21 results about "Diffusion imaging" patented technology

The disclosure relates to techniques for diffusion imaging of an examination region of a patient using a magnetic resonance facility. The technique may include specifying a number, which is at least two, of diffusion gradient pulse sequences for recording diffusion data sets using the magnetic resonance facility such that the diffusion gradient pulse sequences have a b-matrix that describes a planar diffusion encoding. The matrix may have precisely two intrinsic values that differ from zero. The technique may further include recording the diffusion data sets with the specified diffusion gradient sequences, and acquiring a trace-weighted image data set by geometric averaging of the at least two diffusion data sets. The diffusion gradient pulse sequences are determined such that the sum of all the b-matrices results in the unit matrix multiplied by a factor that characterizes the diffusion weighting (e.g. a predetermined b-value) and the number divided by three.

The present invention provides a method and apparatus for generating an echo time-dependent magnetic resonance diffusion imaging signal, which includes the steps of acquiring a magnetic resonance non-diffusion-weighted image and a magnetic resonance multi-b-value diffusion-weighted image, calculating a magnetic resonance diffusion signal without T2 value weighting corresponding to various water components in the human tissue for each voxel using the magnetic resonance non-diffusion-weighted image and quantitative T2 values ​​of various water components in the human tissue, calculating a diffusion coefficient corresponding to various water components in the human tissue for each voxel using the magnetic resonance multi-b-value diffusion-weighted image, the magnetic resonance diffusion signal, and the quantitative T2 values, and calculating a magnetic resonance diffusion imaging signal acquired using an arbitrary echo time for each voxel using the magnetic resonance multi-b-value diffusion-weighted image, the magnetic resonance diffusion signal, the quantitative T2 values, and the diffusion coefficients.

Systems and methods are provided for producing diffusion-weighted images of a subject using a magnetic resonance imaging (MRI) system. The method includes performing a stimulated echo preparation module using non-selective radio-frequency (RF) pulses designed to induce a stimulated echo, performing an acquisition module that includes a multi-slice acquisition of MR data in the presence of diffusion gradients, and reconstructing the diffusion-weighted images of the subject from the MR data.

Disclosed in the present invention are a 3D navigator-based 3D gradient spin-echo diffusion imaging method, and a device. The method comprises: first, using a global saturation module to destroy previous residual transverse magnetization; second, by means of a diffusion encoding module, embedding a pair of trapezoidal cosine oscillation gradients or pulse gradients into radio frequency pulses of 90°x-180°y-90°-x, and separating diffusion encoding from signal acquisition; then using a fat saturation module to suppress a fat signal; using a 3D gradient spin-echo readout method to acquire an image signal; and finally, acquiring a 3D navigation echo signal in the last spin echo for correcting a phase error between multiple excitations. The present invention can effectively eliminate phase errors between multiple acquisitions and realize high-resolution whole-brain diffusion imaging. Compared with 2D echo planar imaging, the present invention increases the signal-to-noise ratio of diffusion magnetic resonance imaging, thereby supporting higher-resolution whole-brain 3D diffusion magnetic resonance imaging.

The present invention relates to a low-field magnetic resonance diffusion imaging reconstruction method, comprising the steps of: acquiring low-field magnetic resonance K-space data; mapping the acquired K-space data to a standard Cartesian coordinate system using a Gaussian kernel grid interpolation method, weighting each radial sampling point using a Gaussian kernel function to ensure that the data is smoothly distributed on a standard grid; utilizing the interpolated K-space data to reconstruct the image based on image prior constraints, wherein the image prior constraints include: the image's low-rank structure and sparse prior information; and gradually optimizing the parameters of the low-rank and sparse components using a hybrid kernel function, and combining these constraints with the image's low-rank structure and sparse prior information to enhance the image reconstruction effect. The present invention also relates to a low-field magnetic resonance diffusion imaging reconstruction system. The present invention can significantly improve image quality and noise immunity.

