33 results about "Filter back projection" patented technology

The invention relates to the technical field of CT detection, and discloses a plane CT fan-beam projection filtering back projection reconstruction method and CT detection device.The method comprises the steps that S1, tilt geometric configuration parameters are determined according to the distance from a detector to a rotation center and a tilt angle, a sample rotating table is controlled to rotate, and the detector is triggered at each rotation angle to collect first projection data; s2, calculating a global three-dimensional coordinate of a detector pixel point under each rotation angle, and generating second projection data; s3, performing frequency domain filtering according to the thickness of the sample and the second projection data to obtain third projection data; and S4, calculating a ray intersection point coordinate from a reconstructed slice pixel point to a detector plane, and reconstructing the third projection data according to the ray intersection point coordinate to obtain a tomographic reconstruction image.According to the method, high-frequency detail features are reserved while noise is suppressed, the problem that a traditional fixed filtering kernel parameter is poor in adaptability to different samples is avoided, and the method is suitable for being applied to the field of image reconstruction. Therefore, high-precision three-dimensional imaging is realized.

The invention relates to the technical field of PCB (Printed Circuit Board) blind hole defect detection, and discloses an X-ray-based PCB blind hole defect detection method, which comprises the following steps of: irradiating a PCB by using X rays, performing data acquisition by using CT (Computed Tomography) projection, establishing an initial image, reconstructing the initial image by using filtered back projection to obtain a reconstructed image, and detecting the blind hole defect of the PCB according to the reconstructed image. According to the blind hole defect detection method based on the deep residual network, through the double-flow neural network architecture constructed on the basis of the deep residual network, geometric features and material features are comprehensively utilized, the blind hole defect detection accuracy is improved, the blind hole defect detection accuracy is improved, and the blind hole defect detection accuracy is improved. The PCB blind hole defect detection device can comprehensively and accurately detect and classify various defects of PCB blind holes, such as cracks, wrinkles, recesses and filling holes, improves the efficiency and accuracy of defect detection, is helpful for timely finding quality problems in the PCB production process, and guarantees the product quality.

The invention discloses a DDPM-based CT image metal artifact elimination method, and aims to solve the artifact problem caused by a metal object in an existing CT image and improve image quality and diagnosis reliability. A diffusion model of unconditional training is adopted, step-by-step back diffusion repair of an artifact area is carried out in a sinogram domain, an unrepaired area is dynamically adjusted by combining with a metal mask, accurate repair of the artifact area is achieved, and original data of the area which is not affected by artifacts are kept. In the training stage of the system, artifact-free data are gradually converted into standard Gaussian noise through forward diffusion; in the inference stage, data are gradually recovered by utilizing back diffusion, block repair is carried out on an artifact region by combining with a metal mask, and a complete sinogram is generated through region merging. And finally, reconstructing a CT image by using a filtered back projection algorithm, and optimizing boundary transition through a smoothing algorithm to ensure seamless connection between the metal object and surrounding tissues.

According to the analysis and reconstruction method and system of the static CT based on the arc-shaped detector and the arc-shaped ray source, the analysis and reconstruction method of the static CT based on the arc-shaped detector and the arc-shaped ray source is carried out through the following five steps, and finally a final CT image is obtained. The method has the beneficial effects that in a filtering back projection algorithm, a variable optimization method of geometric parameter transformation and traditional equiangular fan beam reconstruction is introduced, an accurate arc-shaped geometric scanning model is established, and an analysis reconstruction algorithm of linear multi-source static CT is referred to; meanwhile, the influence of the geometric structure characteristics of the arc ray source-arc detector and the projection data completeness on the reconstruction effect is considered, so that stripe artifacts and motion artifacts can be effectively reduced, and the CT image quality is improved. Meanwhile, the method keeps a frame of filtering back projection, and is low in calculation complexity and rapid in reconstruction.

