44 results about "Joint reconstruction" patented technology

A method, system, and software are provided for joint reconstruction of three-dimensional images using multiple imaging modalities. In an exemplary embodiment, the present approach includes providing a first dataset acquired via a first imaging technique or a first image generated from the first dataset, providing a second dataset acquired via a second imaging technique or a second image generated from the second dataset, and generating a volumetric dataset by extracting information from the first and second datasets or images. The first imaging technique may have better resolution than the second imaging technique in a first direction, and the second imaging technique may have better resolution than the first imaging technique in a second direction. There is provided a system and one or more tangible, machine readable media for performing the act of generating the volumetric dataset by extracting information from the first and second datasets or images.

The invention relates to a method for simultaneously acquiring a plurality of voice signals by using a DCS (distributed compressed sensing) technology. According to the method, the requirements of low hardware cost and high transmission rate of a plurality of sensor acquisition nodes in the agricultural Internet of Things can be met, the power consumption of a plurality of sensors in a communication process is reduced, and the sensors cooperatively work to prevent damage to the whole network structure due to the fact that the energy of certain nodes is excessively fast consumed. A JSM-1-model-based joint sparsity model for the voice signals is established by using a DCS theory, a plurality of pieces of sensing data are simultaneously compressed at a coding end, and the signals are precisely recovered by using a DCS-theory-based joint reconstruction algorithm at a decoding end to implement a complete signal acquisition process.

The invention provides a satellite hyper-spectral image compressed sensing reconstruction method based on image sparse regularization. The method comprises the following steps: Step 1, the three-dimensional cube of known hyper-spectral data is rearranged into a matrix; Step 2, a multi-vector measurement model is constructed with a stochastic convolution transform as a linear observation matrix, and each waveband is independently sampled to generate a measurement vector matrix; Step 3, a hyper-spectral image is decomposed in a sparse transform domain into a spectral association component and a difference component, and an image sparse regularization joint reconstruction model including the association component and the difference component is constructed; and Step 4, an alternating-direction multiplier iteration algorithm for solving the joint reconstruction model is put forward, the association component and the difference component of a transform domain are obtained, and then the association component and the difference component are merged to obtain reestablished hyper-spectral data. The method provided by the invention is high in degree of compression and high in precision during satellite hyper-spectral remote sensing data compression.

The invention discloses a multi-channel compressed sensing optimization method and system based on compressed sensing, and belongs to the technical field of compressed sensing, and the method comprises the steps: carrying out the singular value decomposition of a measurement matrix, obtaining an optimized observation value vector, and reconstructing an original signal through employing a synchronous orthogonal matching pursuit joint reconstruction algorithm. Singular value decomposition is carried out on the measurement matrix, the separation design of the measurement matrix and the reconstruction matrix is achieved, the separable reconstruction matrix is obtained, the correlation between measurement values is reduced due to the fact that optimized reconstruction matrix lines are mutuallyorthogonal, the measurement values are used for reconstructing original signals, and the signal reconstruction precision is greatly improved.

The invention discloses a magnetic resonance multi-contrast image reconstruction method and device. The method includes the following steps: collecting magnetic resonance signal data at N contrasts, wherein the magnetic resonance signal data includes signal data reflecting organizational structure information and signal data reflecting independent contrast information at each contrast; carrying out image reconstruction according to the signal data reflecting independent contrast information at each contrast to get contrast information images; carrying out image sharing information joint reconstruction according to the signal data reflecting organizational structure information to get a shared organizational structure information image of a multi-contrast image; and combining each contrastinformation image with the shared organizational structure information image of the multi-contrast image to generate an initial image at each contrast. The method not only can avoid repeated scanningof organizational structure information and save scanning time, but also can improve the accuracy of reconstructed images.

Systems and methods for joint reconstruction and segmentation of organs from magnetic resonance imaging (MRI) data are provided. Sparse MRI data is received at a computer system, which jointly processes the MRI data using a plurality of reconstruction and segmentation processes. The MRI data is processed using a joint reconstruction and segmentation process to identify an organ from the MRI data. Additionally, the MRI data is processed using a channel-wise attention network to perform static reconstruction of the organ from the MRI data. Further, the MRI data can is processed using a motion-guided network to perform dynamic reconstruction of the organ from the MRI data. The joint processing allows for rapid static and dynamic reconstruction and segmentation of organs from sparse MRI data, with particular advantage in clinical settings.

