronary artery region containing the calcification patch; then using the fuzzy C-means clustering algorithm to divide the above-mentioned blood vessel region into a finite number of super-pixel sets according to the Euclidean distances among the pixel points and the gray scale differences of the pixels in the region; and finally, using a threshold selection method based on gray histogram to screen the super pixel sets to obtain the final measurement and quantification result of the calcification patch, and completing the calcification score calculation of the blood vessel according to the segmentation result. The invention is used mainly in image processing.

Method and system for using segmentation and three-dimensional image-processing techniques of a method to create an automatic method to detect coronary artery calcification deposits and identify their location with respect to a major coronary artery. Finally, different scoring algorithms can be applied to generate calcium scores such as Agatston Janovitz, Mass, and Volume. A total score and a more detailed score can be generated based on the major arteries such as right coronary artery, left anterior descending artery, or left circumflex artery to be incorporated into the patient report.

Coronary artery calcification (CAC) occurs at an earlier age in diabetes and is a risk factor for coronary artery disease (CAD) in subjects with or without diabetes. One postulated mechanism for the increased CAC is the accelerated accumulation of advanced glycation end products (AGEs) in the vasculature. As certain collagen AGEs fluoresce, skin intrinsic fluorescence (SIF) can act as a novel maker of collagen AGEs levels. The present invention provides methods and apparatuses for detecting SIF that can be a useful marker of CAD risk and a therapeutic target.

The invention provides a method, device and system for obtaining coronary-artery-calcification area classification, and relates to the technical field of calcification area selection. The method includes the steps that coronary-artery areas are obtained based on obtained three-dimensional information of CT plain scan information; suspected calcification areas are obtained from the obtained coronary-artery areas according to the certain condition; according to the preset condition, whether the suspected calcification areas need to be cut or not is judged, suspected calcification areas meeting the condition are cut; according to the characteristics of the cut suspected calcification areas, the suspected calcification areas are classified, and the classified suspected calcification areas areobtained. According to the method, device and system for obtaining coronary-artery-calcification area classification, the technical problems that calcification areas are misclassified when the calcification areas are manually marked, and coronary-artery-calcification-area selection and classification are large in error, complex in process and low in efficiency are solved, and coronary-artery-calcification-area selection and classification are simple in structure, high in efficiency and small in error.

The invention provides a method, apparatus and system for calculating coronary artery calcification integral, and relates to the technical field of calcification score. The method comprises: the coronary artery regions are obtained based on plain CT information, Suspected calcification regions are obtained from the acquired coronary artery regions according to certain judgment conditions, and thesuspected calcification regions are classified according to the image characteristics of the suspected calcification regions to obtain the classified coronary artery calcification regions, and the coronary artery calcification scores are obtained based on the calculated calcification scores of the classified coronary artery calcification regions. The invention provides a method, apparatus and system for calculating coronary artery calcification integral, , which can alleviates the technical problems of inaccurate marking or inaccurate marking range and low efficiency of artificially marking the coronary artery calcification area in the prior art, thereby inaccurate integrating of the coronary artery calcification area, low efficiency of calculating integrating and large error, thereby improving the efficiency of calculating the coronary artery calcification integral and reducing the error of calculating.

The present invention provides an automatic heart disease detection system based on a convolutional neural network. The automatic heart disease detection system comprises a database, a lesion information processing module, a key frame extraction module, a data cleaning module, an information fusion and storage module, a convolutional neural network learning module, a model storage and screening module and a lesion detection module. The invention relates to the fields of deep learning, medical treatment and computer vision. By design of scientific optimization, a set of automatic intelligent detection system for coronary artery calcification, complete occlusion and thrombosis based on the convolutional neural network is created and achieved, aiming at the requirements of diagnosis on resultaccuracy and timeliness, the design structure is ingenious, a high-quality auxiliary diagnosis detection result can be output without any manual assistance, and convenience is brought to coronary artery patients and doctors.