Disclosed in the present invention are a weighted analytic filtered back projection reconstruction method and system for asymmetric cone angle artifacts. The method comprises the following steps: dividing a reconstruction area into a plurality of weight regions on the basis of relative positions of a ray source ring and a detector ring; acquiring the projection data volume of voxel points in each weight area irradiated by X-rays; according to the projection data volume of the voxel points in each weight area irradiated by the X-rays, assigning a different initial weight to each weight area; performing smooth transition on the initial weight of each weight area by means of a transition weight to form a final weight assigned to each weight area; and according to different final weights of the weight regions, performing final weighted analytic reconstruction on projection data p (α, β, γ) to acquire a back projection image.

The present invention belongs to the field of magnetic particle imaging technology, and specifically relates to a magnetic particle imaging system and method based on a flexible receiving coil array, which aims to solve the problems of poor adaptability of current rigid receiving coils to biological surfaces and low signal-to-noise ratio for imaging tiny geometric textures. The present invention: Structurally, the receiving coil array is adhered to the surface of the object to be measured, and the compensation coil array is distributed on both sides of the flexible substrate. The receiving coil array is used to receive magnetic nanoparticle signals, and the block-type small compensation coil array is used to compensate for feedthrough interference and electromagnetic interference of the receiving coil; in terms of reconstruction method, the collected receiving signal is Fourier transformed in sequence, the in-phase third harmonic is extracted and superimposed to obtain the processed signal, interpolation is used to construct a sinusoidal diagram for the Cartesian coordinate distribution, and the filtered back projection method is used to reconstruct the three-dimensional particle concentration distribution of the object to be measured. The present invention adopts a flexible receiving coil array strategy and provides an adaptive reconstruction method, thereby improving the imaging signal-to-noise ratio.

The invention discloses a method and a system for removing breathing artifacts of an animal micro-CT image. The method comprises the following steps: preprocessing projection data of an animal to obtain label information; training label information by using a depth target detection network to obtain thoracic cavity diaphragm positions, classifying the thoracic cavity diaphragm positions, extracting target limiting projections at the end of diastole and the end of systole, and performing unequal interval filtering back projection reconstruction on the target limiting projections to obtain a breathing artifact-removed image; correcting the breathing artifact-removed image by using an improved deep noise reduction network to obtain a breathing and stripe artifact-removed image; in-phase projection is extracted in a projection domain and is reconstructed, so that respiratory artifacts are effectively removed from the source; and the improved deep noise reduction network is used to enhance the quality of the sparse reconstruction image, so that the image quality is remarkably improved, and a clearer and more accurate image basis is provided.

The application discloses a kind of based on the sparse helical CT reconstruction method, system, equipment, medium and program product of back projection tensor interpolation, belong to computer tomography (CT) imaging technical field, specifically includes the following steps: generating the back projection tensor data under sparse angle, the back projection tensor data indicates the intermediate data of back projection process in filter back projection algorithm;Interpolation is carried out to the back projection tensor data of the sparse angle by depth learning network, generates the back projection tensor data of full angle;Image reconstruction is carried out based on the back projection tensor data of full angle, and the final CT image is obtained.The reconstruction method of the application suppresses the generation of strip artifact from the source and directly repairs the key intermediate data for reconstruction, which can better retain and restore the true anatomical structure information than post-processing in the image domain, avoids excessive smoothing or filtering, and has generalization ability, suitable for different CT scanning geometry.

The application discloses a small animal in-vivo multi-parameter dynamics monitoring method based on photoacoustic imaging and relates to the technical field of biomedical imaging, which comprises the following steps: obtaining photoacoustic signals generated by a small animal under the excitation of pulsed laser output by an optical excitation module through an acoustic detection module; reconstructing photoacoustic images by filtering back projection algorithm on the photoacoustic signals; calculating the similarity between adjacent images in the photoacoustic images, identifying the images with similarity lower than a set frame threshold as cross-sectional slices contaminated by respiratory motion artifacts and capable of being removed, using interpolation of adjacent non-artifact images to replace the removed image frames, thereby obtaining updated photoacoustic images; selecting a region of interest in the photoacoustic images to monitor oxygenation dynamics, pharmacokinetics, perfusion dynamics or in-vivo dynamics of nanoparticles; the monitoring method effectively suppresses motion artifacts caused by small animal respiration and realizes in-vivo multi-parameter dynamics monitoring of small animals.

