133 results about "Iterative reconstruction" patented technology

The invention relates to the technical field of magnetic resonance imaging, in particular to an intelligent sparse low-rank non-Cartesian magnetic resonance dynamic imaging method, which comprises the following steps of: acquiring magnetic resonance k-space data which is subjected to non-Cartesian continuous sampling by adopting multiple coils, continuously sampling data, rearranging the continuously sampled data along a time dimension by using a framing operator to obtain under-sampled k-space data, and then inputting the framed non-Cartesian under-sampled k-space data to be reconstructed into a trained network for image reconstruction. And reconstructing a final dynamic image through depth space-time sparsity, time low-rank learning, data consistency verification and loss function constraint. Compared with the prior art, the non-Cartesian magnetic resonance dynamic image reconstruction method has the advantages that the image is reconstructed through multiple times of network feedback iteration, the reconstruction speed of the non-Cartesian magnetic resonance dynamic image is greatly increased, and the temporal-spatial resolution is improved.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention discloses a processing surface three-dimensional point cloud defect detection method based on a diffusion model. The method comprises the following steps: obtaining a three-dimensional point cloud of a processing surface of a defect-free industrial product and processing the three-dimensional point cloud to obtain a defect three-dimensional point cloud so as to construct a training set; establishing an improved diffusion model based on displacement iterative reconstruction, and training through the training set until a loss function is converged; obtaining the three-dimensional point cloud of the processing surface of the industrial product to be detected, carrying out point cloud preprocessing, and then carrying out reconstruction through the reconstruction model; and detecting and segmenting the to-be-detected three-dimensional point cloud and the reconstructed point cloud thereof through the detection function to obtain a defect detection classification and positioning result. The method is suitable for defect detection based on a three-dimensional expression form, helps to solve the problems that an existing three-dimensional defect detection method is slow in reasoning speed and large in video memory consumption, helps to realize efficient, rapid and accurate three-dimensional defect detection, and helps to detect the quality of industrial assembly line products.

The invention relates to the technical field of underwater acoustic signal processing in a sonar system, in particular to a large-aperture array robust adaptive waveform recovery method based on focusing covariance sparse reconstruction, and the method comprises the steps: carrying out the time-frequency transformation of all array element receiving time domain data, and obtaining array element domain-frequency domain data; constructing sampling covariance matrixes corresponding to different frequency points according to the array element domain-frequency domain data; constructing a multi-frequency-point focusing covariance matrix according to the sampling covariance matrix and the array manifold vector corresponding to each frequency point; constructing a sparse dictionary matrix adaptive to the focusing covariance matrix according to the linear relation between the focusing covariances of the frequency points, and obtaining a sparse expression of the focusing covariance matrix; according to the sparse representation form of the focusing covariance matrix, utilizing a sparse criterion to carry out iterative reconstruction on the focusing covariance matrix; and robust adaptive waveform recovery is carried out by using the focusing covariance matrix after sparse iteration reconstruction. According to the method, the short-time adaptive waveform recovery performance of the large-aperture array is remarkably improved.

Technology is described for handling truncated data in iterative reconstruction. A method comprises iterating on a volume of an object including a non-truncated part based on image data and at least one truncated part representing deficiently imaged data. The volume is represented by voxels. The iterating includes regularizing the non-truncated part of the volume using a first regularizer, and regularizing the truncated part of the volume using a second regularizer different from the first regularizer.

The invention discloses an image compressed sensing reconstruction method and system based on a recursive diffusion model, and belongs to the technical field of image processing and compressed sensing, and the method comprises the steps: obtaining a to-be-reconstructed original image; performing block compressed sensing sampling on an original image to be reconstructed to obtain a complete observation value; initializing the complete observation value by adopting a pseudo-inverse reprojection operation to obtain an initial reconstructed image; performing complete reconstruction on the initial reconstruction image by using a recursive diffusion model; wherein in the whole image domain, the initial reconstruction image is used as the image estimation of the current iteration, through a lightweight recursion UNet submodule and an operator condition circulation prior submodule in the recursion diffusion model, multi-step iteration reconstruction is carried out, and a final reconstruction image is output. According to the method, a recursive refinement mechanism and stride memory prior are introduced, high-quality image reconstruction is realized under a small number of iteration steps, and the calculation and storage overhead is remarkably reduced.

