29 results about "Volume reconstruction" patented technology

The invention discloses a citrus X-ray image rapid reconstruction and defect segmentation method based on sparse point cloud and 3DGS, and relates to image processing, and the method comprises the following steps: S1, obtaining X-ray image data in a citrus under a sparse view angle; s2, performing three-dimensional reconstruction on the X-ray image data by adopting a three-dimensional Gaussian point cloud reconstruction method based on adaptive density control to obtain a reconstructed three-dimensional body; s3, performing automatic defect segmentation on the reconstructed three-dimensional body through the three-dimensional segmentation model to obtain two-dimensional defect mask slices and three-dimensional defect voxel data; and S4, according to the two-dimensional defect mask slices and the three-dimensional defect voxel data, performing comprehensive evaluation on the internal defects of the citrus, and outputting an internal quality report of the citrus. According to the method, full-process automatic processing from sparse view angle X-ray image data acquisition to three-dimensional volume reconstruction to automatic defect segmentation and type identification is realized, and the purpose of efficiently, accurately and losslessly identifying the internal defects of the citrus is achieved.

A system and method combine optical and radiographic data to enhance imaging capabilities. Specifically, the system combines visually obtained patient pose position information and radiographic image information to facilitate calibrated surgical navigation. The process involves a data acquisition phase, a system calibration phase, a volume reconstruction phase, and a surgical navigation phase, all resulting in the alignment of instrument coordinates with the patient and reconstructed volume coordinates enabling tracking and navigation of surgical instruments within a reconstructed 3D volume of the patient anatomy, even if such anatomy is not exposed during a procedure.

The disclosure provides a three-dimensional CT imaging method and apparatus. The method includes: collecting two DR images with a perpendicular relationship for an object to be imaged; inputting the two DR images into a preset three-dimensional volume reconstruction model to obtain a three-dimensional volume of the object outputted by the three-dimensional volume reconstruction model; and slicing the three-dimensional volume to obtain three-dimensional CT imaging of the object, wherein the preset three-dimensional volume reconstruction model is trained using multiple training samples, each training sample comprising: a DR image group composed of two DR images with a perpendicular relationship, a pseudo-sinogram corresponding to the DR image group, and a three-dimensional volume corresponding to the DR image group.

The invention discloses a low-dose medical CT-oriented AI reconstruction and structure preserving denoising method, and relates to the technical field of AI. The method comprises the following steps: step 1, data preprocessing and initial feature map generation: obtaining a low-dose projection sequence and performing initial volume reconstruction, comparing the original projection with a forward projection obtained based on initial volume reconstruction calculation through a stripe consistency arbiter, and generating a stripe consistency label; step 2, performing iterative updating based on the stripe consistency label; and step 3, iterative optimization based on multi-party data comparison. According to the method, a unified index is established in an image domain and a projection domain, constraint on an important anatomical structure is realized by combining a structure anchoring graph and a tissue consistency field, and differentiated noise suppression and local credible signal backfilling are realized by utilizing strip consistency judgment and a ray sector reinjection mechanism, so that the ray dose is reduced, and the reliability of the system is improved. The marginal definition and tissue coherence are effectively maintained, and the diagnostic value and reliability of the low-dose CT image are remarkably improved.

The application provides a three-dimensional CT imaging method, device, electronic equipment and storage medium. The method comprises the following steps: collecting two DR images with a vertical relationship for an object to be imaged, and obtaining three-dimensional coordinate information of the object to be imaged; inputting the two DR images with the vertical relationship and the three-dimensional coordinate information into a preset three-dimensional volume reconstruction model to obtain three-dimensional volume data of the object to be imaged; and slicing the three-dimensional volume data to obtain a three-dimensional CT image of the object to be imaged. The method can improve the three-dimensional volume reconstruction speed on the basis of ensuring the imaging quality, and then quickly obtain a three-dimensional CT image with high quality.

The invention is entitled "System and method for local three-dimensional volume reconstruction using standard fluoroscope." A system for constructing fluoroscope-based three-dimensional volume data of a target region within a patient from two-dimensional fluoroscope images, including a marker structure, a fluoroscope imaging device configured to acquire a sequence of images of the target region and the marker structure, and a computing device. The computing device is configured to estimate a pose of the fluoroscope imaging device for at least a plurality of images of the sequence of images based on detection of a most likely and maximum probability of a projection of the marker structure as a whole on each of the plurality of images. The computing device is further configured to construct fluoroscope-based three-dimensional volume data of the target region based on the estimated pose of the fluoroscope imaging device.