The invention relates to a medical implantation material and especially relates to a temporomandibular joint fovea prosthesis. The temporomandibular joint fovea prosthesis is especially suitable for various diseases, such as serious bony ankylosis, joint retrogression, rheumatic arthritis, joint coloboma caused by tumor and traumatism, failure of replacing operation for local joint prosthesis and failure of joint reconstruction of autologous tissues. The temporomandibular joint fovea prosthesis comprises a joint fovea prosthesis, a joint fovea bolt, a mandible prosthesis and a mandible bolt, wherein the joint fovea prosthesis is composed of two parts: a joint surface of joint fovea and a joint fovea main body; the joint surface of joint fovea has a shape of an oval hole sunken toward the joint fovea main body; the joint fovea main body is hollow; the joint fovea prosthesis is fixed on a bone through the joint fovea bolt mounted in a joint fovea bolt hole; the mandible prosthesis is composed of two parts: a hemispheroid and a mandible main body; the hemispheroid is connected with the upper part of the mandible main body; the hemispheroid is in contact with the joint surface of joint fovea of the joint fovea prosthesis; the mandible prosthesis is placed on the bone; and the mandible prosthesis is fixed on the bone through the mandible bolt mounted in a mandible bolt hole.

The invention discloses a multi-view compressed sensing image reconstruction method. The multi-view compressed sensing image reconstruction method includes: receiving measured values of various view-angle images at a receiving end and independently reconstructing various view-angle images; performing DE (disparity estimation) and DC (disparity compensation) on the basis of blocks among the reconstructed images and acquiring predicted images of each view angle; solving a joint reconstruction problem set for each view angle by means of the predicted images and the measured values and determining the final reconstruction images of various view angles. In the set joint reconstruction problem, not only can sparsity characteristics of residual errors between the view-angle images and the predicted images in a transform domain be taken into consideration, but the sparsity characteristics of the view-angle images in the transform domain is further considered. Meanwhile, the joint reconstruction problem is solved into an alternative iteration problem of view-angle reconstruction images and residual-error images and can be solved by multiple times of iteration. By the multi-view compressed sensing image reconstruction method, performance and effect of the image reconstruction can be effectively improved.

The invention discloses a broadband distributed Bayes compression spectrum sensing method. The method is applied to detection of broadband spectrum occupation condition in a cognitive radio system and is a new method for spectrum sensing in a broadband range by use of a Bayes compression sensing technology. The actual distribution condition, with which Bayes parameters are in compliance, is considered under the spectrum sensing condition of the distributed multiple cognitive users. Due to introduction of a Laplace hierarchical prior model, experience information occupied by spectrum is fused in the detection process. Cooperative detection of multiple cognitive users is fully considered, reconstruction errors brought about by compression sensing are reduced to the greatest extent through a distributed joint reconstruction algorithm, and the spectrum occupation condition of each sub-band can be detected rapidly and accurately.

A method and a related system (IMA) for reconstructing an image of an electron density in a subject PAT. An x-ray radiation imager is used to expose the subject PAT to radiation to receive projection data. The reconstruction method combines projection data from two channels, namely Compton scatter based attenuation data pC and phase shift data pd Phi.

A PET (Positron Emission Tomography)-MRI (Magnetic Resonance Imaging) maximum posterior joint reconstruction method comprises the following steps of I, collecting PET data and MRI data of an object; II, constructing a mathematical statistical model for joint reconstruction of PET-MRI; III, designing a cross prior model by use of correlation of to-be-constructed PET and MRI images in the mathematical statistical model; IV, performing joint construction on the mathematical statistical model of the PET and MRI images constructed in the step II by use of a maximum posteriori method in combinationwith the cross prior model of the PET and MRI designed in the step III to obtain an optimization equation with a constrained objective function; and V, performing iterative calculation on the optimization equation with the constrained objective function obtained in the step IV to synchronously obtain PET and MRI reconstructed images. According to the PET-MRI maximum posterior joint reconstructionmethod, the PET and MRI images can be synchronously reconstructed, the PET image noise is inhibited, the MRI image artifact is reduced, the quantification level of the reconstructed image is improved,and the clinical diagnosis can be better assisted.

