9 results about "Joint reconstruction" patented technology

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 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 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.

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.

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.