The invention relates to a myocardial perfusion development and coronary artery calcification score combined diagnostic system and method. The diagnostic system comprises a myocardial perfusion development system, a coronary artery calcification score system and a data processing system. Both the myocardial perfusion development system and the coronary artery calcification score system are based on a siemens Symbia T16 SPECT / CT image system, the myocardial perfusion development system is used for collecting nuclein myocardial perfusion development of patients and obtaining diagnostic data by analyzing images. The coronary artery calcification score system works after the myocardial perfusion development system finishes work and is used for collecting coronary artery calcification score data and sending the collected data to an Agatston automatic analysis software system to be computed and analyzed, diagnostic data obtained through the myocardial perfusion development system is combined with the analysis data of the coronary artery calcification score system to be sent to the data processing system to be processed, and the diagnostic result is obtained through statistical analysis. The myocardial perfusion development and coronary artery calcification score combined diagnostic system and method achieve one-stop examination and have the advantages that the system and method are easy and convenient to operate, safe and reliable, the diagnosis time is saved, the cost is saved, the diagnosis efficiency is improved, and the diagnosis accuracy is improved.

The invention discloses a coronary artery calcification lesion full-automatic segmentation method based on chest flat scanning CT, and the method comprises the following steps: carrying out the preprocessing of an original chest flat scanning CT image, and obtaining preprocessing data; inputting the preprocessed data into a heart segmentation model, and segmenting a heart region in the original chest flat-scanning CT image to obtain a heart segmentation image which corresponds to the original chest flat-scanning CT image and contains the heart region; screening suspected coronary artery calcification lesions in the heart area; extracting a plurality of candidate sample blocks from the screened suspected coronary artery calcification lesions; and based on the plurality of candidate sample blocks and the corresponding coordinates of the candidate sample blocks in the coordinate system corresponding to the original chest horizontal scanning CT image, obtaining coronary artery calcification focus segmentation results of the candidate sample blocks by using a calcification focus segmentation model. According to the method, a coarse-to-fine strategy is adopted, a two-stage deep neural network is used for achieving full-automatic coronary artery calcification focus segmentation based on chest flat scanning CT, and a good segmentation result can also be obtained for data with large noise.

The invention relates to a coronary artery calcification plaque segmentation method, a coronary artery calcification integral calculation method and a computer readable storage medium. The method comprises the following steps: acquiring a first medical image of a heart area; classifying each voxel of the first medical image by using a preset segmentation model to obtain a first classification result of classifying each voxel according to a parent category and a second classification result of classifying each voxel according to a subcategory; according to the parent category of each voxel in the first medical image, correcting the subcategory of each voxel in the first medical image to obtain a third classification result; and obtaining a coronary artery calcification plaque segmentation result of the first medical image according to the third classification result. According to the method and the device, the problem of low robustness of a coronary artery calcification plaque segmentation method in related technologies is solved, and the robustness of coronary artery calcification plaque segmentation is improved.

The invention relates to a coronary artery calcification plaque detection method and device, and the method comprises the steps: carrying out the segmentation processing of a to-be-processed image, and determining a plurality of target regions of a first organ in the to-be-processed image; for any target area, determining a first gray threshold and a second gray threshold of the target area according to the gray information of the target area; respectively determining a suspected focus area and a pseudo focus area in the target area according to the first gray threshold and the second gray threshold of the target area; and determining the focus area of the first organ according to the suspected focus area and the pseudo focus area of each target area. According to the embodiment of the invention, omission of the calcified plaque at the small branch blood vessel of the coronary blood vessel in the to-be-processed image can be effectively reduced, the recall rate of the calcified plaque is improved, and the accuracy of identification of the calcified plaque can be remarkably improved.