The invention discloses a building contour accurate extraction method based on prior information and a related device, and the method comprises the steps: obtaining remote sensing image data, carrying out the prior information mining processing of contour extraction on the remote sensing image data, obtaining the contour extraction prior information corresponding to the remote sensing image data, and obtaining the contour extraction prior information of the remote sensing image data; wherein the contour extraction prior information comprises local control prior information and global control prior information; inputting the remote sensing image data and the contour extraction prior information into a building contour extraction reasoning model, and performing iterative reconstruction processing on the remote sensing image data in the building contour extraction reasoning model by taking the contour extraction prior information as coordinated guidance, and outputting the accurate contour mask corresponding to the building in the remote sensing image data. In the embodiment of the invention, remarkable beneficial effects are achieved in the aspects of geometric accuracy, structural integrity and complex scene adaptability of the building contour.

The embodiment of the invention discloses an energy spectrum CT image reconstruction method and a storage medium. The method comprises the following steps: in the current iterative reconstruction process of an energy spectrum CT image, for each energy spectrum, determining transformation projection data corresponding to the energy spectrum, substituting the transformation projection data into a sub-problem pre-constructed for the energy spectrum, and solving the sub-problem after substitution based on a first constraint preset for the sub-problem, the transformation base image meets the first constraint, and the error between the transformation base image and the transformation projection data is minimum; and combining the transformation base images corresponding to the energy spectrums to obtain a combined base image, and substituting the combined base image into a pre-constructed optimization problem to reconstruct a current base image with a minimum error with the combined base image. According to the technical scheme provided by the embodiment of the invention, the energy spectrum CT image can be efficiently and accurately reconstructed, and particularly, the energy spectrum CT image can be efficiently and accurately reconstructed under a low-dose condition and / or a condition that scanning geometric parameters are inconsistent under different energy spectrums.

The invention relates to a high-precision multi-surface wavelength tuning interference measurement signal processing method. The core of the method is to solve the problem of dissociation of superposed interference signals and overcome the frequency spectrum fence effect. The patent process comprises the following steps: constructing a dissociation framework based on an iteration vector and a time delay virtual detection point; optimizing an iteration process by using an approximate Newton method; designing a double judgment dissociation criterion and orthogonally outputting a dissociation result; performing frequency spectrum fine segmentation analysis and phase demodulation function extraction on the dissociation signal to realize high-precision solution of harmonic frequency and phase; and the dissociation quality can be evaluated by using a correlation coefficient quantification method. The method can effectively separate multi-surface interference superposition signals and accurately reconstruct key parameters, is obviously superior to a traditional method (such as FFT), and improves the performance by about two orders of magnitude. Experiments prove that the surface shape error is controlled to be 10e <-3 > magnitude, the frequency precision reaches 10e <-4 > magnitude, and the method has excellent robustness and wide applicability and is suitable for high-precision optical element detection.

The present invention provides an iterative reconstruction method based on adaptive moment estimation FPM, including S1: Under a low-magnification objective lens, using an LED array as a light source, illuminating the sample by sequentially lighting the LED units in the LED array, and collecting a series of low-resolution intensity images of the sample corresponding to the LED positions; S2: Using each low-resolution intensity image to iteratively update the sub-aperture spectrum information corresponding to the LED positions in the support constraint domain in the matching frequency domain, and completing the high-resolution image restoration based on the adaptive moment estimation-based ptychographic reconstruction framework. The present invention realizes faster and more stable image iterative reconstruction of high-quality adaptive FP in a noise environment by using adaptive moment estimation.

