Abstract: Disclosed is a ship image track tracking and prediction method based on ship heading recognition, which includes the following steps: obtaining a ship image data set, preprocessing the data set to obtain a preprocessed data set; inputting the preprocessed data set into the rotating ship detection network for training, obtaining the trained rotating ship detection network, collecting the ship navigation video, and inputting the ship navigation video into the trained rotating ship detection network to obtain the ship detection result; inputting the ship detection result into the rotating ship tracking network and tracking the target ship to obtain the historical trajectory and the heading information of the target ship; inputting the historical trajectory and ship heading information of the target ship into the ship trajectory and ship heading prediction network, and predicting the navigation trajectory and ship heading at sea.

Abstract: The present invention discloses a magnetic resonance fingerprinting imaging method with variable number of echoes, in addition to conventional MRF coding such as changing the excitation pulse angle, the method also introduces the change of the number of echoes, so that quantitative maps of B0, B1+, T1 and T2* can be obtained in a single scan. Further, if the echo time corresponding to the in-phase, opposed-phase and in-phase of water and fat is set for three consecutive echoes, the present invention can also image water and fat, and achieve the accurate quantification of B0, B1+, T1w, T1F, [T2*]w and [T2*]F. Through in vivo experiments and simulations, the effectiveness of the present invention has been proved. Therefore, the present invention can provide multiple information representations for common brain diseases (glioma) and fatty diseases (such as lipoma, fatty liver, etc.), which is conducive to clinical diagnosis and treatment.

Abstract: The disclosure provides a modified EPI sequence for acquiring multi-shot and multi-echo images with interleaved blip-up and blip-down phase encoding; the blip-up and blip-down images are processed by topup in FSL to estimate the inhomogeneous main magnetic field B0 map that causes image distortions; the B0 map is then incorporated into the encoding matrix with a low rank constraint to form a joint reconstruction model; the joint reconstruction model is solved to obtain multiple distortion-free images; and the multiple distortion-free images are matched to dictionary to simultaneous acquire the quantitative T2(=1/R2) and T2*(=1/R2*) maps. In the phantom and in-vivo measurements, the disclosed method rapidly acquires the comparable quantitative images within one hold-breath (for 20 s) to the conventional mapping method, thus providing important practical application value for evaluation of liver damage, iron level and cancer lesion.

Abstract: An attention mechanism-based low-dose dual-tracer PET reconstruction method. The method achieves low-dose dual-tracer PET image reconstruction by an attention mechanism-based convolution network model, and estimates the standard dose and separates dual-tracer PET signals in a sinogram. With the help of deep learning, a feature extraction tool, the method can reconstruct standard-dose single-tracer PET images in a PET Low-Dose Dual-Tracer Sinogram.

Abstract: The present invention discloses a method for constructing an intracardiac abnormal activation point location model based on CNN and LSTM. The model can well locate specific locations of abnormal activation points of VT and obtain three-dimensional coordinates of the locations, while obtaining 12-lead body surface potential data of a patient. The method introduces an idea of deep learning into locating of the abnormal activation points of ventricular tachycardia, uses collected QRS data as an input in a training phase, as well as three-dimensional coordinates of the QRS data corresponding to mapping points as a label to train a CNN-LSTM network, utilizes Conv1D to extract features from the input data, employs LSTM for feature fusion in a time domain, and exploits fully connected layers for regression prediction of the three-dimensional coordinates to finally construct the CNN-LSTM network.

Abstract: The present invention discloses a PET image reconstruction method based on Swin-Transformer regularization, the reconstruction model adopted is composed of several iterative modules, each iterative module consists of three parts: a EM iterative layer, a Swin-transformer based regularization layer, a pixel to pixel image fusion layer, the regularization layer is used to learn the prior information representing the image, comprising a convolution kernel used to extract the shallow features of the image, a Swin-Transformer layer used to extract the deep features of the image, and a convolution layer and a residual connection are used to fuse deep and shallow features. The image fusion layer fuses the results of EM iteration and regularization. The invention can reconstruct high quality PET images from Sinogram projection data, and greatly reduces the noise level of PET images while retaining the structural information.

