19 results about "Joint reconstruction" patented technology

The invention belongs to the technical field of three-dimensional reconstruction, and particularly discloses a three-dimensional human body and scene interaction reconstruction method and system. The method is a three-dimensional human body and scene interactive reconstruction method for monocular video input, not only can quickly complete three-dimensional Gaussian reconstruction of a human body, a static scene and a moving object, but also can quickly complete the three-dimensional Gaussian reconstruction of the human body, the static scene and the moving object by introducing technical means such as posture correction, camera external parameter space regularization, joint reconstruction consistency loss and layered fusion rendering. And accurate alignment of the human body, the scene and the object in spatial positions, shielding relations and illumination styles is ensured, and dynamic interaction reconstruction conforming to real physical logic is realized. Finally, a joint rendering image with high fidelity, continuous time sequence and consistent structure can be generated, a stable and reliable three-dimensional expression capability is provided for complex human body actions, object interaction and scene understanding, and the three-dimensional interaction reconstruction quality and application value under the monocular video condition are greatly improved.

The invention discloses a multi-contrast MRI joint reconstruction method, system and device and a medium, and relates to the technical field of medical imaging and deep learning, and the method comprises the following steps: collecting under-sampling MRI image data of different contrasts, and generating an optimization objective function based on a plurality of contrasts; decomposing the optimization objective function into a first sub-problem related to an auxiliary variable and a second sub-problem related to an objective variable; and alternately solving the first sub-problem and the second sub-problem in sequence to obtain a plurality of optimal MRI reconstructed images. According to the method, feature interaction of multi-contrast data is carried out in a spatial domain and a frequency domain, so that efficient complementation and collaborative modeling of multi-contrast features are realized, and the problem of insufficient utilization of information between contrasts in a traditional method is solved. By sensing the characteristics of each contrast, targeted prompts can be generated to guide the reconstruction process. Therefore, the quality of low-quality contrast is improved, and the overall reconstruction result is enhanced.

The invention provides a PET-MR joint reconstruction method and system based on an expert network modular regularizer. The method comprises the following steps: constructing a PET-MR joint reconstruction backbone network based on a decoupling iteration framework; s2, constructing and integrating a U-Net joint regularizer for expert network modularization, and integrating the U-Net joint regularizer into the iterative reconstruction framework in the step S1 to obtain a final joint reconstruction network model; applying a comprehensive loss function to supervise an end-to-end learning process of the joint reconstruction network model; constructing a training data set, and performing end-to-end joint training on the joint reconstruction network model by using a loss function; in the reasoning stage, PET and MR measurement data to be reconstructed are input into the trained joint reconstruction network model, and finally a reconstructed PET image and a reconstructed MR image are output. According to the method, the functional metabolism information accuracy of the reconstructed PET image and the anatomical structure detail definition of the MR image are enhanced, and the high feature fidelity of each modal image is ensured, so that the overall quality and clinical diagnosis value of the reconstructed image are improved.

The invention discloses a compressed sensing MRI (Magnetic Resonance Imaging) reconstruction method and system based on cross-frame guidance and dual-view interaction enhancement. MRI image pairs are obtained, and image compressed sensing reconstruction is performed by using a network comprising a sampling module, an initial reconstruction module and a plurality of iterative reconstruction modules. Each iterative reconstruction module comprises two branches and a double-view-angle interaction enhancement module, and one branch outputs key frame reconstruction guide information; and the other branch reconstructs the auxiliary frame reconstruction image, and performs adjacent slice joint reconstruction by combining key frame reconstruction guide information. Frequency domain information is reserved by constructing image pairs with different sampling ratios, adjacent slices have high spatial correlation, the cross-frame guide module is used for performing feature interaction and information compensation between the slices with different sampling ratios, the double-view-angle interaction enhancement module is used for enhancing structural continuity from different axial directions, and the structural continuity is enhanced. And the global structure is consistent and details are reserved.

