42 results about "Ordered subsets" patented technology

The CT imaging system optimizes its image generation by adaptively weighting certain parameters during the iterations in an iterative reconstruction algorithm. The projection data is grouped into N subsets, and after each of the N subsets is processed by the ordered subsets simultaneous algebraic reconstruction technique (OSSART), the image undergoes total variation (TV) minimization process. During the iterative reconstruction algorithm, a combination of the parameters such as a total variation, a relaxation parameter and a step size parameter is assigned a respective value based upon the current value of the iteration.

Embodiments of the invention disclose an order processing method and apparatus. A specific embodiment of the method comprises the steps of obtaining order information of multiple to-be-delivered orders, wherein the order information comprises shipping address information and receiving address information; according to a source delivery point corresponding to a shipping address and a destination delivery point corresponding to a receiving address, dividing the to-be-delivered orders into multiple to-be-processed order subsets; based on a preset combination condition, dividing the to-be-processed order subsets into multiple to-be-combined order sets; and for each to-be-combined order set in the multiple to-be-combined order sets, executing the following order processing operations of determining whether the to-be-combined order set meets a preset delivery condition or not; and in response to the situation that the to-be-combined order set meets the preset delivery condition, combining the to-be-processed order subsets in the to-be-combined order set to generate a delivery order set. The order delivery is arranged according to the delivery order set generated by the method, so that relatively high delivery efficiency can be realized, the transport capacity is reduced, and the delivery cost is reduced.

The CT imaging system optimizes its image generation by frequently updating an image and adaptively minimizing the total variation in an iterative reconstruction algorithm using many or sparse views under both normal and interior reconstructions. The projection data is grouped into N subsets, and after each of the N subsets is processed by the ordered subsets simultaneous algebraic reconstruction technique (OSSART), the image volume is updated. During the OSSART, no coefficients is cached in the system matrix. This approach is intrinsically parallel and can be implemented with a GPU card. Due to the more frequent image update and the variable step value, an image quality has improved.

In the present invention, the fast high-resolution image reconstruction of PET projection data is realized by introducing the organic combination of super-laxation speeding factor and variable background constraint, the reconstruction speed and spatial resolution are superior to available ordered subset expectation maximum (OSEM) process, and the antijamming capacity is superior to traditional filtering back projection (FBP) process. The present invention is the improvement of OSEM, and said algorithm combines several kinds of data processing technology in the reconstruction of the heat source image to obtain reconstructed image with high spatial resolution and low noise.

The CT imaging system optimizes its image generation by adaptively changing parameters in an iterative reconstruction algorithm based upon certain information such as statistical information. The coefficients for the parameters include at least a first coefficient for a predetermined data fidelity process and a second coefficient for a predetermined regularization process in an iterative reconstruction algorithm. The iterative reconstruction algorithm includes the ordered subsets simultaneous algebraic reconstruction technique (OSSART) and the simultaneous algebraic reconstruction technique (SART). The first coefficient and the second coefficient are independently determined using some predetermined statistical information such as noise and or error in matching the real data.

The invention discloses an X-ray multi-energy spectrum computed tomography (CT) projection data processing and image reconstruction method, which mainly comprises an X-ray energy spectrum CT projection sinogram processing method and a compressed sensing-based accelerated iterative convergent reconstruction algorithm. The X-ray energy spectrum CT projection sinogram processing method mainly comprises the following steps of: (1) restraining vertical streaking artifacts in a projection sinogram; (2) removing high-brightness noisy points in the projection sinogram. The compressed sensing-based accelerated iterative convergent reconstruction algorithm refers to that image total variation (TV) minimization-based optimal constraint conditions and the ordered-subset simultaneous algebraic reconstruction techniques (OS-SART) are combined. As a number of defects still exist in a traditional X-ray energy spectrum CT detection system (X-ray energy resolution photon counting detector), more noise and artifacts exist in acquired projection data. According to the X-ray multi-energy spectrum CT projection data processing and image reconstruction method, X-ray multi-energy spectrum CT projection data are effectively preprocessed by utilizing a preprocessing means, and meanwhile, a TV-based OS-SART algorithm is introduced into X-ray multi-energy spectrum CT image reconstruction, and therefore, image iterative convergence is accelerated, and the noise and the artifacts in a reconstructed image is well restrained.