The invention discloses a scattering and hardening artifact eliminating method, system, device and equipment for turbine blade CT imaging and a medium, and relates to the field of aero-engine turbine blade CT detection. The method comprises the following steps: acquiring an original multicolor projection image and a scattering correction projection image of the turbine blade before and after placing a grating correction plate by using a cone beam CT (Computed Tomography) system; the aluminum strips arranged on the grating correction plate at intervals have inclined angles; according to the two images, determining a multi-color projection image for scattering removal of the turbine blade, and further utilizing a beam hardening correction model to obtain a single-color projection image of the turbine blade; weighting the monochromatic projection image and the scattering-removed multicolor projection image by adopting a weighted compensation enhancement correction algorithm which introduces a trade-off factor to obtain a projection image of which the cup artifact is eliminated; and finally, performing three-dimensional reconstruction by adopting a filtered back projection algorithm to obtain a projection correction slice image of the turbine blade. The method can effectively eliminate cup-shaped artifacts caused by scattering and hardening, and improves the image reconstruction quality.

The application discloses a deep learning-based limited-angle projection phase contrast CT image reconstruction method and device, which comprises the following steps: acquiring limited-angle projection data of X-ray phase contrast CT scanning through an equal-angle increment strategy; performing flat-field correction and dark-field correction preprocessing on the limited-angle projection data to obtain limited-angle projection data before phase recovery; performing preprocessing on the limited-angle projection data through a phase recovery strategy to obtain limited-angle projection data after phase recovery; performing CT reconstruction on the limited-angle projection data before and after phase recovery through a filtered back projection strategy to obtain CT images of the limited-angle projection before and after phase recovery; constructing a phase contrast CT intelligent image reconstruction network model and training; and performing reconstruction processing on the CT images of the limited-angle projection before and after phase recovery through the trained phase contrast CT intelligent image reconstruction network model to obtain reconstructed phase contrast CT images. The application can quickly and efficiently perform phase contrast CT image reconstruction, effectively suppress limited-angle reconstruction artifacts and improve image quality.

The application provides a four-dimensional computer tomography hybrid imaging method, which can be applied to the technical field of medical imaging and other technologies requiring X-ray CT imaging. The method comprises the following steps: performing image reconstruction on projections of a target object obtained through four-dimensional computer tomography by using a filtered back projection algorithm to obtain a first reconstructed image; performing image reconstruction on the first reconstructed image based on an error minimization algorithm by using a trained hybrid imaging neural network to obtain a second reconstructed image, and performing elastic registration and motion compensation between the second reconstructed images to obtain a third reconstructed image; and performing iterative image reconstruction based on bone weighting on the third reconstructed image to obtain a four-dimensional computer tomography hybrid imaging result of the target object. The application also provides a four-dimensional computer tomography hybrid imaging device, an electronic device and a storage medium.

The invention relates to the technical field of image recognition, in particular to an intelligent garbage classification system based on X-ray transmission and deep learning. A scanning module of the system adopts multi-angle dual-energy X-rays to obtain high-energy and low-energy projection data, and reconstructs a cross section attenuation image through a filtering back projection algorithm; the image processing module calculates an effective atomic number of a pixel point by using a dual-energy imaging algorithm, and performs mapping based on a color model so as to realize material distinguishing of the organic matter, the mixture and the inorganic matter; the garbage identification module inputs the mapping transmission image into a deep learning model, completes identification of specific categories of metal, plastic and the like, and outputs a classification instruction in combination with a garbage classification library; and the garbage sorting unit moves the garbage carrier to a collection area according to the instruction. The data management unit stores the transmission image and the classification result, and continuously optimizes the recognition model through incremental learning. According to the invention, transmission type identification and intelligent classification of garbage can be realized, and the classification precision and the system stability are improved.