The invention discloses a food calorie calculation method based on binocular vision, and relates to the technical field of computer vision and nutrition analysis. The method comprises the following steps: firstly, acquiring a food image by using a calibrated binocular camera, and preprocessing the image; and then a semi-global block matching algorithm is combined with weighted least square filtering to calculate and optimize a disparity map, and an edge-disparity collaborative network is introduced to improve the matching precision of the weak texture food. Then, segmenting a food area through a U-Net network, and outputting an accurate contour after edge optimization; and identifying the specific category of the food by using a lightweight classification network. And then, according to the food form, adaptively selecting an equivalent cylinder method or a point cloud reconstruction method to carry out three-dimensional volume reconstruction, and converting the volume into mass in combination with a density database. And finally, querying a calorie database containing cooking mode information to obtain an accurate calorie value. The method is obviously superior to the prior art, and an efficient and reliable solution is provided for family diet management and health monitoring.

The invention discloses a holographic three-dimensional bioassay method based on depth label coding and an attention mechanism. The invention relates to a holographic biometric platform employing depth label coding and end-to-end attention-enhanced three-dimensional reconstruction. The platform can enhance the depth of field and volume detection capability, and significantly improve the detection sensitivity and accuracy. A depth label coding strategy is integrated into a neural network architecture based on an attention mechanism, and three-dimensional volume reconstruction is converted into a dual-task framework combining bounding box detection and depth classification. And the limitation of a traditional generation type holographic reconstruction algorithm is overcome, so that accurate real-time three-dimensional positioning is realized. The holographic three-dimensional space positioning is beneficial to expanding field depth application and improving the density of the countable microspheres, chloramphenicol can be detected in a wide dynamic range, and compared with the traditional bioassay based on a two-dimensional hologram, the sensitivity is improved.

The invention discloses a collateral physiology intelligent evaluation system and method based on a coronary artery angiography image, and the system comprises an image collection module, a segmentation module, a reconstruction module, a modeling module, and an index calculation module. Wherein the image acquisition module is used for acquiring coronary artery angiography and screening minimum overlapped and shortened angiography projection images after CTO-PCI front and back side branch blood supply arteries and occluded blood vessels are opened; the segmentation module is used for carrying out frame-by-frame blood vessel segmentation on the obtained coronary artery angiography by adopting a deep learning algorithm; the reconstruction module is used for performing blood vessel three-dimensional volume reconstruction on the coronary artery binary image obtained through segmentation; the modeling module is used for modeling blood flow and calculating average blood flow; the index calculation module is used for calculating the collateral branch blood flow index according to the blood flow change before and after the CTO is opened. According to the method, the subjective defect of a traditional visual scoring system is eliminated, the operation process does not need to be interrupted, special consumables are not needed, and the medical cost and the occurrence risk of vascular injury related complications in the operation are effectively reduced.

The disclosure provides a three-dimensional CT imaging method and apparatus. The method includes: collecting two DR images with a perpendicular relationship for an object to be imaged; inputting the two DR images into a preset three-dimensional volume reconstruction model to obtain a three-dimensional volume of the object outputted by the three-dimensional volume reconstruction model; and slicing the three-dimensional volume to obtain three-dimensional CT imaging of the object, wherein the preset three-dimensional volume reconstruction model is trained using multiple training samples, each training sample comprising: a DR image group composed of two DR images with a perpendicular relationship, a pseudo-sinogram corresponding to the DR image group, and a three-dimensional volume corresponding to the DR image group.

The present invention provides a method and system for self-supervised super-resolution reconstruction of magnetic resonance images based on a prior-guided diffusion model. The method comprises the following steps: image preprocessing, pseudo-pairing dataset construction, prior condition establishment, model training, model testing, and three-dimensional isotropic volume reconstruction. By constructing a self-supervised pseudo-pairing mechanism, the present invention significantly reduces reliance on true high-resolution images, enabling high-quality reconstruction of anisotropic MRI images into isotropic images. Simultaneously, by introducing multiple medical priors, including high-frequency residuals, edge structures, and region-of-interest guidance, the method effectively enhances image detail preservation and structural boundary clarity. While maintaining anatomical fidelity, it also improves model convergence speed and robustness, providing a technically feasible and clinically applicable solution for high-resolution reconstruction of medical images.

