162results about How to "Reduce rebuild time" patented technology

An apparatus for wide-field functional imaging (WIFI) of tissue includes a spatially modulated reflectance/fluorescence imaging (SI) device capable of quantitative subsurface imaging across spatial scales, and a laser speckle imaging (LSI) device capable of quantitative subsurface imaging across spatial scales using integrated with the (SI) device. The SI device and LSI device are capable of independently providing quantitative measurement of tissue functional status.

The invention discloses a single-image super-resolution reconstruction method based on a symmetric depth network and belongs to the image processing technology field. The method mainly comprises the following steps of 1, making a high resolution image block and low resolution image block training set; 2, constructing a symmetric convolution-deconvolution depth network used for model training; 3, based on the constructed depth network and the made data set, carrying out network model training; and 4, based on a learned model parameter, inputting one low resolution image, wherein acquired output is a reconstructed high resolution image. In the invention, a convolution layer and a deconvolution layer are combined and simultaneously a network depth is increased; the network depth is used to increase network performance; a reconstruction capability of an image detail portion is enhanced and a good image super-resolution reconstruction effect is acquired. The method has a wide application prospect in fields of image high definition displaying, medical imaging, a remote sensing image and the like.

A process for extracting hemopoietic stem cells from the placenta of healthy puerperant to create hemopoistic stem cell bank features that the mechanical, enzyme digestive and monoclonal antibody-magnetic bead method is used to extract blood stem cells from placenta, and concentrate and purify them. Its advantages are high activity and high concentration.

A data storage system is disclosed that utilizes garbage collection and hybrid self-mapping for managing system data. In one embodiment, a system data region having an amount of valid system data that is below a threshold is freed. Write amplification associated with managing and storing system data can be reduced at least in part because only valid system data can be copied during garbage collection of the selected region. Mapping information associating system data with physical locations in non-volatile storage where system data is stored can be generated, which can reduce system data reconstruction time during start-up of the data storage system. Increase in efficiency and reduction in startup and initialization time can be attained.

The present invention is a system for reducing rebuild time in a RAID (Redundant Array of Independent Disks) configuration. The system includes a plurality of RAID disk drives, a plurality of hot spare disk drives, and a controller communicatively coupled to the plurality of RAID disk drives and the plurality of hot spare disk drives. The system functions so that rebuild data is striped by the controller across at least two hot spare disk drives included in the plurality of hot spare disk drives.

A system and method for rebuilding a storage drive utilizes a rebuild management module within a RAID controller to conduct a substantially sequential rebuild operation on a rebuild disk. When the rebuild management module receives host I/O requests during a rebuild operation, these requests are facilitated using other disks. After the substantially sequential rebuild is complete, the rebuild management module updates the rebuild disk based upon the host I/O requests received during the sequential rebuild operation.

The invention provides a computer tomography (CT) parallel reconstructing system and an imaging method thereof. The CT parallel reconstructing system comprises a front end sampler, a central node and a plurality of sub-nodes connected with the central node, wherein each sub-node is provided with an image processor. The imaging method of the CT parallel reconstructing system comprises the following steps: (1) a reconstructed image is divided into a plurality of block regions; (2) original projection data of each angle, which is sampled by the front end sampler, is received by the central node; (3) calculation tasks are allotted to the sub-nodes by the central node, and the reconstruction values of one or a plurality of block regions are calculated by the sub-nodes to which the reconstruction calculation tasks are allotted; (4) the reconstruction calculation tasks are completed by the sub-nodes; and (5) a complete reconstruction image is combined by the central node. By adopting the method of dividing the reconstructed image into subregions, the invention fully excavates the parallel characteristics of CT scan and reconstruction, and performs parallel reconstruction of GPU while data collection, and the reconstruction time is lowered from the minute grade of the prior art to millisecond grade, thereby accurately displaying the reconstruction of the sectional image of a detected object in real time.

One aspect of the invention is a method for providing protection from data loss on a storage device. An example of the method includes designating a number n, and identifying a plurality of groups of strips on the storage device, wherein a number of strips in a plurality of the groups is equal to the number n. This example also includes reserving a respective parity protection strip on the storage device, for a plurality of the groups of strips identified on the storage device. This example further includes calculating a parity value for a plurality of parity protection strips on the storage device, wherein each calculated parity value is a function of information stored in each of the strips in the corresponding group of strips. This example additionally includes storing the calculated parity protection strip parity values, in corresponding parity protection strips.

An electrical parameter imaging apparatus and method includes the acquisition of a charge distribution pattern on an array of electrodes that surround an object being imaged. In addition the boundaries of regions having differing electrical characteristics within the object are measured by a secondary imaging method. The internal boundary location measurements are employed to provide a quicker method to compute electrical parameters of tissues inside the boundary using the acquired charge distribution pattern.

The invention is a method for emission tomographic reconstruction from measurement data, the method comprising the steps of: obtaining the measurement data corresponding to activity of particle emissions of a volume from a tomographic imaging system, and performing at least one iteration step to obtain emission density data of particle emissions of the volume from the measurement data, wherein the at least one iteration step comprises a forward projection being a projection from the emission density data to the measurement data, and a back projection being a projection from the measurement data to the emission density data, and the at least one iteration step is carried out in a parallel hardware architecture, wherein both the forward projection and the back projection are of at least partially gathering type, and the measurement data is of binned type. The invention also relates to computer readable medium and a system for processing tomographic reconstruction from measurement data.