The invention discloses a three-dimensional human body model joint reconstruction method based on a monocular image, electronic equipment and a storage medium, and the method comprises the steps: obtaining a body monocular image, a hand monocular image and a face monocular image based on an original monocular image, and extracting the coordinates of two-dimensional key points in the body monocular image, the hand monocular image and the face monocular image; the method comprises the following steps of: obtaining a body candidate frame, a hand candidate frame and a face candidate frame, cutting an original monocular image based on the three candidate frames to obtain three feature images, respectively obtaining features in each feature image by using a local feature extraction network, splicing the features to obtain cascade features, training an SMPLX model, and carrying out three-dimensional human body reconstruction based on the cascade features; the depth information extracted by the method is richer, and the constructed three-dimensional human body model is higher in precision and shorter in time consumption.

The invention discloses an AI-based human body multi-core DWI joint reconstruction method. The method comprises the steps: establishing a human body multi-core DWI image training set; establishing a human body multi-core DWI joint reconstruction model; defining a loss function of the human body multi-core DWI joint reconstruction model; training a human body multi-core DWI joint reconstruction model by adopting a gradient descent algorithm; inputting a new under-sampling DWI image into the trained human body multi-core DWI joint reconstruction model, and performing forward propagation of the model to obtain a final reconstruction image containing different b values. According to the method, the high-quality reconstructed image can be obtained under the high acceleration multiple, and the reconstruction speed is high.

The invention discloses a low-overhead power data acquisition method based on distributed compressed sensing. The overhead for acquiring power data in an intelligent power grid is reduced. The electric power sparse matrix can perform better sparse representation on the electric power data. According to the joint reconstruction algorithm, the original data can be accurately reconstructed at the terminal, and the power data transmission overhead is reduced to a great extent.

A number of surgical procedures, particularly orthopaedic procedures, involve manual percussion, typically mallet-applied. As discussed in detail herein, such procedures can be problematic as they not only require a second set of hands but are often associated with incomplete and/or uncontrolled axial movement and/or injury to the surgeon's hands. Described herein are specialized hand-held devices, as well as systems, kits, and methods associated therewith, that avoid such problems by joining or replacing the external “mallet” with an internal “hammer” that can automate the delivery of controlled repetitive “strikes”, more particularly repeated strikes to a proximal end of a drill, driver and/or insertion device, so as to mete out pre-determined incremental movement to a distal end component, such as a sharpened socket-forming tip, an interference-plug type suture anchor, or a dilating device for modifying the cross-section of a tunnel formed in a bone. The present invention has particular utility in connection with ligament, tendon and joint reconstruction procedures, such as shoulder, ankle, and knee repair. The present invention is also applicable to those surgical procedures that require the production of and access to off-axis bone sockets.

The invention provides a temporomandibular joint (TMJ) and mandible joint prosthesis which at least comprises a TMJ fossa prosthesis, a mandible prosthesis and a joint head prosthesis, wherein the mandible prosthesis comprises a mandible ascending ramus part and a mandible extending part, the mandible ascending ramus part and the mandible extending part are connected to form an individualized simulation mandible ascending ramus and at least part of mandible body form; the tail end of the mandible ascending ramus part of the mandible prosthesis is fixedly connected with the articular head prosthesis; and the bent concave surface of the mandible prosthesis is used as the upper side, and a continuous or discontinuous porous structure area is arranged in the middle of the mandible prosthesis.The TMJ and mandible joint prosthesis can repair TMJ functions, mandible forms, dentition defects and the like of various TMJ-mandible joint defects individually at the same time to achieve joint reconstruction of TMJ-mandible-dentitions.

In joint reconstruction, repair and cushioning applications, a synthetic polypeptide material is useful that contains cross-linked polypeptides that are modeled on human elastin or other fibrous proteins. The polypeptides comprise at least three consecutive beta-sheet/beta-turn structures and at least one amino acid residue that participates in cross-linking.