Abstract: The present invention discloses a low dose Sinogram denoising and PET image reconstruction method based on teacher-student generator, the adopted network model is divided into a Sinogram denoising module and a PET image reconstruction module, the entire network needs to be processed in a training stage and a test stage. In the training stage: the present invention uses the denoising module to denoise the low dose Sinogram, and then makes the reconstruction module use the denoised Sinogram to reconstruct, in which the teacher generator is introduced in the training stage to constrain the whole, the denoising module is decoupled from the reconstruction module, and a better reconstructed image is obtained through training. In the testing stage, the present invention only needs to input low-dose Sinogram to the denoising module to obtain the denoised Sinogram, and then input the denoised Sinogram to the student generator to get the final reconstruction image.

Abstract: The present invention discloses a PET system attenuation correction method based on a flow model. The flow model adopted is a completely reversible model, the forward and reverse mappings share the same parameters, and the structure itself is a consistency constraint. The model utilizes the spatial correlation of adjacent slices, and adopts the structure of multi-slice input and single-slice output. The model consists of multiple reversible blocks, each of which consists of a enhanced affine coupling layer and a reversible 1×1 convolutional layer, and uses several small u-nets to learn the transformation parameters of the enhanced affine coupling layer. The present invention avoids additional CT or MR scanning, saves scanning cost for the patient, and reduces the damage of CT radiation to the patient; compared with similar methods, higher quality non-attenuation-corrected PET image can be obtained.

Abstract: The method for improving the coincidence time resolution of PET system based on STFT, the method obtains waveform data by setting a point source at a specific location, and then obtains a short term frequency domain amplitude information through short time Fourier transform (STFT), after that, carrying out a train set, a validation set, and a test set partitioning, and a residual neural network model composed of residual modules and a fully connected layer is used for training, achieving estimation of the TOF time of the PET system.

Abstract: The present invention discloses a magnetic resonance fingerprinting imaging method with variable number of echoes, in addition to conventional MRF coding such as changing the excitation pulse angle, the method also introduces the change of the number of echoes, so that quantitative maps of B0, B1+, T1 and T2* can be obtained in a single scan. Further, if the echo time corresponding to the in-phase, opposed-phase and in-phase of water and fat is set for three consecutive echoes, the present invention can also image water and fat, and achieve the accurate quantification of B0, B1+, T1w, T1F, [T2*]w and [T2*]F. Through in vivo experiments and simulations, the effectiveness of the present invention has been proved. Therefore, the present invention can provide multiple information representations for common brain diseases (glioma) and fatty diseases (such as lipoma, fatty liver, etc.), which is conducive to clinical diagnosis and treatment.

Abstract: A method is described for seismic depth uncertainty analysis including receiving wavelet basis functions and cutoff thresholds and randomly perturbing wavelet coefficients in reduced wavelet space based on the wavelet basis functions and the cutoff thresholds to generate a plurality of random wavelet fields; receiving a reference model in a depth domain; transforming the plurality of random wavelet fields to the depth domain and combining them with the reference model to form candidate models; performing a hierarchical Bayesian modeling with Markov Chain Monte Carlo (MCMC) sampling methods using the candidate models as input to generate a plurality of realizations; and computing statistics of the plurality of realizations to estimate depth uncertainty. The method may be executed by a computer system.

Abstract: A method is described for estimating depth uncertainty including receiving seismic data, a reference model, and trial model realizations; generating realization gathers from the trial model realizations; generating reference gathers from the reference model; determining a reference data fit based on the reference gathers and a data fit for trial models based on the realization gathers; selecting refined models from the trial model realizations based on the reference data fit, the data fit for trial models, and a data fit tolerance criterion; and calculating depth uncertainty based on statistics of the refined models. The method may be executed by a computer system.

Abstract: A method is described for stochastic modeling of seismic velocity and anisotropic parameters, including receiving 3D bounds of normal moveout velocity (Vnmo) and anisotropic parameter ?; modeling 3D bounds for vertical velocity V and anisotropic parameter ? based on the 3D bounds of Vnmo and ?; generating 3D model realizations of V, ?, and ? within the 3D bounds; and testing detectability of each of the 3D model realizations to create a detectable subset of model realizations wherein the detectability identifies which 3D model realizations will produce images with flat migrated gathers. The method may be executed by a computer system.

Abstract: A method is described for seismic depth uncertainty analysis including receiving wavelet basis functions and cutoff thresholds and randomly perturbing wavelet coefficients in reduced wavelet space based on the wavelet basis functions and the cutoff thresholds to generate a plurality of random wavelet fields; receiving a reference model in a depth domain; transforming the plurality of random wavelet fields to the depth domain and combining them with the reference model to form candidate models; performing a hierarchical Bayesian modeling with Markov Chain Monte Carlo (MCMC) sampling methods using the candidate models as input to generate a plurality of realizations; and computing statistics of the plurality of realizations to estimate depth uncertainty. The method may be executed by a computer system.

