8 results about "Time intensity curve" patented technology

The invention discloses an ultrasonic contrast time intensity curve quantitative analysis method and system and a medium, and relates to the technical field of medical image ultrasonic contrast. Comprising the following steps: acquiring an ultrasound contrast dynamic image sequence of a tumor focus; delimiting a focus ROI (Region of Interest) of the dynamic image sequence; drawing an ultrasound contrast time intensity curve by taking time as an abscissa and the gray value of each frame of image in the focus ROI region as an ordinate; using an encoder to extract the features of the ultrasound contrast time intensity curve, and using a decoder to reconstruct the features of the ultrasound contrast time intensity curve into a smooth time intensity curve; identifying key time nodes of the smooth time intensity curve through an intelligent inflection point detection algorithm; acquiring an intensity parameter of the smooth time intensity curve through a self-adaptive segmentation integration technology; carrying out quantitative analysis on the ultrasound contrast time intensity curve based on the key time node and the intensity parameter of the corresponding tumor focus; according to the invention, the efficiency and accuracy of time intensity curve quantitative analysis are improved.

An ultrasound imaging disclosure according to an embodiment of the present disclosure relates to an ultrasound imaging apparatus configured to irradiate ultrasonic waves to an object to which an ultrasonic contrast agent is administered, receive an echo signal reflected from the object, and analyze a contrast enhanced ultrasound image for the object, the ultrasound imaging apparatus including an image processor configured to set a region of interest in the ultrasound image, and generate a time intensity curve representing brightness of each frame of the ultrasound image within the set region of interest and a curve fitting model representing a tendency of the time intensity curve according to a predetermined mathematical model.

of the disclosure: An ultrasound imaging system acquires, stores, and displays a sequence of ultrasound images acquired during a cycle of contrast agent wash-in and wash-out. A time-intensity curve of contrast perfusion is produced from the sequence of images and is used by a PSF thinning processor to adaptively vary the degree of PSF thinning for different phases of contrast perfusion. Preferably the PSF thinning is varied during the processing and replay of stored images from a low degree of PSF thinning which images microbubbles with high sensitivity but low resolution at the start of contrast agent infusion, to a high degree of PSF thinning at peak perfusion for the production of high resolution, low sensitivity images of small vasculature.

The invention provides a method and a system for automatically extracting ultrasonic contrast peak frames to distinguish benign and malignant nodules. An ultrasonic contrast video is analyzed based on deep learning and a time intensity curve; through an improved YOLOv5 target detection and segmentation model and in combination with a region positioning adaptive algorithm, accurate positioning and continuous tracking are carried out on nodules in the ultrasound contrast video; and time intensity curve analysis is completed by adaptively selecting a reference region, peak frames and peak time of an ultrasound contrast video are efficiently extracted and input into a deep learning classification model, and the function of predicting and classifying benign and malignant gland nodules is realized. According to the method, the consistency and comparability of clinical diagnosis are remarkably improved, the method has the advantages of being high in efficiency, high in robustness and the like, the analysis efficiency is greatly improved, and the consistency of cross-platform analysis results is improved; the method is full in process automation, high in clinical practicability, good in clinical transformation prospect and convenient for secondary development, popularization and application of scientific research institutions and medical institutions.

The application provides a method and system for dividing tumor perfusion sub-regions based on dynamic ultrasound contrast data, the method comprising obtaining pre-processed dynamic ultrasound contrast images and pixelizing, so as to obtain time-intensity curves of each pixel point of tumor tissue and average time-intensity curves of normal tissue, obtain perfusion characteristic parameter ratios of each pixel point of tumor tissue, generate perfusion sub-regions of the tumor through clustering results of clustering of all pixel points in the interval, and map information thereof back to the original image. The application maximizes the use of dynamic image data through a non-invasive diagnostic method, comprehensively captures perfusion characteristics of the tumor from the pixel level, visualizes and quantifies tumor perfusion heterogeneity, improves the readability of the dynamic contrast video, helps to explore the correlation between tumor sub-regions and benignity and malignancy and construct a diagnostic model accordingly, improves the diagnostic accuracy of breast cancer, and provides more information support for personalized treatment of patients.

The application discloses a kind of prostate cancer lesion detection model construction method based on contrast image feature extraction, belong to image processing field, including the following steps: establishing training sample, establishing prostate cancer lesion detection model and training prostate cancer lesion detection model;Prostate tumor detection model contains space-time feature extraction network and target detection network.In the establishment training sample, based on time intensity curve correction algorithm can automatically extract arteriolar microbubble in ultrasonic contrast video from the time segment of appearance to peak value, and prostate tumor detection becomes more sensitive.The prostate cancer lesion detection model of the application can richly extract the space-time features and prostate cancer lesions in the training samples by virtue of the advantages of three-dimensional deep learning, can adapt to different sizes of prostate cancer lesions, and makes use of the dynamic characteristics of ultrasonic contrast, and thus can improve the prediction accuracy of the lesions.

The application provides a bolt monitoring method and device based on electromagnetic ultrasonic and 3D phased array, relates to the technical field of bolt monitoring, and adopts a two-stage detection process. Firstly, preliminary screening is carried out through electromagnetic ultrasonic. An electromagnetic ultrasonic device excites ultrasonic waves and receives echo signals. According to the relationship between the intensity of echo signals and the time interval, the time-intensity curve of each detection section is constructed, and candidate sections with abnormal echo signals are quickly screened out. Through 3D phased array technology, comprehensive three-dimensional space scanning is carried out on the candidate sections. Through focusing ultrasonic waves in three-dimensional space for accurate scanning, the ultrasonic echo signals of each space point are obtained. By extracting the ultrasonic echo signal intensity of each space point and calculating the standardized deviation, abnormal points exceeding the set threshold can be marked. Through space aggregation analysis, multiple abnormal points are aggregated into abnormal areas, and the accuracy of defect positioning is further improved.

The application discloses the technical field of cavitation microbubble size detection, and relates to an effective cavitation microbubble size distribution estimation method based on a non-monotonous decrease time-intensity curve. recyc The microbubble size range participating in cavitation activity in the "effective window" within the pulse interval time ΔT is 0~R recyc The microbubble size range participating in cavitation activity in the "effective window" within the pulse interval time ΔT is 0~R n The microbubble size range participating in cavitation activity in the "effective window" within the pulse interval time ΔT is 0~R The microbubble size distribution obtained by fitting the time-cavitation intensity curve by changing the pulse interval time is the effective cavitation microbubble size distribution under the action of the pulse duration T. The application fills the technical gap, and can accurately calculate the size distribution of the effective cavitation microbubble by accurately simulating the dynamic behavior of the microbubble under the action of the ultrasonic pulse and combining the parameter fitting method.