The present invention relates to image satellite planning, and more particularly to a method for allowing a deeper search for high value targets in a time-limited planning environment. In an exemplary embodiment, a method of computing an ordered subset of targets includes using an approximation for the time needed for the satellite to re-orient to a new target, rather than calculating each maneuver time between targets. By approximating the maneuver time rather than calculating it, the calculation time is reduced. Each iteration through the traveling salesman problem takes less time, and more iterations can be accomplished between imaging windows. The iterative process can search deeper into the traveling salesman problem to find a better solution.

Methods, systems, and non-transitory computer readable media for image reconstruction are presented. Measured data corresponding to a subject is received. A preliminary image update in a particular iteration is determined based on one or more image variables computed using at least a subset of the measured data in the particular iteration. Additionally, at least one momentum term is determined based on the one or more image variables computed in the particular iteration and / or one or more further image variables computed in one or more iterations preceding the particular iteration. Further, a subsequent image update is determined using the preliminary image update and the momentum term. The preliminary image update and / or the subsequent image update are iteratively computed for a plurality of iterations until one or more termination criteria are satisfied.

A framework for an iterative reconstruction algorithm is described which combines two or more of an ordered subset method, a preconditioner method, and a nested loop method. In one type of implementation a nested loop (NL) structure is employed where the inner loop sub-problems are solved using ordered subset (OS) methods. The inner loop may be solved using OS and a preconditioner method. In other implementations, the inner loop problems are created by augmented Lagrangian methods and then solved using OS method.

The invention discloses a large-scale logistics distribution method based on a cloud platform. According to the method, a logistics distribution circle is divided according to the number of the flow of people, clients or the like, a logistics center scans commodity information and uploads data, then commodities are made to match distribution circles, the commodities in the distribution circles aredivided into a plurality of order subsets through a k-means clustering algorithm, distribution routes of each distribution circle are calculated through a discrete particle swarm algorithm, the clients check distribution states via APPs or WEB ends, and the center monitors vehicle positions and states through a vehicle-mounted terminal.

A method transforms a generalized parity check matrix representing a linear block binary code. First, an input generalized parity check matrix is defined for the linear block binary code. Auxiliary sets are formed from the input generalized parity check matrix and organized into a partially ordered set. The subsets of each auxiliary set are ordered in a list, and parity check equations are constructed from the list of ordered subsets. The parity check equations are translated into an output generalized parity check matrix, which can be used to decode a message encoded according to the linear block binary code.

A method, computer program product, and a data processing system for determining an edge for inversion in a cyclic compound directed graph. A plurality of graph nodes of a graph are evaluated for generation of a first node order subset pair. A determination that the graph requires a recursive evaluation for completing node ordering of the plurality of graph nodes is made. The graph is divided into a plurality of graph partitions, and respective node order subset pairs are generated for the graph partitions.

The CT imaging system optimizes its image generation by updating an image with the current application of a data fidelity update and a regularization update together in a single step in an iterative reconstruction algorithm. The iterative reconstruction algorithm includes the ordered subsets simultaneous algebraic reconstruction technique (OSSART) and the simultaneous algebraic reconstruction technique (SART). The data fidelity update and the regularization update are independently obtained using some predetermined statistical information such as noise and or error in matching the real data.

The invention discloses a CT (Computed Tomography) iterative image reconstruction method, which can be applied to precise sectional image reconstruction in incomplete projection data situations such as low X-ray tube current, under-sampling or limited angle. The method comprises steps of projection-driven iterative calculation, adaptively-determined ordered subset number, adaptive weight, and distance-driven orthographic projection and back projection calculation. A set of all projection data processed by each time of iterative calculation at each projection angle is firstly divided into a plurality of ordered subsets and each subset undergoes the following steps of distance-driven orthographic projection, subtraction with real projection data and weighting, distance-driven back projection and to-be-reconstructed image correction. In the scanning modes of low X-ray tube current, under-sampling or limited angle, a reconstructed image which can fully meet imaging clinical diagnosis demands can be obtained, the method can be self-sufficiently applied to CT imaging aiming at reducing the X-ray radiation dose or can be used for front-end processing of other CT image reconstruction algorithm.