The invention provides a compact sandstone three-dimensional mineral characterization method based on a CT image and an EDS image, and the method comprises the following steps: (1) firstly scanning and recognizing a real compact sandstone sample by using a micron CT to obtain a corresponding CT image, then analyzing and scanning the compact sandstone sample by using an X-ray energy disperse spectroscopy to obtain a compact sandstone sample; obtaining the proportions of various minerals in the sample by using an XRD (X-Ray Diffraction) experiment; (2) registering the two-dimensional CT image slice and the EDS image; (3) simultaneously carrying out joint reconstruction on the CT image slices and the EDS images to obtain a series of reconstruction data, and combining the reconstruction data with the image scanning data to obtain a training database; and (4) performing optimization adjustment on the three-dimensional mineral model by adopting an optimization algorithm by taking the mineral composition measured by the XRD experiment as a constraint, so as to obtain a three-dimensional mineral structure closest to the real rock sample. According to the method, three-dimensional mineral distribution closest to real rock core mineral components can be obtained, and the characterization precision is improved while the efficiency is considered.

The application discloses an anatomic structure perception low-field multi-contrast fast MRI joint reconstruction method, and is specifically implemented according to the following steps: step 1, constructing an anatomic structure perception low-field multi-contrast magnetic resonance image joint reconstruction model; step 2, constructing an anatomic structure perception low-field multi-contrast magnetic resonance image joint reconstruction network according to the reconstruction model constructed in step 1; step 3, training the anatomic structure perception low-field multi-contrast magnetic resonance image joint reconstruction network; and step 4, applying the trained anatomic structure perception low-field multi-contrast magnetic resonance image joint reconstruction network to magnetic resonance imaging. The method solves the problem of insufficient imaging optimization of a lesion part in the existing reconstruction method.

The invention provides a joint reconstruction and dynamic registration method and device, equipment and a medium, and relates to the technical field of image processing. The method comprises the following steps: obtaining a fused feature map through feature extraction operation and feature fusion operation, and carrying out dynamic deformation registration operation on a preprocessed SPECT basic image and a target CT image by using the fused feature map to obtain a registered CT image, a registered SPECT image and a dynamic displacement field for compensating physiological movement. And performing reconstruction operation on the registered SPECT image by using a reconstruction constraint condition, the registered CT image and the dynamic displacement field to obtain a reconstructed SPECT image. According to the method, a joint optimization framework is constructed by deeply embedding dynamic deformation registration into a reconstruction process, so that the SPECT reconstruction can fully utilize CT anatomy prior and displacement field constraint, a traditional serial error chain is broken, and the reconstruction precision and function-anatomy alignment degree are remarkably improved.

The invention relates to the field of intelligent medical treatment, in particular to a portable 4D ultrasonic distributed beam and AI joint reconstruction method and device and a program product. Comprising the steps that N ultrasonic echo signals are obtained, and N is a natural number larger than 1; carrying out delay correction calculation on the N paths of echo signals and then carrying out weighted stacking to obtain a distributed beam; layering the distributed beam to obtain a display data stream and a reconstruction data stream; carrying out image reconstruction on the reconstructed data stream and then carrying out image target region segmentation to obtain a segmentation mask; performing geometric parameter calculation on the segmentation mask to obtain ultrasonic target area data; obtaining an ultrasonic display image through the display data stream; and superposing and fusing the ultrasonic display image and the ultrasonic target area data to obtain a final ultrasonic image. The application has good clinical value.

The application discloses a solid internal temperature field and thickness efficient joint reconstruction method based on a quasi-Newton method, belongs to the field of ultrasonic nondestructive detection, and comprises the following steps: constructing a multi-parameter inversion problem with boundary heat flow and thickness as decision variables, defining an objective function containing a regularization term, and converting the inversion problem into a nonlinear optimization problem; for the nonlinear optimization problem, the quasi-Newton method L-BFGS is used to invert the heat flow, and the steepest descent method DG is used to invert the thickness, so that the internal temperature of a high-temperature structure and the size and thickness thereof are reconstructed. The application can realize synchronous high-precision and high-efficiency reconstruction of the internal temperature field and thickness size of a solid structure.