The invention discloses a multi-view shape constraint system, which comprises a slicing unit for slicing a received image to obtain a coronal slice, a sagittal slice and an axial slice; a first expansion Unet unit, a second expansion Unet unit and a residual expansion network RDN unit for receiving the coronal slice, the sagittal slice and the axial slice respectively; a multi-view shape constraint unit used for integrating different view information through multi-view attention learning to generate multi-view attention features; and a multi-task learning unit that estimates a related calcification index using the output of the multi-view shape constraint unit. The first expansion Unet unit and the second expansion Unet unit model the coronal slice and the sagittal slice and obtain prediction graphs of the coronal view and the sagittal view as supplementary information of calcification lesions, and the residual error expansion network RDN unit models the axial slice and outputs the prediction graph of the axial slice.

The invention provides a method, apparatus and system for calculating coronary artery calcification integral, and relates to the technical field of calcification score. The method comprises: the coronary artery regions are obtained based on plain CT information, Suspected calcification regions are obtained from the acquired coronary artery regions according to certain judgment conditions, and thesuspected calcification regions are classified according to the image characteristics of the suspected calcification regions to obtain the classified coronary artery calcification regions, and the coronary artery calcification scores are obtained based on the calculated calcification scores of the classified coronary artery calcification regions. The invention provides a method, apparatus and system for calculating coronary artery calcification integral, , which can alleviates the technical problems of inaccurate marking or inaccurate marking range and low efficiency of artificially marking the coronary artery calcification area in the prior art, thereby inaccurate integrating of the coronary artery calcification area, low efficiency of calculating integrating and large error, thereby improving the efficiency of calculating the coronary artery calcification integral and reducing the error of calculating.

A method of predicting a potential degree of Coronary Artery Calcification (CAC) risk includes receiving a patient's medical test data relating to CAC; determining a cluster to which the patient's medical test data belong based on an age of the patient; extracting a risk factor score including at least two Coronary Artery Calcification Scores (CACSs) from the medical test data; storing a plurality of prediction models used for predicting a potential degree of CAC risk; and predicting a potential degree of CAC risk at a specific point in time by applying a CACS growth rate of the patient's medical test data calculated using the at least two CACSs of the patient's medical test data and the extracted risk factor score to a prediction model corresponding to the determined cluster to which the patient's medical test data belong among the plurality of prediction models.

The invention relates to a method for calculating a coronary artery calcification ratio based on end-to-end reasoning of a convolutional neural network. The method comprises the following steps: determining a CT image group to be input; then, segmenting the CT image group into grid 3D images; inputting the information of the image into a neural network to extract abstract features; performing neural network training on whether the image is a heart or not through a first full connection layer; performing neural network training for judging whether the image has coronary artery or not through asecond full connection layer; performing neural network training for judging whether calcification exists in coronary artery of the image or not through a third full connection layer; performing neural network training on the position of the coronary artery of the image through a fourth full connection layer; and performing neural network training of the calcification proportion of the coronary artery of the image through a fifth full connection layer. The method solves the problem of how to simulate a plurality of judgments performed by an imaging doctor in the process of judging the calcification of a case, and provides the end-to-end neural network capable of performing layer-by-layer reasoning.

The invention provides a CFD-based coronary artery blood flow simulating system and method. The simulating system comprises a Computed tomography (CT) data obtaining system, a blood vessel 3D model reconstruction system, a CFD pre-processing system, a CFD computation system and a post-processing system, wherein the CT data obtaining system obtains a CT image sequence of cardiovascular system of the human body; the blood vessel 3D model reconstruction system carries out 3D reconstruction on the extracted image sequence to obtain a 3D coronary-artery blood vessel model; the CFD pre-processing module carries out pre-processing required by hydrodynamic computation on the 3D coronary-artery blood vessel model; the CFD computation system resolves a 3D flow control equation of blood in the coronary artery blood vessel and outputs a computation result; and the post-processing system adds the computation result to the corresponding position of the 3D coronary-artery blood vessel model, and displays color cloud atlas. The coronary artery FFR is calculated and used as the standard for assessing the coronary artery function, disadvantages in determining the blood supply state of the cardiac muscle in the coronary artery anatomy aspect are overcome patients are not sound, and the cost is lower.