The invention discloses a SPECT (single-photon emission computed tomography) imaging method based on an ordered subset algorithm. The SPECT imaging method includes that a SPECT detector and a collimator are arranged in an equilateral polygon arranged on the periphery of a through hole of a rotating frame, the number of edges of the polygon corresponds to the number of angles in one subset, the rotating frame is rotated to have data collected, each time of data collection is accompanied with OS-EM iteration once, and after multiple iterations, images meeting requirements of image resolution are reestablished through a computer. The SPECT detector and the collimator are in polygon arrangement matched with the ordered subset algorithm, the advantages of the algorithm can be effectively exerted by means of hardware grouping, collecting full 360 angles is not needed, unnecessary photon counting is effectively reduced, and collecting time is shortened; besides, quantity demanded on the detectors and the collimators is decreased by adopting the rotating frame, and economical efficiency is high.

The CT imaging system optimizes its image generation by updating an image with the current application of a data fidelity update and a regularization update together in a single step in an iterative reconstruction algorithm. The iterative reconstruction algorithm includes the ordered subsets simultaneous algebraic reconstruction technique (OS SART) and the simultaneous algebraic reconstruction technique (SART). The data fidelity update and the regularization update are independently obtained using some predetermined statistical information such as noise and or error in matching the real data.

The embodiment of the invention provides a logistics processing method and device, electronic equipment and a storage medium, and aims to reduce the occupation of logistics resources. The method comprises the following steps: collecting orders received within a set time, and determining a corresponding order set; querying a target address of an order in order data of the order in the order set; converting the target address into longitude and latitude information of the order; clustering the orders in the order set according to the longitude and latitude information of the orders; and determining an order subset according to the clustering result, and distributing logistics tasks corresponding to the order subset. The order subsets can be obtained based on clustering, the logistics tasks corresponding to the order subsets are distributed, unified allocation and reasonable logistics resources can be achieved based on global orders, and the processing efficiency can be improved.

The invention discloses a nondestructive testing system and method for positioning a chamber inner wall defect through a positron probe. The testing system comprises a hardware part and a software part. The hardware part comprises a base; a to-be-tested part front end support and a to-be-tested part rear end support which are located on the base and an air floatation guide rail module with a grating scale; a to-be-tested part; an annular detector support which is installed on the air floatation guide rail module with the grating scale; an annular detector array which is installed in the annular detector support; a positron probe source lead chamber which is located on the base; a positron probe liquid circulating input pipe and a positron probe liquid circulating output pipe which are connected between the positron probe source lead chamber and the to-be-tested part. The software part comprises a counting module of detectors for recording the number of gamma photons produced by positron annihilation; a maximum likelihood estimation mathematical model module for gamma photon equalization and compensation; a model solution module based on an ordered subset expectation maximum; an image enhancement module based on maximum interclass variance and a 3D image synthesis module.

Propulsion of an unmanned vehicle may include determining and ordering a subset of altitude-differentiated wind vectors, the subset facilitating directional air flow from a starting geographic region to a destination geographic region, and configuring the vehicle and adjusting the altitude of the vehicle to the altitude corresponding to each of the subset of wind vectors as ordered based on a flight plan that includes at least one of a duration and distance for each of the ordered subset of the wind vectors.

A framework for an iterative reconstruction algorithm is described which combines two or more of an ordered subset method, a preconditioner method, and a nested loop method. In one type of implementation a nested loop (NL) structure is employed where the inner loop sub-problems are solved using ordered subset (OS) methods. The inner loop may be solved using OS and a preconditioner method. In other implementations, the inner loop problems are created by augmented Lagrangian methods and then solved using OS method.

A method and system is provided for performing medical imaging. The method and system includes at least one radiation detector to detect radiation from a subject, and an image processor which determines attenuation paths for an image point, groups substantially similar attenuation path lengths for the same image point to form a modified subset group, and processing image data using the modified subset group in order to provide a reconstructed image substantially similar to an original image.

An apparatus for and method of utilizing an Internet terminal coupled to the world wide web to interface with an existing proprietary data base management system by building a component which accesses said proprietary data base management system. A central process provides a listing of tables or steps to a use at the Internet terminal. The user selects an ordered subset of the list which defines the desired component. The component design is stored as this ordered subset. The design may be subsequently converted to script, as convenient, for run-time execution.