The present invention discloses a method and device for infrared diffusion imaging of small targets based on a point source blackbody. First, based on the principle of small target infrared imaging diffusion, a small target diffusion imaging model is constructed to obtain a theoretical diffusion area that varies with distance. The constructed small target diffusion imaging model is calibrated based on dynamic test performance to obtain a small target diffusion calibration area. The small target diffusion calibration area is then compared with the actual diffusion area of ​​the small target measured to verify whether the derived diffusion calibration area conforms to the small target imaging diffusion size. This method and device overcome the problem in the prior art that the target imaging principle is not suitable for small target imaging ambiguity. It can effectively improve the accurate measurement of small target imaging size and provide a testing basis for target recognition in future infrared imaging systems.

The application discloses a physical basis model multi-position high-definition diffusion magnetic resonance imaging method, and relates to a multi-shot planar echo diffusion magnetic resonance imaging method. The method comprises the following steps: 1) acquiring k-space data of a diffusion magnetic resonance imaging to be reconstructed, which is collected by a multi-shot planar echo sequence; 2) designing a bidirectional one-dimensional low-rank reconstruction model based on a local smooth phase; 3) generating data by using a physical diffusion model; 4) training a basis model neural network by using the physical generated data; 5) solving the bidirectional one-dimensional low-rank reconstruction model; and 6) combining reconstruction parameters prompted by the basis model neural network, and generalizing reconstruction to obtain multiple-position high-definition diffusion magnetic resonance images without motion artifacts. The method realizes robust high-definition magnetic resonance diffusion imaging of multiple challenging positions without adjusting parameters by using a bidirectional one-dimensional low-rank optimization reconstruction method and a neural network to adaptively optimize reconstruction parameters.

The invention relates to the technical field of magnetic resonance imaging, in particular to a two-dimensional magnetic resonance diffusion imaging method and device for inhibiting metal artifacts, and the method comprises the steps: rearranging three-dimensional k-space data corresponding to a magnetic resonance signal based on a frequency band separation algorithm of echo migration, carrying out the conversion to obtain a two-dimensional image arranged in a target direction, separating frequency band components, and obtaining a two-dimensional image; therefore, a frequency band graph capable of reflecting the partial resonance frequency space distribution caused by the metal is generated; and on the basis, inter-layer and intra-layer signal offset is further corrected, and finally a two-dimensional magnetic resonance diffusion imaging image after correction of the metal artifacts is obtained. According to the invention, the metal artifacts can be reduced and inhibited while the scanning time, the imaging resolution, the diffusion weighting intensity and the scanning coverage range are ensured and maintained, so that rapid and high-fidelity magnetic resonance diffusion imaging is realized in the presence of a metal implant.

The invention relates to the technical field of environmental monitoring, in particular to a gas diffusion imaging processing method considering path confidence correction, which comprises the following steps of: calculating link input, establishing a relationship between observation and concentration by adopting a nonlinear diffusion imaging model, and improving inversion stability by adopting path length estimation and confidence correction. A reliable concentration field is obtained through energy functional optimization; based on the concentration sequence and the image sequence, combining brightness consistency and conservation error to estimate a wind speed field, and then using physical stepping and learning residual errors to obtain an evolution operator with physical and data constraints to predict the concentration of a plurality of steps in the future; a prediction module is embedded into system dynamics, the influence of equipment actions on the concentration is described, a risk measurement design and an optimization control strategy are configured, and a key area is controlled to keep the safe concentration under various disturbances. According to the scheme, the problems of unstable imaging error and diffusion prediction, uncontrollable intervention and the like in the traditional technology are solved.

The present disclosure relates to a magnetic resonance diffusion imaging fat suppression method based on echo navigation water-fat separation, comprising: acquiring echo and / or navigation echo corresponding to multiple chemical shift encoding points under the condition that the b value is equal to 0 and the condition that the b value is greater than 0; solving the water map, the fat map, the main magnetic field inhomogeneity and the relaxation time of the target object under the condition that the b value is equal to 0, and determining the water map and the fat map of the target object under the condition that the b value is greater than 0. Through the present disclosure, a chemical shift encoding fat suppression method suitable for clinical use is provided for magnetic resonance EPI diffusion imaging, the interference of fat chemical shift artifacts is reduced, the accuracy of image contrast and quantitative parameter measurement is improved, the universality of chemical shift encoding EPI DWI is improved, and the image reconstruction speed and robustness are accelerated.