The invention relates to an image artifact correction method, system and device, computer equipment and a storage medium. The method comprises the following steps: acquiring target scanning data corresponding to a region of interest; the target scanning data comprises data in a reconstruction view field range and data in a scanning view field range; performing image reconstruction on the region of interest according to the data in the reconstruction view field range to obtain a target reconstruction scanning image; and for an area between the scanning visual field range and the reconstruction visual field range, if the tissue scanning value is smaller than a preset tissue threshold value, performing scleroossification correction on the target reconstruction scanning image to obtain a target correction scanning image. By the adoption of the method, image reconstruction and osteosclerosis correction can be combined, under the condition that it is judged that no object (such as bone tissue) exists outside the target visual field, the image is reconstructed and osteosclerosis correction is conducted directly through data in the current scanning visual field range, the time of one-time filtering back projection can be saved, and the efficiency of image reconstruction and osteosclerosis correction is improved. And the image reconstruction efficiency of the region of interest is improved.

The invention discloses an SPECT low-dose dynamic reconstruction method based on a deformable convolution and deep expansion network, and relates to the technical field of data processing, and the method comprises the following steps: carrying out the normalization of original projection data, and carrying out the grouping according to a time window; generating an initial image sequence by using a filtered back projection algorithm; constructing a deep expansion network framework comprising a plurality of iteration layers; calculating a projection residual error in each layer and updating the image to match measured data; performing spatial denoising and feature enhancement on the image by using a convolutional neural network; learning inter-frame motion through deformable convolution and carrying out time alignment on features; repeating the processing steps in the network for a plurality of times; and performing cutting and intensity scaling on the final output image to generate a final result. According to the method, the deformable time convolution module is integrated into each iteration layer of the deep expansion network, so that modeling can be effectively carried out, and non-rigid motion in a dynamic sequence can be compensated, and therefore, the space-time consistency and quality of a reconstructed image sequence can be improved.

This invention relates to a high-intensity pulsed X-ray beam monitoring device and method, applicable to flash therapy. The high-intensity pulsed X-ray beam monitoring device of this invention uses a conversion target to convert a high-intensity pulsed X-ray beam into low-intensity electron-positron annihilation gamma rays. A ring detector array is set around the conversion target to capture the electron-positron annihilation gamma rays and calculate and determine their emission positions. The intensity of the X-ray beam under test is estimated by the intensity of the electron-positron annihilation gamma rays and the conversion efficiency of the conversion target; valid coincidence events are screened, and the coordinates of the annihilation positions are reconstructed using filtered back projection based on the signals of multiple coincidence events, obtaining the shape distribution of the X-ray beam and the intensity distribution of the beam interface.

The invention discloses a method for cooperatively distinguishing pores and bubbles in ultra-stable foam based on FC and sphericity analysis, and belongs to the field of solid-liquid separation of flotation clean coal, and the method comprises the following steps: obtaining a three-dimensional grayscale image of the foam through Micro-CT, performing three-dimensional reconstruction according to a filtered back projection algorithm, and extracting a gap structure; determining a representative sub-volume based on the change in porosity to ensure statistical reliability; adopting a Feret Caliper method to calculate geometrical characteristic parameters of the gap and performing morphological classification; determining a distinguishing threshold TH by combining the relationship between the sphericity and the bubble volume fraction; and automatically identifying and distinguishing the bubbles and the pores according to the threshold value. And finally verifying by using an optical microscope result. According to the method, non-destructive recognition and quantitative analysis of the internal structure of the ultra-stable foam are achieved, high precision and repeatability are achieved, and a reliable image quantification means is provided for optimization of a coal dehydration process.