The invention relates to the technical field of coronary rotational atherectomy, and particularly relates to a rotational atherectomy interventional treatment instrument for treating a coronary arterycalcification lesion. The rotational atherectomy interventional treatment instrument comprises a rotational atherectomy instrument host, a pedal control board, a nitrogen tank, a propeller, a rotational atherectomy catheter and a rotational atherectomy guide wire; a speed reduction switch is arranged on the pedal control board, and the outside of the pedal control board is communicated with a speed reduction control pipe and two ventilation hoses; a gas outlet connector is arranged at the top end of the nitrogen tank, and the gas outlet connector is communicated with a nitrogen interface by agas supply pipe; the propeller comprises a machine body, a moving handle, an optical fiber rotation speed connection cable, a saline solution input pipe and a gas connection pipe; a strip slot is formed on the upper end face of the machine body; the moving handle is mounted in a sliding mode inside the strip slot; scale lines are also formed on both front and rear sides of the strip slot; and a handle travel control structure is also mounted in a sliding mode on the machine body. According to the invention, a forward distance of a rotational atherectomy head can be limited, so as to effectively avoid a deviation generated due to artificial control on the front and rear process of the rotational atherectomy head and protect a coronary artery canal behind the calcification lesion position from being damaged.

An apparatus and a method predict a patient's potential change of Coronary Artery Calcification (CAC) level using various risk factors including a Coronary Artery Calcification Score (CACS). The apparatus includes a receiving unit, a cluster determining unit, a risk factor score extracting unit, a prediction model storage unit, a prediction model learning unit, and a predicting unit, and the method includes a receiving process, a risk factor score extracting process, and an operation performing process.

The invention discloses a full-automatic CT image coronary artery calcification score calculating method. The method includes the steps that a heart and an aorta area are divided by using coronary artery CT contrastographic images of the same patient; the central axis of each coronary artery is extracted and named; the results are mapped to a common CT image through image registration to generate regions of interest used for detecting calcification points, and therefore the calcification points on blood vessel walls of the coronary arteries in the CT image can be positioned accurately. In addition, the calcification points belonging to different main branches of the coronary arteries can be differentiated by using the naming results of the central axes of the coronary arteries. The accuracy of coronary artery calcification score calculation and the efficiency of computer-aided diagnosis can be effectively improved through the method.

The invention relates to an automatic detection method for coronary artery calcified plaques based on cardiac CT images, which belongs to the technical field of biomedical engineering. The technology is based on three-dimensional reconstruction of cardiac CT images, and uses a series of features to describe and distinguish coronary plaques from different angles. Arterial calcified plaques, followed by classification and quantification of coronary artery calcified plaque candidates in four stages, enabling automatic detection of calcified plaques. This technology can automatically and quickly detect and measure calcified plaques in a group of cardiac CT images and identify the coronary branches where they are located, which can effectively reduce the manual burden and errors caused by subjective factors, and improve the efficiency and accuracy of image reading , has important clinical significance, and obtains considerable social and economic benefits.

The present disclosure relates to a method and device for detecting coronary artery calcified plaques. The method includes: performing segmentation processing on an image to be processed, and determining multiple target areas of the first organ in the image to be processed; for any target area, according to the target area According to the grayscale information of the target area, determine the first grayscale threshold and the second grayscale threshold; according to the first grayscale threshold and the second grayscale threshold of the target area, respectively determine the suspected lesion area and the pseudo lesion area in the target area ; Determining the lesion area of ​​the first organ according to the suspected lesion area and the pseudo lesion area of ​​each target area. According to the embodiments of the present disclosure, not only can effectively reduce the omission of calcified plaques in small branch vessels of coronary vessels in the image to be processed, improve the recall rate of calcified plaques, but also significantly improve the accuracy of calcified plaque identification.