Abstract: The present invention discloses a cardiac transmembrane potential reconstruction method based on a graph convolutional neural network and an iterative threshold contraction algorithm. The cardiac transmembrane potential is econstructed iteratively through the iterative threshold contraction algorithm embedded in the graph convolutional neural network, and the graph convolutional neural network is used to extract the correlation information between nodes in the non-Euclidean dat a of the cardiac transmembrane potential, while retaining the rigorous mathematical calculations of the iterative threshold contraction algorithm, a solution og the cardiac transmembrane potential is obtained after multiple iterations.

Abstract: The invention discloses a limited-angle CT reconstruction method based on Anisotropic Total Variation. According to the method, through an image reconstruction model using low dose and sparse-view-angle CT images, a fast iterative reconstruction algorithm is combined with an Anisotropic Total Variation method. The problems that in an existing limited-angle CT reconstruction method are effectively solved, such as partial boundary ambiguity, slow convergence speed and unable to accurately solve. In the process of solving the model, the slope filter is introduced in the Filtered Back-Projection to preprocess the iterative equation, and the Alternating Projection Proximal is used to solve the iterative equation, and the iteration is repeated until the termination condition is satisfied; the experimental comparison with the existing reconstruction methods shows that the invention can achieve better reconstruction effect.

Abstract: A method for predicting multi-type ECG heart rhythms based on graph convolution includes: acquiring 12-lead ECG signals from a body surface of a patient, and resampling an ECG signal of each lead to a same signal length; constructing a node mutual information pooling U-shaped graph convolution network, and extracting deep features of the ECG signals by using a feature extraction module; performing one-layer one-dimensional convolution on the deep features to obtain a graph feature matrix to be constructed; inputting the obtained undirected graph into a graph encoding module in the graph convolution network, quantitatively calculating node mutual information of the undirected graph by using the graph encoding module, and selecting a node subset with the maximum mutual information to decrease the number of nodes in the undirected graph for down-sampling; inputting the undirected graph with the decreased number of nodes into a graph decoding module.

Abstract: The disclosure provides a modified EPI sequence for acquiring multi-shot and multi-echo images with interleaved blip-up and blip-down phase encoding; the blip-up and blip-down images are processed by topup in FSL to estimate the inhomogeneous main magnetic field B0 map that causes image distortions; the B0 map is then incorporated into the encoding matrix with a low rank constraint to form a joint reconstruction model; the joint reconstruction model is solved to obtain multiple distortion-free images; and the multiple distortion-free images are matched to dictionary to simultaneous acquire the quantitative T2 (=1/R2) and T2* (=1/R2*) maps. In the phantom and in-vivo measurements, the disclosed method rapidly acquires the comparable quantitative images within one hold-breath (for 20 s) to the conventional mapping method, thus providing important practical application value for evaluation of liver damage, iron level and cancer lesion.

Abstract: This present invention discloses a dynamic dual-tracer PET reconstruction method based on a hybrid-loss 3D CNN, which selects a corresponding 3D convolution kernel for a 3D format of dual-tracer PET data, and performs feature extraction in a stereoscopic receptive field (down-sampling) and the reconstruction (up-sampling) process, which accurately reconstructs the three-dimensional concentration distributions of two different tracers from the dynamic sinogram. The method of the invention can better reconstruct the simultaneous-injection single-acquisition dual-tracer sinogram without any model constraints. The scanning time required for dual-tracer PET can be minimized based on the method of the present invention. Using this method, the raw sinogram data of dual tracers can be reconstructed into two volumetric individual images in a short time.

Abstract: The present invention discloses a low dose Sinogram denoising and PET image reconstruction method based on teacher-student generator, the adopted network model is divided into a Sinogram denoising module and a PET image reconstruction module, the entire network needs to be processed in a training stage and a test stage. In the training stage: the present invention uses the denoising module to denoise the low dose Sinogram, and then makes the reconstruction module use the denoised Sinogram to reconstruct, in which the teacher generator is introduced in the training stage to constrain the whole, the denoising module is decoupled from the reconstruction module, and a better reconstructed image is obtained through training. In the testing stage, the present invention only needs to input low-dose Sinogram to the denoising module to obtain the denoised Sinogram, and then input the denoised Sinogram to the student generator to get the final reconstruction image.