The invention discloses a high-definition PET image reconstruction method. The method comprises the following steps: marking a fixed point position in a local space in a visual field according to structural symmetry of a PET detection system; placing a point source for fixed-point sampling; reconstructing the list-mode data of the point source by adopting a three-dimensional ordered subset expectation maximum value method; fitting and solving an image space fixed-point voxel point diffusion function value by using a Gaussian function, and constructing system response function estimation of each point in the whole space; estimating a corrected image reconstruction model according to a system response function; constructing a Metz filtering function, and correcting an image reconstruction model in an image space; and inputting actual measurement data of a scanned object into the corrected image reconstruction model to obtain a high-definition PET image. Accurate system modeling is carried out on the list-mode data based on the point spread function, the experiment duration and the data storage space required by system response function modeling are reduced, the calculation complexity is reduced, filtering processing is carried out by using the Mitz filtering function, and the signal-to-noise ratio and the contrast ratio of the image are improved.

A method, system and unit for controlling the configuration of a transmission path coupling a first unit and a second unit is disclosed. The first unit and the second unit are configurable to support the transfer of information items over the transmission path in time-slots of successive transmission frames in accordance with a plurality of modes, each of the plurality of modes allocating differing numbers of time-slots within each frame to different types of information items. The method comprises the steps of: programming control logic of the first unit and the second unit with an indication of an ordered subset of the plurality of modes to be supported by the first unit and the second unit; configuring the first unit and the second unit to support the transfer the information items over the transmission path using a default one of the plurality of modes; and responsive to an indication requesting that a different mode be selected, causing the first unit and the second unit to be configured to support the next in the ordered subset of the plurality of modes indicated by the control logic. By providing within each unit a predetermined ordered list of modes to be supported by those units, it is only necessary to provide an indication that some change is required and the mode which needs to be changed to can be determined simply by referring to the ordered list. It will be appreciated that this provides a mechanism to switch efficiently between modes, significantly reduces the amount of data required to indicate the new mode and may improve the efficiency of total data transaction.

The invention discloses a human body action recognition method based on muscle signals. The method comprises the steps of obtaining depth image data corresponding to the muscle signals on the basis ofdepth signal flow, and distinguishing action information of muscle junction points in a human body in a three-dimensional space; and optimizing muscle junction signals of the human body on the basisof the action information of muscle junctions, filtering the muscle junction signals or redundant muscle junction signals which do not influence or slightly influence human body behavior recognition,building a human body behavior conditional random field model, obtaining a human body behavior recognition model, and predicting subsequent actions of the human body based on the human body behavior recognition model. According to the method, an improved sheep-flock optimization algorithm is used for performing selection. Compared with an unimproved method, the improved sheep-flock optimization algorithm has the advantages that a population is initialized by using a point set method, so that the algorithm is prevented from falling into local optimum; the convergence speed of the algorithm is increased by using an ordered subset method and introducing a sheep-flock wait-and-see method; and meanwhile, the recognition effect of video human body behaviors is improved.

Calibration of industrial CT system inspector by group series winding relates to re-established images. It carries out to process projection data generated by inspector group series winding model based on Ordered Subsets Convex, to make medium filtration after each iteration in order to speed up convergence. This invention needs less times of convergence and has quality calibration.

The invention is suitable for the field of clustering algorithms, and provides an order dispatching method based on a KM algorithm, an order dispatching device based on the KM algorithm, computer equipment and a medium. The order dispatching method based on the KM algorithm comprises the following steps: classifying an unprocessed order set to obtain K order subsets, wherein the K is an integer greater than 0; determining target salesman accounts corresponding to the K order subsets by using a KM algorithm according to the distances between the K order subsets and the K salesman accounts; and performing distribution operation on the K order subsets according to the target salesman accounts corresponding to the K order subsets. By applying the technical scheme provided by the embodiment of the invention, the problem that the rationalization degree of order distribution is not high due to the fact that the process of the existing order delivery mode is relatively tedious is solved. Therefore, the order distribution mode is more reasonable and effective, and the order processing efficiency of the whole salesman is improved.