6 results about "Wall motion" patented technology

The application relates to a method and device for estimating a ventricular wall motion based on an adaptive regularization optical flow model, belonging to the technical field of ultrasonic image processing. The method is characterized in that: firstly, the obtained optical flow vector is analyzed through two-dimensional wavelet analysis and function mapping to obtain motion boundary information of the motion field relative to sudden change; secondly, the sudden change motion boundary information is adaptively fed back to the optical flow calculation model to correct the regularization coefficient of the smoothing term and recalculate the optical flow field; finally, the optical flow calculation process is repeated until the wavelet high-frequency component of the optical flow field converges; the method reflects the gradient difference between the myocardial ventricular wall and the surrounding tissue in the regularization coefficient, thereby improving the accuracy of motion estimation.

Pulse wave velocity (PWV) is a measure of arterial stiffness and a cardiovascular disease risk factor. Accurate PWV estimation is difficult due to complex arterial dynamics, such as longitudinal motion and natural tissue oscillations. This present disclosure relates to a robust, motion-resistant PWV estimation framework including: 1) tracking and compensating for arterial longitudinal wall motions, 2) suppressing undulating arterial motion via common mode rejection, and 3) enhancing differential signals to extract wall expansion. In vitro experiments with induced lateral motion indicate the framework's PWV estimates (6.19±0.33 m / s) closely matched reference values (6.26±0.12 m / s; error: 1.1%), outperforming methods without motion compensation (4.46±1.78 m / s; error: 28.8%). In vivo trials with five healthy subjects showed an average PWV of 4.18±0.56 m / s using the motion-resistant method, compared to 2.54±0.95 m / s without motion compensation (p<0.005). This framework enhances PWV estimation reliability, offering clinical potential for better arterial stiffness assessment and cardiovascular risk stratification.

Pulse wave velocity (PWV) is a measure of arterial stiffness and a cardiovascular disease risk factor. Accurate PWV estimation is difficult due to complex arterial dynamics, such as longitudinal motion and natural tissue oscillations. This present disclosure relates to a robust, motion-resistant PWV estimation framework including: 1) tracking and compensating for arterial longitudinal wall motions, 2) suppressing undulating arterial motion via common mode rejection, and 3) enhancing differential signals to extract wall expansion. In vitro experiments with induced lateral motion indicate the framework's PWV estimates (6.19±0.33 m / s) closely matched reference values (6.26±0.12 m / s; error: 1.1%), outperforming methods without motion compensation (4.46±1.78 m / s; error: 28.8%). In vivo trials with five healthy subjects showed an average PWV of 4.18±0.56 m / s using the motion-resistant method, compared to 2.54±0.95 m / s without motion compensation (p<0.005). This framework enhances PWV estimation reliability, offering clinical potential for better arterial stiffness assessment and cardiovascular risk stratification.

The application discloses a kind of non-contact emotion recognition methods based on frequency-modulated continuous wave radar, belong to high-tech transformation traditional industry and non-contact emotion monitoring field.The method mainly includes the following processes: (1) by transmitting antenna array transmitting frequency-modulated continuous wave signal and by receiving antenna array receiving;(2) by MVDR algorithm, extract the echo signal from the chest wall of monitoring object in radar echo signal;(3) from the phase of radar echo signal, demodulate the motion signal of the chest wall of monitoring object;(4) by filter, extract the respiratory signal from the chest wall motion signal;(5) extract the duration of successive heartbeat from the motion signal of the chest wall of monitoring object;(6) calculate the physiological characteristics for emotion recognition;(7) train random forest machine learning model to realize emotion recognition.The application uses microwave as detection medium, has many advantages, such as convenient to use, low requirement to environment, fast measurement and higher accuracy.

This application discloses a plaque segmentation model training method, an information processing method, apparatus, device, and medium for carotid plaque segmentation, relating to the field of information processing technology. The method includes: performing cardiac cycle detection on raw carotid ultrasound video and normalizing the video frame sequence to cardiac phase to obtain a preprocessed carotid ultrasound video; constructing a target training dataset using the preprocessed carotid ultrasound video; including a first training dataset with segmentation annotations and a second training dataset without segmentation annotations; extracting plaque motion features and vessel wall motion features from the first training dataset to construct motion coupling constraints; training a plaque segmentation network model based on the target training dataset, and dynamically adjusting the weights of the loss term in the joint loss function according to the course learning strategy during training, so as to optimize the network parameters based on the joint loss function and motion coupling constraints to train a plaque segmentation model.

The application relates to a chamber wall segment motion estimation method based on confidence weight analysis of optical flow tracking, belongs to the technical field of ultrasonic image processing, and aims to solve problems such as the need to rely on subjective judgment of doctors and quality of echocardiographic images for myocardial functional abnormalities. Through optical flow calculation based on confidence weight analysis, motion vector decomposition and ROI region dynamic tracking, the dynamic motion field of each segment is accurately calculated, and finally, the myocardial segment motion time curve is obtained. The optical flow calculation method based on confidence weight analysis corrects the motion amount from top to bottom through a Gaussian pyramid algorithm, combines image correction technology, adds an image "confidence weight matrix", greatly reduces the calculation proportion of the "shadow" place by using the confidence weight coefficient, and thus improves the accuracy of optical flow calculation. Through motion vector decomposition, ROI region dynamic tracking is realized, the motion characteristic information of the myocardial segment is more accurately reflected, and the calculation precision of the myocardial chamber wall motion is improved.