This invention provides an echo generation method based on single-frequency MIMO arc scanning. Using simulated single-frequency MIMO arc scanning data as input, the method employs a filtered back projection (BP) algorithm to generate three-dimensional images at different attitudes. Scattering center information from the three-dimensional images is extracted to form a list of all-attitude three-dimensional scattering centers. Echoes are synthesized by weighted superposition of scattering center contributions based on the geometric relationship between the radar and the scattering centers, combined with directivity patterns. This method significantly reduces the amount of broadband data required by traditional imaging methods, improves the modeling efficiency of single-frequency input, and can adapt to echo simulations for arbitrary paths and a large number of attitudes, significantly enhancing echo simulation efficiency.

The invention relates to a DT labeled neutron source transmission CT imaging detection system and method.The DT labeled neutron source transmission CT imaging detection system comprises a neutron source device, a sample table, a ray detector and a data acquisition and analysis system.The neutron source device is a DT neutron source, and the ray detector comprises a neutron detector array and an associated alpha array detector; the neutron detector array and the associated alpha array detector are respectively arranged in two opposite directions of the neutron source device, the sample table is arranged between the neutron source device and the neutron detector array, and the neutron detector array and the associated alpha array detector are respectively connected with the data acquisition and analysis system. According to the method, the transmission neutron counting rate of each neutron detector unit is obtained by using a flight time method according to time-space correlation characteristics of neutrons and alpha, a high-signal-to-noise-ratio transmission neutron image is obtained by using a tomography mode, and a three-dimensional image structure of a detected sample is obtained by using a filtered back projection algorithm.

The invention relates to the technical field of CT imaging, in particular to a CT hardening artifact correction method based on index and power function transformation. The method comprises the following steps: scanning under normal imaging tube voltage of an object to obtain an X-ray image, and calculating to generate a multi-energy projection; substituting the multi-energy projection into a core transformation formula, and outputting a corrected projection; and finally, reconstructing an image for the corrected projection by adopting a filtering back projection or an iterative algorithm, and weakening a hardening artifact of the reconstructed image. A titanium alloy coated steel sample verifies that a titanium alloy area in a conventional reconstructed image is significant in gray difference, hardening artifacts are weakened after correction, a gray curve is more uniform, and the quantitative analysis accuracy of materials is improved. The problem of hardening artifacts caused by multi-energy-spectrum X-rays is solved through index and power function transformation, the traditional multi-step correction process is simplified, the number of traditional correction model parameters is reduced, and the method is suitable for industrial CT detection scenes.

The invention discloses an axial flow fan intelligent maintenance system based on three-dimensional visualization, and relates to the field of intelligent maintenance, and the system comprises an acquisition module, an abnormity identification module and an auxiliary maintenance module; the acquisition module periodically acquires a signal set, and generates an equipment feature set and a scanning image set through a vector analysis method and a frequency domain filtering back projection method; the anomaly identification module identifies a state type by using an integrated decision network, generates a mark scanning image set based on a regional gray analysis method and decides to send maintenance data; the auxiliary maintenance module generates a maintenance marking model and a dismounting scheme through a reconstruction correction method and an ant colony algorithm, can accurately detect abnormity and provide visual auxiliary maintenance, and improves the efficiency and accuracy.

This invention relates to the field of PCB blind via defect detection technology and discloses an X-ray-based method for detecting PCB blind via defects. The method includes the following steps: first, the PCB is irradiated with X-rays; then, data is acquired and an initial image is established using CT projection; subsequently, the initial image is reconstructed using filtered back projection to obtain a reconstructed image; then, the blind via region is extracted using the reconstructed image; next, a two-stream neural network architecture is constructed based on a deep residual network; finally, the blind via defect is detected and classified using the two-stream neural network architecture. This invention, through a two-stream neural network architecture constructed based on a deep residual network, comprehensively utilizes geometric and material features, enabling comprehensive and accurate detection and classification of various defects in PCB blind vias, such as cracks, wrinkles, dents, and voids. This improves the efficiency and accuracy of defect detection, helps to promptly identify quality problems in the PCB manufacturing process, and ensures product quality.