The invention belongs to the technical field of communication, and discloses a communication method and system based on broadband interference suppression and target signal reconstruction, and the method comprises the steps: collecting an original broadband communication signal; carrying out compressed sampling on the preprocessed signal to obtain compressed data; performing time-frequency domain conversion on the compressed data, and performing sparse representation on the signal by using the sparse dictionary to obtain time-frequency domain conversion data; reconstructing an original broadband communication signal from the time-frequency domain conversion data by adopting an iterative reconstruction algorithm and combining an interference suppression strategy, and eliminating an interference component to obtain a target signal; and comparing the quality index of the reconstructed target signal with a preset performance index, and dynamically optimizing at least one key parameter of compressed sampling, sparse representation, iterative reconstruction or interference suppression according to a comparison result. According to the method, a complex and changeable interference environment can be adaptively dealt with, it is ensured that the system always keeps the optimal processing effect, and the robustness is far better than that of a traditional method adopting fixed parameters.

The invention relates to the technical field of nuclear medicine diagnosis, and particularly provides an ROI image reconstruction method and system based on SPECT data, and the method comprises the steps: reconstructing a coarse scanning image according to large-view coarse scanning projection data; generating ROI image estimation according to the coarse scanning image; performing loop iteration updating on the ROI image estimation until a preset convergence condition is met; the loop iteration updating process comprises the following steps: generating estimated projection data according to ROI image estimation of current iteration; obtaining projection deviation according to the estimated projection data and the small-view fine scanning projection data; obtaining an image state index according to ROI image estimation of the current iteration and the previous iteration; determining a coarse scanning data contribution weight according to the image state index; updating the ROI image estimation of the current iteration according to the projection deviation, the small-view fine scanning projection data, the coarse scanning data contribution weight and the large-view coarse scanning projection data; according to the method, the iterative reconstruction process can adapt to the dynamic change of the image state.

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 knowledge base construction method and system based on urban planning implicit knowledge deduction, relates to the technical field of urban planning, and aims to solve the problems that an existing planning knowledge base is insufficient in implicit knowledge mining, poor in scene adaptability and lack of dynamic optimization capability. The method comprises the steps of obtaining multi-source data and performing standardization processing to obtain an enhanced data pool; optimizing the large language model to extract explicit and implicit planning knowledge; constructing a planning knowledge association network with association strength; building an initial knowledge base containing classification, retrieval and implicit knowledge deduction units; generating an adaptability evaluation result through a multi-scene test; constructing a knowledge evolution rule based on an evaluation result and completing knowledge base iterative reconstruction; and establishing a real-time spatial data dynamic triggering mechanism to realize continuous optimization of the knowledge base. According to the method, accurate association and deduction of explicit and implicit knowledge are realized, the scene adaptability and dynamic updating capability of the knowledge base are improved, scientific and efficient knowledge support is provided for urban planning decision making, and the method is suitable for various planning scenes.

A medical image reconstruction method includes: obtaining real projection data collected by a medical imaging device; obtaining an inverse matrix corresponding to the medical imaging device that is physically stored in advance, in which the inverse matrix is obtained by performing an inverse operation based on a system matrix corresponding to the medical imaging device, and the system matrix indicating a geometrical relationship between projection rays of the medical imaging device at each view angle and of reconstructed image pixels; and determining a reconstructed image of the real projection data according to the real projection data and the inverse matrix. By storing a large inverse matrix of the system matrix, rapid image reconstruction may be achieved through the inverse matrix and the real projection data, reconstruction efficiency of a medical image may be improved, and wide application and development of iterative reconstruction technology in clinical practice may be promoted.

The invention relates to a Go-pos scene self-adaptive remote sensing image super-resolution reconstruction system and method, relates to the technical field of space optics, and solves the technical problems that when a traditional algorithm faces a remote sensing image with a complex landform, super-resolution reconstruction fails, the image edge reconstruction effect is poor, initial frame construction is incomplete, and subsequent iteration cannot be supported. The system comprises an input image loading module, a scene recognition module, a degradation model configuration module, an initial reconstruction NEDI module, a POCS iteration reconstruction module, an edge enhancement module and an output and storage module. According to the Go-pos scene adaptive remote sensing image super-resolution reconstruction system and method provided by the invention, a set of remote sensing image super-resolution reconstruction scheme with strong robustness, wide adaptability and excellent detail reconstruction effect is constructed through combination of scene recognition driven adaptive degradation modeling and an edge-oriented reconstruction strategy; and particularly, the method shows higher universality and reconstruction quality in diversified landform scenes.