The invention relates to the field of 4DCT image data processing, in particular to a crack tracking model data preprocessing method based on a 4DCT image. Data volume reconstruction is performed based on coal rock sample synchronous loading and 4DCT scanning to obtain a coal rock sample dynamic fracture evolution sequence, angle deviation and background interference are eliminated by combining horizon slice integration, circular mask denoising and fracture rotation alignment, and the image contrast is enhanced by using gray threshold compression and fracture feature enhancement. And finally, accurately outputting the whole fracture spatio-temporal evolution process through a standardized preprocessing sample set input model. According to the method, the technical bottlenecks of discontinuous dynamic data, horizon offset and characteristic fuzziness of the traditional CT are overcome, the identification precision of the crack tracking model on microcrack initiation and expansion is remarkably improved, and a high-reliability dynamic observation method is provided for coal rock mechanical behavior analysis and engineering safety evaluation.

Methods and systems for generating animatable garments from a single image are disclosed. The system accesses a monocular image depicting a person wearing a fashion item, and generates a three-dimensional (3D) mesh representing the person in a normative space. The system determines a gesture of the person depicted in the monocular image and modifies the gesture of the 3D mesh to match the determined gesture of the person. The system extracts a portion of the 3D mesh corresponding to the fashion item, and generates an augmented reality (XR) item using the extracted portion of the 3D mesh.

The invention discloses an artificial intelligence-driven three-dimensional digital volume-dependent deformation measurement system and method. The system comprises a three-dimensional speckle volume data acquisition unit, a three-dimensional coordinate normalization unit, a deep neural network prediction unit, a three-dimensional physical constraint unit and a three-dimensional field reconstruction output unit. The numerical stability of network input is ensured through three-dimensional coordinate normalization, synchronous prediction of a three-dimensional displacement field and a strain tensor field is realized by adopting an end-to-end neural network architecture, and physical rationality between the displacement field and the strain field is ensured by introducing volume reconstruction consistency constraint and three-dimensional deformation compatibility constraint. According to the invention, self-supervised learning is carried out only by relying on volume image pairs before and after deformation and physical constraints, synchronous and high-precision measurement of a three-dimensional displacement field and a strain tensor field is realized, and the problem of error amplification caused by three-dimensional numerical differentiation in a traditional method is avoided; the method is suitable for three-dimensional deformation measurement and analysis in the fields of material science, mechanical engineering, biomedical engineering and the like.

The application belongs to the technical field of image processing, and discloses a fast splicing method for CBCT vertical scanning, which comprises the following steps: step S1, a plurality of scanning positions of a rotating platform are arranged along the Z direction, and a scanning object is scanned at each scanning position to obtain scanning data; step S2, the angle offset of adjacent scanning positions is calculated according to the scanning data; volume reconstruction is performed on each scanning position; step S3, a common reconstructed slice image at the adjacent scanning positions is extracted, and the feature points of the slice image are determined by using a SIFT algorithm; the rotation angle of the slice image is determined by using a RANSAC algorithm; and step S4, the reconstructed volumes of the adjacent scanning positions are spliced after being rotated according to the rotation angle calculated in step S3. The application introduces a high-precision motion calibration technology and an improved image registration algorithm, the image quality is significantly improved, the joint seam artifact is eliminated, and a reliable guarantee is provided for high-resolution three-dimensional reconstruction.

The application discloses a shale oil and gas production dynamic prediction method based on GCN-GRU, and relates to the technical field of oil and gas well production prediction.According to the related parameters of each fractured horizontal well in a shale oil and gas well station, the volume reconstruction region spatial relationship and the artificial fracture distribution characteristics are combined, the interwell interference coefficient is calculated, the multi-well adjacency matrix is constructed, the production dynamic data is obtained according to the production of each fractured horizontal well in the shale oil and gas well station, the production dynamic data is distributed to a training set and a test set, the GCN-GRU model is constructed, the production of the fractured horizontal well is predicted by using the production dynamic data in the training set and the multi-well adjacency matrix to train the model, and the accuracy of the model prediction after training is verified by using the production dynamic data in the test set.The interwell interference is quantified, and the high-precision prediction of the shale oil and gas production under the conditions of three-dimensional development and large-scale hydraulic fracturing is realized.

Slice alignment based on reconstructed volume. Disclosed herein are apparatuses and methods for aligning a charged particle beam based on a volume reconstruction. An example method includes at least: forming a reconstructed volume of a portion of a sample, the sample including a plurality of structures, and the reconstructed volume including a portion of the plurality of structures; performing a mathematical transformation on each of a plurality of planes of the reconstructed volume over a range of angles; and determining a target orientation of the sample over the range of angles based on the mathematical transformation of each of the plurality of planes, wherein the target orientation aligns the plurality of structures parallel to an optical axis of a charged particle beam.