The present invention discloses a tomographic reconstruction method and device based on dual-plane long-stroke scanning, which is applied to a dual-plane X-ray machine system. The method comprises the following steps: setting a dual-plane X-ray scanning mode and a geometric environment to obtain original images of an object to be reconstructed in different relative position relationships; preprocessing the original image to obtain grayscale values ​​of the original image, performing logarithmic transformation to convert the grayscale values ​​into corresponding X-ray absorptivity, and determining a tomographic reconstruction range according to the long-stroke scanning geometric environment; selecting a reconstruction width, traversing all reconstruction range points with the reconstruction width, and completing reconstruction of coronal and sagittal tomographic images by filtered back projection; obtaining overlapping parts of the reconstructed tomographic images, and splicing the overlapping parts using a weighted superposition method to form a long-stroke image, thereby realizing a tomographic reconstruction algorithm for dual-plane long-stroke scanning, realizing reconstruction of coronal and sagittal weighted-position tomographic images, expanding the tomographic image reconstruction range, and improving the image inter-layer resolution.

The invention relates to the technical field of computed tomography imaging, in particular to a CT reconstruction method and system based on data rearrangement and deep learning angle extrapolation, and the method comprises the steps: data rearrangement: enabling an actual ray source sampling point to be opposite to a detector pixel, constructing virtual scanning geometry, and recombining a truncation projection set into global projection; deep learning angle extrapolation: inputting the rearranged limited angle global projection into a physical perception Transform network, predicting to obtain projection data of a missing angle, and forming a complete full-angle projection set; image reconstruction: carrying out filtering back projection reconstruction on the complete full-angle projection set to obtain an initial reconstruction image; and image post-processing: inputting the initial reconstructed image into a double-domain iterative optimization network, and realizing end-to-end cooperative training through projection domain-image domain joint loss. According to the method, the problems of artifacts and structural distortion generated when an image is reconstructed under the conditions of limited angles and cut-off projection in a traditional method are effectively solved, and the imaging quality and reliability of an STCT system are remarkably improved.

The invention relates to the technical field of medical image processing, and discloses a sparse CT (Computed Tomography) reconstruction method fusing channel weighting and a residual dense network, which comprises the following steps of: performing filtering back projection and re-projection operation on an original sparse sinogram to generate a re-projected full-size sinogram; constructing a residual dense network based on channel fusion; inputting the full-size sinogram into the network for training, introducing an improved joint loss function for optimization, and generating a trained network by learning a recovered sinogram and a reference sinogram and recovering an error between a sinogram filtering back projection reconstructed image and a reference sinogram filtering back projection reconstructed image; inputting the acquired sparse sinogram to carry out projection recovery, and after generating a recovered sinogram, carrying out filtering back projection to generate an artifact-removed CT image; according to the method, the precision of the reconstructed image is improved, missing or inaccurate projection information in the original sparse projection is effectively complemented, and the edge definition and the organization structure integrity of the reconstructed image are remarkably improved.

A kind of detection method of different density materials in cigarette based on three-dimensional reconstruction of industrial CT, characterized by: through industrial CT scanning, realize the digitization of three-dimensional structure of cigarette, obtain the two-dimensional image of tobacco distribution inside cigarette through filtered back projection technology, then obtain the three-dimensional reconstruction model of cigarette through three-dimensional reconstruction technology, since X-ray will attenuate when penetrating through object, the attenuation degree of ray of four different materials, tobacco, cut stem, expanded tobacco and expanded cut stem is different, which is reflected in the different gray values in three-dimensional reconstruction model, so the tobacco, cut stem, expanded tobacco and expanded cut stem in cigarette sample can be identified based on gray value.The advantages of the present application are that the tobacco, cut stem, expanded tobacco and expanded cut stem in cigarette can be automatically and accurately detected, with high detection efficiency and low error rate, and the method of the present application belongs to non-destructive testing technology, which can effectively reduce the loss of cigarette and has important significance for the development of the industry.