The present disclosure provides a cone beam computed tomography system for proton therapy comprising an x-ray source configured to generate x-rays and positioned to rotate around a patient at a treatment isocenter, a detector assembly positioned opposite the x-ray source and configured to receive x-rays that have passed through the patient, a rotation mechanism configured to rotate the x-ray source and the detector assembly around the patient while maintaining their relative positions, a translation mechanism configured to translate at least one of the x-ray source, the detector assembly, or a patient couch during rotation to implement a wobbled scan orbit, and a scatter rejection grid attached to the detector assembly and focused on the x-ray source to reduce scattered x-rays. The translation mechanism distributes missing projection data across multiple radii and angles, enabling improved reconstruction quality through iterative reconstruction methods.

The present invention discloses a multimodal super-resolution quantitative phase microscopy method. The present invention uses a programmable LED array as an illumination light source, employs an objective lens with a low numerical aperture to obtain a stack of collected light intensity maps in multiple modalities, and uses the calculated light intensity maps as intensity constraints to perform multiple iterative updates, thereby achieving super-resolution quantitative phase microscopy. By virtue of the low numerical aperture objective lens and the programmable LED array, the present invention reduces the requirement for high-coherence illumination of the imaging system, and uses the idea of iterative reconstruction to complete phase recovery, thereby achieving super-resolution quantitative phase microscopy, which has the advantages of high resolution, high signal-to-noise ratio, and large field of view.

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 application discloses a CT image iterative reconstruction method and system based on maximum residual extension projection. The application collects CT original projection data, constructs a linear equation group model, disassembles a reconstruction sub-problem, iteratively selects a work row and column with the maximum residual after initialization of parameters, adopts a phased differentiated extension projection strategy combined with an adaptive step length update solution vector, terminates iteration in combination with multiple convergence criteria, and finally reorganizes a reconstructed image. Meanwhile, optimization strategies such as projection data extension, extended power divergence optimization, multi-domain network constraint and residual prior regularization are configured to adapt to complex working conditions such as truncation, noise, sparsity and limited angle. The application also builds a modular closed-loop reconstruction system to realize full-process automatic operation. The application can accurately correct reconstruction errors, accelerate the iterative convergence speed, effectively suppress various types of artifacts, improve imaging accuracy and robustness, does not require a large amount of training data, is suitable for multiple CT imaging scenes, and has high engineering promotion value.

The invention discloses a hyperspectral image reconstruction system based on a binary neural network, and the system comprises a binary neural network model used for reconstructing a hyperspectral image, and the binary neural network model comprises a degeneration parameter estimator which is used for estimating degeneration degree information based on a measurement value, and outputting K groups of degeneration parameters; k is a positive integer; the initialization extractor is used for extracting full-precision information from the measured value to obtain an initial hyperspectral reconstruction signal; and the K iterative reconstruction modules are connected to the output ends of the degradation parameter estimator and the initialization extractor and are used for reconstructing a hyperspectral image according to the K groups of degradation parameters and the initial hyperspectral reconstruction signal.

The invention relates to a lunar surface three-dimensional terrain variation iteration reconstruction method. The method comprises the following steps: converting initial terrain and laser point data of a lunar surface into a satellite-fixed rectangular coordinate system; projecting remote sensing image data to the surface of the initial terrain; constructing a physical reflection model, and establishing a functional of terrain and image luminosity by using the physical reflection model; constructing a cost function; and the cost function is minimized through numerical iteration, and the lunar surface three-dimensional terrain fused with the laser and the image is reconstructed. Compared with the prior art, the method has the advantages of good reconstruction effect and the like.

The invention relates to a magnetic resonance image processing method and device, and an EPI-based image processing method. The method comprises the steps of obtaining a first sampling image of a target object according to sensitivity information of a receiving coil corresponding to the target object and K space data; correcting artifacts in the first sampling image to obtain a second sampling image of the target object; and iterating data of a preset first magnetic resonance image by taking the second sampling image as a constraint condition and taking the K space data as a fidelity term in an iterative reconstruction process to obtain a magnetic resonance image of the target object. By adopting the method, the problem of aliasing artifacts existing in magnetic resonance imaging images can be solved.