The invention provides an ultrafast multi-dimensional super-resolution imaging method based on fluctuation high-order acoustic reconstruction, belongs to the technical field of super-resolution imaging, and solves the problem that in the existing ultrasonic imaging technology, when the imaging frame rate is reduced, the imaging effect and the imaging speed are obviously reduced. Comprising the following steps: S1, performing singular value decomposition filtering in a time domain to extract a microbubble signal; s2, extracting MB flow direction information by using MB direction filtering in the Fourier domain; s3, background filtering is carried out in the wavelet domain; s4, interpolation is carried out in a Fourier domain, and then a Richardson-Lucy (RL) pre-deconvolution step is carried out; s5, further separating the activity of the MB by using MB density filtering in the time domain; s6, performing high-order accumulation calculation; s7, using a three-dimensional sparse convolution algorithm to improve the spatial resolution and the imaging quality; and S8, completing volume reconstruction by using data fusion.

A system and method combine optical and radiographic data to enhance imaging capabilities. Specifically, the system combines visually obtained patient pose position information and radiographic image information to facilitate calibrated surgical navigation. The process involves a data acquisition phase, a system calibration phase, a volume reconstruction phase, and a surgical navigation phase, all resulting in the alignment of instrument coordinates with the patient and reconstructed volume coordinates enabling tracking and navigation of surgical instruments within a reconstructed 3D volume of the patient anatomy, even if such anatomy is not exposed during a procedure.

Methods and systems for generating a body mesh from a single image are disclosed. The system predicts both a volumetric reconstruction tensor of a monocular image and a pose of an object by applying a first machine learning model to the monocular image. The system identifies a portion of a pose of an object corresponding to a point in a canonical space associated with the set of location encoding information. The system obtains points of a volumetric reconstruction tensor corresponding to the identified portion of the pose. The system classifies the obtained points as being inside or outside the canonical volume by applying a second machine learning model together with the set of position encoding information to the points of the obtained volume reconstruction tensor. The system generates a three-dimensional (3D) mesh representing an object in a normative space.

The invention discloses a material multi-scale strain-microstructure analysis method and system with adaptive grids, multi-modal fusion and life prediction. The method comprises the following steps: acquiring multi-angle projection data of a material under a loading condition by using a switchable multi-mode internal imaging probe, and fusing multi-source sensing information to form unified volume reconstruction; three-dimensional noise reduction and detail enhancement are realized by utilizing a generative adversarial network, and high-reliability data are calculated and screened based on voxel confidence and are input into an adaptive grid refining module, so that global and local matching precision improvement is realized; the microstructure features are segmented by the convolutional neural network and then are quantitatively associated with the strain field input statistics / depth modeling module, and a fatigue life or failure probability result is output through a life prediction interface. Compared with the prior art, the adaptability of the multi-scale strain measurement method in an imaging mode is improved, the problem that the fixed grid division precision and the calculation efficiency are difficult to consider at the same time is solved, and the matching error in a low signal-to-noise ratio scene is remarkably reduced.

The invention provides a seabed interface layer three-dimensional visualization method based on an electrical probe networking monitoring system. An electrical probe network formed by a plurality of electrical probes in a networking mode, an underwater collection station, an offshore buoy, a satellite communication system, a remote terminal and an overwater and underwater communication cable connecting the electrical probes, the underwater collection station and the offshore buoy are included. Through the technical scheme of the invention, a solution is provided for seabed environment data acquisition from'point 'to'body'. On the basis of the system, a calculation analysis method integrating data body reconstruction, multi-interface intelligent identification and multi-parameter synchronous inversion is provided, spanning from discrete data to structural characteristics and attribute distribution is achieved, and the space-time evolution rule of the internal structural characteristics of the seabed interface layer in the extreme environment can be clearly, visually and quantitatively analyzed.

This invention discloses a magnetic resonance imaging (MRI) image reconstruction method based on three-dimensional Gaussian volume rendering. The method includes: first, acquiring point clouds of slice data; second, constructing a three-dimensional Gaussian primitive mesh and fitting the point cloud to obtain basic voxel intensities; then, introducing a neural residual field to learn and predict intensity residuals to correct the basic intensities; finally, fusing the basic intensities and residuals to generate high-fidelity final voxel intensities, and reconstructing the slice data into a three-dimensional volume based on this, mapping it to the target image space. By using this invention, high-resolution MRI volume reconstruction tasks can be completed quickly, meeting the time-sensitive requirements of clinical workflows. This invention can be widely applied in the field of image processing.