The invention discloses a CT image noise reduction method and system based on projection domain statistical feature guidance. The method comprises the steps that a noisy CT image obtained through filtering back projection reconstruction and projection data corresponding to the noisy CT image are obtained; a preset projection noise statistical model is combined to generate a noise variance contribution graph aligned with the noisy CT image space; constructing a composite similarity measurement criterion based on the noisy CT image and the noise variance contribution map; and based on a composite similarity measurement criterion, performing non-local mean noise reduction processing on the noisy CT image to obtain a denoised CT image. A composite similarity measurement criterion is constructed by introducing a noise variance contribution graph aligned with a noisy CT image space, and a non-local mean noise reduction process is guided, so that the retention capability of a real anatomical structure is remarkably improved and the robustness of artifacts is enhanced while signal dependent quantum noise is effectively suppressed.

The invention discloses a CT (Computed Tomography) image hybrid filtering optimization method, which comprises the following steps of: 1) reconstructing projection data of a sample by using a filtering back projection algorithm to obtain an initial CT image of the sample; (2) detecting the initial CT image, determining the position of a ray penetrating through the target structure in projection data, and carrying out classified filtering on the projection data to obtain a CT reconstruction image after artifacts are removed; 3) performing coordinate transformation on the CT reconstruction image, and transforming the CT reconstruction image from a rectangular coordinate to a polar coordinate to obtain an image under the polar coordinate; 4) performing wavelet transformation on the image under the polar coordinates, and decomposing the image into a low-frequency component, a vertical high-frequency component, a horizontal high-frequency component and a diagonal high-frequency component under multiple scales; multiplying the vertical high-frequency component by an enhancement coefficient to enhance edge features in the image under polar coordinates, and then performing wavelet inverse transformation; and 5) converting the processed image to rectangular coordinates to obtain a final optimized sample CT image.

The invention relates to the field of brain tumor grading, in particular to a brain tumor grading evaluation method based on deep learning and digital twinning. According to the technical scheme, the method comprises the steps that brain tissue CT image data are acquired and preprocessed; constructing a tumor digital twinborn three-dimensional model, performing three-dimensional reverse reconstruction on the preprocessed two-dimensional CT image by adopting a filtered back projection algorithm to obtain corresponding three-dimensional data, and constructing a virtual simulation experiment environment and a tumor digital twinborn three-dimensional model by using the three-dimensional data UE and a visual development framework based on the UE; making a digital twin data set, selecting a deep learning model, and introducing the domain confrontation layer into the deep learning model for training; and after the deep learning model training is finished, adjusting a parameter group required for constructing the tumor digital twinborn three-dimensional model based on a genetic algorithm, obtaining an optimal parameter group, and dynamically updating the tumor digital twinborn three-dimensional model based on the optimal parameter group. The method is suitable for brain tumor grading.

The application discloses a kind of low-dose CT self-supervised denoising methods of physical driving sinusogram segmentation, belong to computer tomography and medical image processing field.The application utilizes CT projection angle noise statistical independence, adopts golden ratio segmentation strategy to divide original sinusogram into two angle coverage uniform, noise independent sub-sinusogram, obtains self-supervised training sample pair by filtered back projection;Through 2.5D ResUNet network of double branch shared weight extraction interlayer context feature, combined with the multi-constraint function of pixel-level L2 loss, perception loss and sinusogram consistency loss is jointly optimized.The application can complete training without normal dose CT matching data, effectively solve the ethics and cost problem of supervised learning, the peak signal-to-noise ratio of reconstructed image is about 2dB higher than similar self-supervised method, close to the performance of supervised learning method, applicable to the high-quality denoising reconstruction of clinical low-dose CT image.