3results about How to "Accurate Reference Data" patented technology

The present invention discloses a prostate cancer lesion detection method based on PET / CT images, and relates to the technical field of medical image processing. By acquiring the PSMA PET / CT images of the user, the soft tissue window CT images and the lung window CT images are extracted according to the CT images; the CT images are segmented to obtain organ segmentation images; the PSMA PET images, the soft tissue window CT images, the lung window CT images, and the organ segmentation images are respectively input into the PET lesion detection model and the CT lesion detection model to obtain the PET lesion detection images and the CT lesion detection images; and the PSMA PET / CT lesion detection information is generated according to the two lesion detection images. The present invention combines the PSMA PET / CT images with the CT anatomical information, and can more accurately detect and segment lesions, provide more accurate reference data for clinicians, and provide better data support for the formulation of individualized treatment plans.

This invention provides a wind tunnel test angle-of-attack adjustment device and its calculation method. The device includes an angle-of-attack mechanism, a suction mechanism, a front wind speed sensing component, and a rear wind speed sensing component. The suction mechanism includes a suction channel and a fan. Both the front and rear wind speed sensing components include a high-sensitivity sensor and a lifting drive component. The front wind speed sensing component also includes a low-sensitivity sensor, located downstream of the suction channel. The high-sensitivity and low-sensitivity sensors are connected to the fan and the lifting drive component via a control unit. The method includes selecting a working sensor based on the test angle of attack adjusted by the angle-of-attack mechanism; setting the target boundary layer thickness and the wind tunnel set flow rate; adjusting the real-time power of the fan; controlling the height of the working high-sensitivity sensor; and obtaining the flow rates of the high-sensitivity and low-sensitivity sensors; adjusting the real-time power of the fan based on the flow rates of the high-sensitivity and low-sensitivity sensors. This invention has advantages such as effectively controlling the boundary layer thickness and improving test accuracy.

The present application relates to a kind of methods for predicting porous medium structure using machine learning, comprising: obtaining the image of porous medium prepared in different working conditions and corresponding parameter information;According to phase state characteristics, image is processed, and image dataset is obtained;From image dataset, optionally two images of different working conditions are selected, and with corresponding parameter information, a training sample is formed;Using several training samples, generative adversarial network model is trained, which uses unsupervised image-to-image conversion algorithm for training, for each input training sample, the reconstruction image of original image and predicted image are output, the model is verified by the output predicted image, and the prediction model is obtained after training;The image of porous medium prepared in known working condition and corresponding parameter information are input into prediction model together with the preset parameter information of target object, and the predicted image of target object is obtained, so that the structure prediction of target object can be efficiently and accurately obtained by the present application.