The invention provides an iterative reconstruction method and system for internal tomography. An X-ray microscopic tomography system provides the ability to strictly determine regularization parameters for iterative reconstruction of a sample from projection data of the sample. This allows less experienced operators to determine regularization parameters with sufficient accuracy.

The invention relates to the technical field of data processing, and further relates to a data extraction method and system for digital geological mineral exploration. Comprising the following steps: step 1, performing analog-to-digital conversion on multiple paths of original magnetic field response voltage signals synchronously acquired by multiple optical pump atomic magnetometer sensing units to obtain multiple paths of original magnetic field response digital sequences; 2, performing phase gradient field iteration reconstruction processing on the magnetic field in-phase response matrix and the magnetic field orthogonal response matrix to obtain a reconstructed phase matrix and a magnetic field amplitude matrix; and 3, calculating a magnetic field amplitude deviation matrix and a phase deviation matrix according to the magnetic field amplitude matrix and the reconstructed phase matrix, converting the mineral anomaly candidate position into a geological space coordinate, and outputting geological mineral anomaly feature data.

State of the art systems being used for QSM reconstruction have explored prior information from both magnitude and phase data. However, the underlying assumption is that the susceptibility maps and the magnitude images have coinciding edges. Establishing the ground-truth susceptibility maps is difficult and leads to limited applicability of supervised methods. Further, with portable MRI machines becoming a reality, low-field imaging is getting more prominence, which brings in several associated challenges due to noise and external interference. The disclosure herein generally relates to magnetic resonance imaging (MRI) imaging systems, and, more particularly, to a method and system for quantitative susceptibility mapping (QSM) reconstruction in magnetic resonance imaging (MRI) systems. The system performs an iterative reconstruction of QSM, wherein in each iteration the reconstructed QSM from previous iteration is refined by comparing with a reference image generated using same subject's prior MRI data.

The present application relates to the technical field of spectral CT imaging, and particularly relates to a physical parameter-based material decomposition method and device, electronic equipment and medium, the method comprising: calculating at least two groups of different equivalent energy CT projection correction parameters and spectral correction parameters based on known phantom spectral CT pre-scan data and initial equivalent spectrum; correcting a material decomposition model according to the parameters to establish a mapping relationship between CT projection data and base material thickness under corresponding equivalent energy. The corresponding projection data of the to-be-measured phantom is corrected using the projection correction parameters, and the base material thickness data is obtained in combination with the mapping relationship; the base material image is generated through analysis or iterative reconstruction, and the virtual single-energy image is obtained through linear combination, thereby solving the problem of low spectral CT material quantitative analysis precision in the related art and improving the precision of spectral CT material quantitative analysis.

Systems, methods, and apparatuses for image reconstruction. One computer-implemented method includes receiving measurement data representing a subject and generating an image estimate of the subject based on the measurement data. The method also includes refining the image estimate by performing an iterative reconstruction process comprising a plurality of iteration steps, each iteration step comprising (i) computing a data consistency term based on the image estimate and the measurement data, (ii) generating a correction term using a machine learning model based on the image estimate, the data consistency term, the measurement data, and cross-iteration information, and (iii) updating the image estimate based on the correction term, the data consistency term, and a global learning rate corresponding to the iteration step.

This invention relates to the field of medical image reconstruction technology, specifically to a method for reconstructing low-dose CT images of patients with coronary artery disease based on image enhancement. The method includes: acquiring multi-angle raw projection data of low-dose CT images from patients with coronary artery disease; performing statistical noise modeling and signal recovery on the projection data to obtain denoised projection data; generating an initial coronary CT image sequence through an iterative reconstruction algorithm; extracting cardiac structures and completing region segmentation to obtain labeled images; performing region-specific differential image enhancement based on the labeled images; enhancing vessel wall details and extracting calcified plaque features; combining edge-preserving noise suppression with post-optimization; and outputting high-quality low-dose CT reconstructed images. This method effectively improves the noise artifact problem caused by low-dose scanning, preserves key anatomical details and lesion features of the coronary arteries and myocardium, and enhances the clinical diagnostic practicality of low-dose CT images for coronary artery disease.