The present invention relates to an image processing technique for effectively suppressing noise by providing a fully automatic calcification patch detection, segmentation and quantization apparatus and method based on fuzzy super pixel clustering in CTA data. The technical scheme adopted in the present invention is the method for the enhancement of coronary artery calcification detection and quantification in CT image. The method comprises the following steps: first, using a seed point to automatically choose a low threshold region to grow and obtain a coronary artery region containing the calcification patch; then using the fuzzy C-means clustering algorithm to divide the above-mentioned blood vessel region into a finite number of super-pixel sets according to the Euclidean distances among the pixel points and the gray scale differences of the pixels in the region; and finally, using a threshold selection method based on gray histogram to screen the super pixel sets to obtain the final measurement and quantification result of the calcification patch, and completing the calcification score calculation of the blood vessel according to the segmentation result. The invention is used mainly in image processing.

The present invention relates to a method and a device for identifying and assigning coronary calcification to at least one coronary vessel of a heart (H) in a patient (P), wherein coronary calcification is identified on the basis of a 3D image data record of the heart (H) in the patient (P), which data record was generated by an imaging instrument without contrast agent being administered; at least one 3D model (30) of the profile of the coronary vessels (31 to 34) in a human is provided; and wherein the identified coronary calcification in the patient (P) is assigned to at least one coronary vessel (31 to 34) of the at least one 3D model (30) of the profile of the coronary vessels (31 to 34) in a human. Moreover, the invention relates to a computer program product, having a computer program (13) that is stored on a medium that can be read by a computational unit (12), for carrying out the method for identifying and assigning coronary calcification when the computer program (13) is loaded into the computational unit (12).

The invention relates to a blood miR-3135b detection kit used for diagnosis of coronary artery vascular calcification, which includes a primer for amplifying miR-3135b. In addition, the invention also relates to an application of such primer for amplifying the miR-3135b in preparation of the blood detection kit for diagnosis of the coronary artery vascular calcification.

The invention discloses a method for fully automatic segmentation of coronary calcification lesions based on plain CT of the chest, comprising the following steps: performing preprocessing on the original plain CT image of the chest to obtain preprocessed data; In this method, the heart region in the original chest plain scan CT image is segmented to obtain a heart segmentation image corresponding to the original chest plain scan CT image containing the heart region; the suspected coronary artery calcification is screened in the heart region; Extract multiple candidate sample blocks from arterial calcifications; and based on the multiple candidate sample blocks and their corresponding coordinates in the coordinate system corresponding to the original chest CT image, use the calcification focus segmentation model to obtain the coronary artery calcifications of the candidate sample blocks Split results. The method of the present invention adopts a coarse-to-fine strategy, and uses a two-stage deep neural network to realize the automatic segmentation of coronary artery calcification based on chest plain scan CT, and can also obtain good segmentation results for data with relatively high noise.

Provided is an agent that binds to a wild type or variant pre-miRNA-1229 comprising a G-quadruplex (GQ) structure, wherein binding of the agent to the wild type or variant pre-miRNA-1229 stabilizes the GQ structure of the wild type or variant pre-miRNA-1229. In some embodiments, the variant is rs2291418. Provided is a method of treating a disease in a subject, comprising: administering a therapeutically effective amount of the agent to the subject. In some embodiments, the disease is Alzheimer's disease, cancer or coronary artery calcification.

A method for calculating a coronary artery calcium score, the method comprising acquiring a target image for a coronary artery and myocardium before contrast enhancement, identifying the coronary artery included in the target image by using an artificial neural network, calculating a coronary artery calcium score based on the identified coronary artery, wherein the artificial neural network is trained based on a training database generated via alignment between a pre-acquired image of a coronary artery and myocardium before contrast enhancement and a pre-acquired image of a coronary artery and myocardium after contrast enhancement.