In a method and magnetic resonance imaging apparatus wherein magnetic resonance signals are simultaneously received from an examination subject by multiple reception coils, a single, uninterrupted pulse sequence is executed which includes reference scans of the subject with a first sequence kernel that is optimized for coil sensitivity calibration, immediately followed by a series of accelerated image scans with a second sequence kernel, different from the first sequence kernel, that is optimized for imaging. Coil sensitivity maps for the respective coils are calculated from the data acquired in the reference scans, and an image of the subject is reconstructed by operating on the image data with a parallel reconstruction algorithm employing the calculated coil sensitivity maps.

A plurality of coil elements (18, 18') and corresponding receivers (26) define a plurality of channels, each carrying a corresponding partial k-space data set (60, 64). One or more processors (30) generate (80) a first image representation (76) based on the plurality of partial k-space data sets, generate a relative sensitivity map (82) for each of the channels, project (90) the first image representation (76) with each of the relative sensitivity maps (82) to generate a plurality of recreated k-space data sets (92), and each partial k-space data and the corresponding recreated k-space data set are combined to generate substituted k-space data sets (96). The substituted k-space data sets are reconstructed (100) into a plurality of images (102) which are combined (104) to create a final image (106).

he invention provides a magnetic-resonance fast imaging method and system based on iterative feature correction. The magnetic-resonance fast imaging method based on iterative feature correction comprises: reconstructing undersampled data obtained from K space to obtain an initial reconstruction image; executing sparse constraint-based denoising processing on the initial reconstruction image to obtain a noise pattern; executing feature correction on the noise pattern to obtain a correction image containing detail features; and optimizing the correction image by means of Tikhonov regular method to obtain a final reconstruction image. According to the invention, through executing the feature correction on the initial reconstruction image, the correction image containing detail features is obtained, and then through optimizing the correction image, the final reconstruction image is obtained. The method adopting the invention is easier to obtain the detail features, solves effectively the problem that the detail features of the construction image are easy to lose, and effectively improves quality of the reconstruction image and shortens reconstruction time through a detail feature correction technology.

The invention relates to a fast light super-resolution reconstruction dense residual error network. Based on a dual-channel deep residual error network (FLSR) of a convolutional network, a deep channel is mainly used for learning high-frequency texture information of an image, and a shallow channel is used for learning low-frequency information of of the image. In order to accelerate the convergence speed of the network, a residual error connection mode is added in the structure, in addition, the image detail information of the front convolution layer can be directly transmitted to the rear convolution layer through the residual error connection mode, and reconstruction of images with better quality is facilitated; and a dense connection mode which contributes to weakening gradient disappearance and improving the model performance is also adopted in the structure. While image quality evaluation indexes such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and information fidelity (IFC) standard measurement are improved, parameters and computational complexity of the model are reduced, the reconstruction speed of the image is increased, and the method can be applied to actual life.

The invention discloses a rotating plate double-panel PET system based on polygon view symmetry. The system can obtain multi-angle projection data and solve the problem that a panel PET system can not obtain complete angle projection data. The invention further discloses an imaging method based on the system. The method comprises the following steps that a data collecting module is used for obtaining the complete angle projection data; a view, a coding crystal and a response line are divided, and required voxel and corresponding line codes are determined and calculated; a polygon view system response matrix is calculated, and the system response matrix and the projection data are utilized for carrying out iteration reconstruction; the polyhedral voxel is converted into the cube voxel, and image display is carried out. According to the method, the symmetry of the polygon view structure is utilized, limitation on the symmetry of the rotating system from the square view structure is broken, the calculation amount of the system matrix is reduced, and the reconstruction efficiency of the PET system is improved; by adjusting the size of sectors in the view, the method can be widely applied to the rotating panel PET system.

The invention discloses a method for medical ultrasound three-dimensional imaging based on parallel computers. The method includes the following steps: S1. creating a task pool, namely, reading image data in accordance with user setting parameters and construct the task pool; S2. conduct partitioning to obtain sub-tasks, namely, adopting domain partition method to partition collected ultrasound image sequences into a plurality of sub-tasks in accordance with defined particle size, wherein each sub-task is an interpolation to complete adjacent images of S frames; S3. sub-tasks distribution, namely, distributing sub-tasks to different processors; S4. calculating sub-tasks, namely each processor executing sub-tasks coordinately and parallelly; S5. overall interpolation, namely conducting overall interpolation to sub-task results obtained by reconstruction of each processor to get a three-dimensional image; and S6. image display, namely displaying the reconstructed three-dimensional image. The method solves the time-consuming problem of the ultrasound three-dimensional imaging. Besides, the parallel computer environment is constructed by low-cost computers. Thus, the computing resources can be fully used and the cost is also low.

The invention discloses a high-quality imaging method of a spectral imaging system based on a convolutional neural network, and belongs to the field of computational photography. According to the invention, the hyperspectral image imaging process and the reconstruction process are considered together; in the reconstruction process, the spatial correlation and the spectral correlation between images are respectively considered, the residual error learning is used to accelerate the training speed and convergence speed of the network, the coding network is optimized while the network is reconstructed, and a GPU is used to complete the optimization solution of the whole network. A cuDNN library is used to accelerate the operation speed of the network. The method comprises updating network parameters by using a random gradient descent method; and performing block-by-block processing to complete reconstruction of the hyperspectral image. According to the method, the hyperspectral image reconstruction of the CASI spectral imaging system can be completed with high quality, the high spatial resolution and high spectral fidelity of a reconstruction result are ensured, meanwhile, the efficiency of hyperspectral image reconstruction is improved, and the application range of hyperspectral images is expanded. The method can be used in the fields of manned spaceflight, geological survey, agricultural production, biomedicine and the like.