36 results about "Coronary artery calcification" patented technology

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.

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 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.

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.

Systems and methods are provided for training an artificial intelligence model for detecting a calcified portion of a blood vessel in an input medical image. One or more first medical images of a blood vessel in a first modality and one or more second medical images of a blood vessel in a second modality are received. A calcified portion of a blood vessel is detected in one or more first medical images. The artificial intelligence model is trained for detecting a calcified portion of the blood vessel in the input medical image in the second modality based on the one or more second medical images and the calcified portion of the blood vessel detected in the one or more first medical images.

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 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.