7 results about "Effective atomic number" patented technology

A method is provided for generating a medical image indicating the presence of uric acid. A base image indicates the presence of uric acid on a pixel-by-pixel basis. Pixel values of the base image are modified using the effective atomic number and the electron density values associated for the tissue associated with those pixels.

Provided is an object detection method and system capable of increasing a detection rate for hazardous substances such as liquid explosives as well as existing explosives, the object detection method including (a) preparing an X-ray image of a subject, (b) calculating effective atomic number values (Zeff) by using the X-ray image, and (c) segmenting a target object image from the X-ray image by using the effective atomic number values and energy-band-based multi-energy image reconstruction.

The application discloses a kind of geological sample component identification and abnormal detection system based on machine learning, it is related to geological sample component identification and detection technical field, first by the ratio of Compton and Rayleigh scattering intensity calculated by ray fluorescence energy spectrum, obtain effective atomic number and its uncertainty;Again, the continuum of short-wave infrared spectrum is removed, the depth of aluminum hydroxyl absorption band and band shape asymmetry are extracted, and the slope is used to represent the granularity, and the monotone spline is trained to obtain the clay proportion estimation;According to the end member set and atomic number table, the theoretical atomic number and uncertainty are obtained, and the measurement side is integrated into a unified scale to obtain the cross-modal inconsistency index.The state variable and the index are used as the target to adaptively optimize the collection and retest, and the consistency constraint is executed to solve the mixture and abnormality determination.The application can carry out high-precision identification and abnormality determination under complex working conditions.

The invention provides a Bi-based material for X-ray detection and protection and a preparation method and application thereof, and belongs to the technical field of X-ray detection and protection. According to the invention, the Bi-based material is BiO (IO3), BiVO4 or Bi2WO6; the preparation method comprises the following steps: mixing an I2O5 solution and a BiH10N3O14 suspension, and reacting to obtain BiO (IO3); the preparation method comprises the following steps: mixing Bi (NO3) 3.5 H2O, NH4VO3 and a nitric acid solution, and dropwise adding ammonia water into the mixed solution until yellow precipitate appears, so as to obtain BiVO4; the preparation method comprises the following steps: mixing a Na2WO4. 2H2O solution and a Bi (NO3). 5H2O suspension, and reacting to obtain Bi2WO6. The Bi-based material disclosed by the invention has excellent X-ray detection performance and protection effect, and due to the extremely high atomic number of the Bi element, the high equivalent atomic number and density of the material are finally formed, so that full absorption of X-rays is realized.

The present application provides a kind of material identification method, this method is based on the material decomposition of double effect, is calibrated with two or more materials, improves the accuracy of material decomposition, obtains more accurate atomic number estimation physical model by adaptive parameter determination method.The present application is divided into empirical double effect material decomposition and adaptive effective atomic number estimation two parts, wherein empirical double effect material decomposition, by the calibration of multiple standard phantoms, the determination of decomposition parameters and the estimation of electron density are realized by the polynomial combination of projection data and the constraint in image domain;Effective atomic number estimation establishes an adaptive effective atomic number estimation model by the calibration of known standard phantom, realizes the estimation of effective atomic number of imaging material.The present application can realize more accurate electron density and effective atomic number estimation for different kinds of materials at different energies through adaptive material decomposition algorithm, improves the precision of material identification.

This invention discloses a material identification method and system based on physical constraint deep learning. The steps of this invention include: constructing a simulation phantom and assigning it material information; acquiring projection data of the simulation phantom; generating high- and low-energy reconstructed images based on the projection data, using the effective atomic number image and density image of the simulation phantom as basic label images; generating reconstructed images at a set energy based on the projection data as single-energy label images; inputting the basic label images into an X-ray dynamic interaction model, outputting single-energy images to optimize the X-ray dynamic interaction model, obtaining an MLP-type X-ray dynamic interaction model; inputting the high- and low-energy reconstructed images into the main network model to obtain effective atomic number images and density images, and inputting them into the MLP-type X-ray dynamic interaction model to generate a single-energy image optimized main network model; inputting the reconstructed image of the object to be detected into the main network model to obtain its effective atomic number image and density image.

In the case of a liquid material contained in a container without having a shape, it is very difficult to accurately predict the type or physical properties of the liquid, so that there is a problem that the accuracy or detection rate is very low.SOLUTION: The present invention relates to a method and system for detecting an object capable of improving a detection rate of dangerous materials such as liquid explosives as well as existing explosives, and may include (a) preparing an X-ray transmission image of an object, (b) calculating an effective atomic number value (Zeff) using the X-ray transmission image, and (c) classifying a target article image from the X-ray transmission image using the effective atomic number value and reconstruction of a multi-energy image for each energy band.SELECTED DRAWING: Figure 1