The application discloses a kind of multi-sampling rate seismic data volume reconstruction method and device.The method includes, according to multi-sampling rate seismic data volume, establish observation system regular empty data volume, assign the data of matching way in seismic data volume or set initial value for the way therein, constitute initial data volume;Initial data volume is divided into multiple small data volumes, one-dimensional Fourier transform of time variable is carried out to each small data volume, and the data volume of f-x-y domain is obtained;For each f-x-y domain data volume, the transformed data volume is obtained by three-dimensional curve wave iterative transformation;The initial reconstruction data volume is obtained by combining the transformed data volume;According to seismic data volume, the initial reconstruction data volume is corrected, and the reconstruction data volume is obtained.The curve wave transformation of three-dimensional seismic data volume is carried out in f-x-y domain using split beam, the problem of large memory occupation in large data volume three-dimensional curve wave transformation is solved, and the reconstruction of multi-sampling rate seismic data can be completed with high fidelity and high efficiency.

A three-dimensional reconstruction method of the craniomaxillofacial region based on free-hand ultrasound is proposed, which includes: (1) real-time volume reconstruction: using a binocular camera to track the position of the ultrasound probe in real time, while adopting a two-stream neural network to segment the ultrasound image and reconstruct the voxel grid in real time; (2) voxel denoising: using morphological post-processing and template matching methods to eliminate outliers, and then converting the voxel model into a point cloud; (3) surface reconstruction: reconstructing the point cloud data into a polygonal mesh through a self-supervised implicit neural representation network to obtain a smooth bone surface.

A system and method for constructing fluoroscopic-based three dimensional volumetric data from two dimensional fluoroscopic images including a computing device configured to facilitate navigation of a medical device to a target area within a patient and a fluoroscopic imaging device configured to acquire a fluoroscopic video of the target area about a plurality of angles relative to the target area. The computing device is configured to determine a pose of the fluoroscopic imaging device for each frame of the fluoroscopic video and to construct fluoroscopic-based three dimensional volumetric data of the target area in which soft tissue objects are visible using a fast iterative three dimensional construction algorithm.

The embodiment of the invention discloses a personalized lateral ventricle image generation and disease risk assessment method and device, and the method comprises the steps: obtaining an original historical image, an original current image and a plurality of original clinical features of a patient, carrying out the standardization processing, and carrying out the three-dimensional volume reconstruction of the standardized historical image and the standardized current image, obtaining a reconstructed historical image and a reconstructed current image; performing lateral ventricle segmentation and feature extraction on the reconstructed historical image and the reconstructed current image by using a neural network model; performing image registration and alignment on the reconstructed historical image and the reconstructed current image based on the feature positioning key points to obtain an aligned historical image and current image, and extracting difference features based on the historical image and the current image; and inputting the difference features and the standardized clinical features into a trained random forest model, and obtaining patient disease types, corresponding disease probabilities and risk levels based on overall disease probability analysis, which are output by the random forest model.

This invention belongs to the field of volume measurement technology and relates to a method for calculating the volume of irregularly shaped latex products. The method includes: fixing the irregularly shaped latex product to be measured on a measuring carrier and immersing it vertically in a measuring liquid at a constant rate; synchronously collecting the immersion depth and liquid level in real time; retrieving the carrier's drainage reference; calculating the instantaneous drainage cross-sectional area based on the rate of change of liquid level with depth; subtracting the carrier's discharge capacity to obtain the intrinsic instantaneous cross-sectional area; monitoring the area jump between sampling points; when the jump exceeds a threshold, using the slope before and after the abrupt change point for interpolation compensation; and performing depth integration on the compensated area to obtain the three-dimensional volume. This invention effectively eliminates transient liquid surface distortion in water immersion measurements, eliminates measurement errors caused by bubble rupture or capillary seepage, and improves the physical certainty of volume reconstruction for complex structures.

The application discloses an AI reconstruction and structure maintaining denoising method for low-dose medical CT, relates to the field of AI technology, and comprises the following steps: step 1, data preprocessing and initial feature map generation, including: obtaining a low-dose projection sequence and an initial volume reconstruction, comparing original projections with forward projections calculated based on the initial volume reconstruction through a strip consistency arbitrator, and generating a strip consistency label; step 2, iterative updating based on the strip consistency label; and step 3, iterative optimization based on multi-party data comparison. By establishing a unified index in the image domain and the projection domain, combining a structure anchor graph and a tissue consistency field, and realizing constraint on important anatomical structures, and by simultaneously utilizing strip consistency arbitration and a ray sector back-annotation mechanism to realize differential noise suppression and local reliable signal backfilling, the application can effectively maintain edge definition and tissue coherence while reducing the radiation dose, and significantly improve the diagnostic value and reliability of low-dose CT images.