The application discloses a TOF-PET reconstruction method based on implicit neural representation, which has a significant technical advantage in the deep integration of physical modeling and data-driven methods. Unlike traditional deep learning methods that rely only on large-scale training data for end-to-end mapping, the method explicitly embeds the physical forward model of TOF-PET imaging in the INR framework. By introducing a strict data consistency term, the network output always satisfies the system projection constraint and TOF response characteristics. This effectively avoids problems such as excessive smoothing, artifact enhancement, and structural distortion under adverse imaging conditions such as low count and low dose. The method achieves high-precision, quantifiable, and physically interpretable reconstruction results, significantly improving the reliability of the reconstructed image in clinical quantitative analysis. Without changing the overall modeling idea, the method can be flexibly extended to dynamic PET imaging, activity-attenuation joint reconstruction, motion compensation reconstruction, and multi-modal fusion tasks such as PET-CT and PET-MRI, and can handle a variety of complex imaging problems.

The invention discloses a solid internal temperature field and thickness efficient joint reconstruction method based on a quasi-Newton method, and belongs to the field of ultrasonic non-destructive detection, and the method comprises the steps: constructing a multi-parameter inversion problem with boundary heat flow and thickness as decision variables, defining a target function containing a regularization item, and converting the inversion problem into a nonlinear optimization problem; and aiming at the nonlinear optimization problem, combining a quasi-Newton method L-BFGS inversion heat flow with a steepest descent method DG inversion thickness, and reconstructing the internal temperature and the size thickness of the high-temperature structure. According to the invention, synchronous high-precision and high-efficiency reconstruction of the internal temperature field and the thickness size of the solid structure can be realized.

The application discloses a compressed sensing MRI reconstruction method based on cross-frame guidance and dual-view interactive enhancement and a system thereof. MRI image pairs are acquired, and a network including a sampling module, an initial reconstruction module and a plurality of iterative reconstruction modules is used for image compressed sensing reconstruction. Each iterative reconstruction module includes two branches, a dual-view interactive enhancement module, one branch outputs key frame reconstruction guidance information, and the other branch reconstructs auxiliary frame reconstruction images, and combines the key frame reconstruction guidance information to perform adjacent slice joint reconstruction. The application retains frequency domain information by constructing image pairs with different sampling ratios, adjacent slices have high spatial correlation, a cross-frame guidance module is used to perform feature interaction and information compensation between slices with different sampling ratios, a dual-view interactive enhancement module is used to strengthen structural continuity from different axial directions, global structural consistency is ensured, and details are retained.

Apparatus and methods for joint and bony segment replacement may utilize additive manufacturing (e.g., 3D printing) of various anatomic constructs with PEEK / zeolite / ion (PZI) material. Heavy metal ion loading options may be utilized which may provide differing properties to different surfaces of the resultant apparatus. Moreover, using the PZI material, the apparatus being implanted in joint and bony segment replacement may favorably manipulate the biologic microenvironment in which it may be implanted. Further, through use of PZI material, current large joint replacement options also may be disrupted including, but not limited to, modifications of the joint replacement itself, and the augments and supporting joint reconstruction devices used in bone loss situations. Methods for joint and bony segment replacement may provide mixing two or more types of PZI material together through an additive manufacturing process; and introducing the mixture as part of an implantable apparatus.

The application belongs to the technical field of three-dimensional reconstruction, and specifically discloses a three-dimensional human body and scene interactive reconstruction method and system. The method is a three-dimensional human body and scene interactive reconstruction method for monocular video input, which can quickly complete three-dimensional Gaussian reconstruction of human bodies, static scenes and moving objects, and through introduction of posture correction, camera external parameter space regularization, joint reconstruction consistency loss and hierarchical fusion rendering technical means, ensures accurate alignment of the human body, the scene and the object in spatial position, occlusion relationship and illumination style, and realizes dynamic interactive reconstruction conforming to real physical logic. Finally, the application can generate high-fidelity, time-continuous and structure-consistent joint rendering images, provides stable and reliable three-dimensional expression capability for complex human body motion, object interaction and scene understanding, and greatly improves the three-dimensional interactive reconstruction quality and application value under monocular video conditions.