Systems and methods are provided for producing diffusion-weighted images of a subject using a magnetic resonance imaging (MRI) system. The method includes performing a stimulated echo preparation module using non-selective radio-frequency (RF) pulses designed to induce a stimulated echo, performing an acquisition module that includes a multi-slice acquisition of MR data in the presence of diffusion gradients, and reconstructing the diffusion-weighted images of the subject from the MR data.

A computer-implemented method for adjustable three-dimensional (3D) rendering of a location in a subject includes the step of receiving image data for white matter tracts in a subject with discrete spatial resolution, wherein the image data is formed by diffusion imaging. A first stereoscopic image is rendered on a display from the image data. The first stereoscopic image includes image parallax that allows for three-dimensional viewing. Input signals from a pointing device or gestures that allow manipulation of the first stereoscopic image while maintaining correct three-dimensional spatial relationships are received. The pointing device interacts with the first stereoscopic image in three-dimensional space with six degrees of freedom.

A computer-implemented method includes: generating a set of simulated diffusion signals based on a corresponding set of diffusion sampling parameters and a corresponding set of micro structural model parameters, wherein the diffusion sample parameters correspond to magnetic resonance (MR) parameters, and wherein the microstructural model parameters characterizing a microscopic diffusion with a spatial orientation; processing the set of simulated diffusion signals based on the corresponding set of diffusion sampling parameters to generate a first set of output metrics, wherein the first set of output metrics are associated with the spatial orientation; consolidating multiple sets of output metrics into a dictionary, wherein each set of the multiple sets of output metrics are generated by processing a corresponding set of simulated diffusion signals and associated with a corresponding spatial orientation; applying the dictionary to a set of acquired diffusion MR signals; and generating at least one macroscopic diffusion feature that is orientation invariant.

Provided herein is technology relating to medical imaging and particularly, but not exclusively, to devices, methods, systems, and kits related to a quantitative diffusion imaging phantom.

The application discloses a 3D gradient spin echo diffusion imaging method and device based on a 3D navigator. The method first destroys previous residual transverse magnetization in a global saturation module; secondly, a pair of trapezoidal cosine oscillation gradients or pulse gradients are embedded into 90° x -180° y -90° ‑x RF pulses to separate diffusion encoding from signal acquisition; then, a fat saturation module is used to suppress fat signals; then, a 3D gradient spin echo readout mode is used to acquire image signals; finally, 3D navigator echo signals are acquired in the last spin echo to correct phase errors between multiple excitations. The application can effectively eliminate phase errors between multi-section acquisitions and realize high-resolution diffusion imaging of the whole brain. Compared with 2D planar echo imaging, the application improves the signal-to-noise ratio of diffusion magnetic resonance imaging, thereby supporting higher-resolution whole brain 3D diffusion magnetic resonance imaging.

The invention discloses a thermal diffusion imaging method based on an equivalent transport structure, and the method is characterized in that an equivalent velocity field is a gradient field (nearly no rotation) in a passive cooling section and under the conditions of near isotropy and near constant, and the divergence of the equivalent velocity field presents an internal and external different sign source-sink ring structure at the boundary of an internal non-uniform / abnormal region. In addition, the first symbol overturning moment of the divergence is utilized, the ring mid-value characteristic moment is obtained through ring domain mid-value or robust statistics, and the depth-time scale is established to complete the depth estimation of the abnormal region. The method has the following beneficial effects: 1) the equivalent transport structure is strictly identical transformation, and artificial model approximation is avoided; (2) the robustness to radiation gain drift and illumination unevenness is improved; the method comprises the steps of (1) providing a constant residual error and a non-negative inequality residual error, (2) weakening the dependence on a threshold value and empirical characteristics, (3) providing physically verifiable quality control and explicitly alarming convection / anisotropy / non-Fourier effect for a conservation residual error, a non-negative inequality residual error and a rotation degree, and (5) linearly calibrating a depth-time scale in the same material and performing quantitative estimation, and is suitable for complex components such as aerospace composite materials and the like.