The application discloses a multi-contrast MRI joint reconstruction method, system, device and medium, relates to the technical field of medical imaging and deep learning, and comprises the following steps: collecting under-sampling MRI image data of different contrasts, and generating an optimization objective function based on multiple contrasts; the optimization objective function is decomposed into a first sub-problem about auxiliary variables and a second sub-problem about target variables; the first sub-problem and the second sub-problem are alternately solved in sequence, and multiple optimal MRI reconstruction images are obtained. The application realizes efficient complementary and collaborative modeling of multi-contrast features by performing feature interaction of multi-contrast data in the spatial domain and the frequency domain, and overcomes the problem of insufficient utilization of information between contrasts in the traditional method. By perceiving the features of each contrast, targeted prompts can be generated to guide the reconstruction process. Thus, the quality of low-quality contrast is improved, and the overall reconstruction result is enhanced.

This invention discloses a method for joint reconstruction of refractive surfaces and underwater scenes based on 3D Gaussian ray tracing, belonging to the field of computer vision and 3D reconstruction technology. By designing a hybrid water surface representation method, the water surface is represented as a neural height field; refraction calculations are performed to obtain the corresponding rays after refraction through the water surface; the underwater scene is modeled as a 3D Gaussian field for underwater scene rendering; a multi-view image supervision model is constructed, a 3D reconstruction loss function is designed, and end-to-end joint optimization is performed; thus, joint reconstruction of refractive surfaces and underwater scenes based on 3D Gaussian ray tracing is achieved. This invention achieves high-precision reconstruction while ensuring fast training speed and real-time new perspective rendering frame rate, solving the technical problems of high computational cost, multi-view geometric inconsistency, and inability to efficiently extract and separate water surface and underwater geometric structures in existing underwater scene 3D reconstruction methods.

The invention discloses a multi-person three-dimensional body posture and spatial positioning combined reconstruction method and system, and relates to the field of multi-modal three-dimensional reconstruction. The method comprises the steps that CSI signals are collected and preprocessed, and radio frequency feature representation is constructed; human body structured visual features are extracted by using a camera, and are aligned with CSI data through a timestamp and spatial calibration; a continuous three-dimensional radio frequency implicit field is constructed based on NeRFs, signal intensity is synthesized through a volume rendering technology, and deep fusion is carried out on the signal intensity and visual features under a cross-modal attention mechanism; a Transform encoder and double-decoder structure is adopted, three-dimensional joint point coordinates and spatial position coordinates of multiple persons are regressed, and joint optimization of postures and positioning is achieved through multitask attention. According to the method, the NeRF radio frequency field and the multi-head attention mechanism are fused to carry out multi-person three-dimensional human body posture and positioning combined reconstruction, and high-precision posture and spatial position reconstruction can be realized in a complex scene.

The application discloses a sound pressure-sound velocity synchronous reconstruction method of multi-grid iteration in photoacoustic tomography, and relates to the technical field of medical image reconstruction, and comprises the following steps: constructing a joint reconstruction least square problem by minimizing the two norms of a real detector detection signal and a simulation detection signal, and dividing the joint reconstruction least square problem into two problems for solving; problem one is to solve a sound pressure distribution by a known sound velocity distribution, and problem two is to calculate a sound velocity distribution by taking the sound pressure distribution obtained by problem one as a known sound pressure distribution, and the obtained sound pressure distribution and sound velocity distribution are taken as initial values and cycled to problem one for solving; multi-grid iteration reconstruction: joint reconstruction iteration is carried out on coarse and fine grids, and finally, the output of the fine grid is taken as the reconstructed sound pressure distribution and the reconstructed sound velocity distribution; by joint reconstruction under different scale grids, the method can simultaneously reconstruct the initial sound pressure distribution and the sound velocity distribution from photoacoustic signals, improve image quality, and accelerate the joint reconstruction speed.