The invention relates to a fat pressing method and device for magnetic resonance diffusion imaging based on water-fat separation, and the method comprises the steps: adjusting the bandwidth corresponding to a target excitation pulse and a target refocusing pulse, so as to change the position of a main peak fat layer excited and refocused in initial magnetic resonance imaging, and inhibit the main peak fat; moving a preset signal reading window to collect diffusion and non-diffusion imaging signals of single excitation or multiple excitation corresponding to the signal reading window at different moving time; constructing a corresponding diffusion imaging signal model, simplifying the diffusion imaging signal model, solving the non-diffusion imaging signal model through a water-fat separation algorithm to obtain a water graph, a fat graph, a magnetic field non-uniformity graph and a relaxation time graph corresponding to the non-diffusion imaging signal model, and calculating a coil sensitivity graph corresponding to the non-diffusion imaging signal model; and solving a diffusion imaging signal model to obtain a diffusion-weighted water map, a fat map and a phase difference between excitation so as to finish inhibition of olefin peak fat.

The application relates to a temperature gradient measuring method based on a schlieren and a neural network and an air supply and exhaust regulation system, and relates to the technical field of air flow temperature field detection. The regulation method comprises the following steps: collecting background schlieren images and air flow diffusion schlieren images of a heat source monitoring area through a first background schlieren temperature measuring platform and a second schlieren diffusion imaging platform; extracting light deflection displacement features of the background schlieren images, inputting the trained BP neural network model, and obtaining grid temperature field and temperature gradient distribution features; extracting position coordinates of a transient air flow diffusion front end of the air flow diffusion schlieren images and mapping the position coordinates to the grid temperature field to determine the temperature gradient and the corresponding temperature feature value mean value of the grid; comparing the mean value with a preset early warning threshold value, generating air supply and exhaust regulation and alarm instructions in combination with the number of on-site operators, controlling the linkage air supply and exhaust of the same layer ventilation gate, the bottom air supply unit and the top exhaust unit, and stopping the operation when the mean value is lower than a stop threshold value.

The invention relates to a self-navigation multi-section simultaneous excitation three-dimensional diffusion imaging method and device, and the method comprises the steps: carrying out the multi-section simultaneous excitation of each to-be-imaged region of a target object according to a target sampling rule, and carrying out the signal collection, and obtaining the four-dimensional k space data of each to-be-imaged region; based on the four-dimensional k space data, performing reconstruction along the inter-segment coding dimension, and performing one-dimensional inverse Fourier transform along the inter-segment coding dimension to obtain four-dimensional mixed space data corresponding to the to-be-imaged areas; calculating kz center data of each excitation based on each four-dimensional mixed space data to obtain self-navigation phase information for each excitation; and utilizing the self-navigation phase information of each excitation to perform joint image reconstruction of all excitation on the four-dimensional mixed space data of the to-be-imaged area, and obtaining an imaging result of each to-be-imaged area of the target object after channel merging. Extra navigation echoes do not need to be collected, and the collection time of multi-section simultaneous excitation imaging is shortened.

The present application relates to the technical field of environmental monitoring, and more particularly to a gas diffusion imaging processing method considering path confidence correction, comprising: calculating link input, establishing the relationship between observation and concentration by using a nonlinear diffusion imaging model, improving inversion stability by using path length estimation and confidence correction, and obtaining reliable concentration field through energy functional optimization; based on concentration sequence and image sequence, combining brightness consistency and conservation error to estimate wind speed field, and then using physical stepping and learning residual to obtain evolution operator prediction of future concentration with physical and data constraints; embedding the prediction module into system dynamics to describe the influence of device action on concentration, configuring risk measurement design and optimization control strategy, and controlling the key area to maintain safe concentration under various disturbances. The present application solves the problems of imaging error, unstable diffusion prediction and uncontrollable intervention in the prior art.