2221 results about "Mri image" patented technology

The invention discloses an MRI medical image correction method and system based on a convolutional neural network and a computer readable storage medium, and the method comprises the steps: obtaining a brain MRI medical image, and carrying out the preprocessing; extracting lesion features of the brain lacuna infarction from the preprocessed MRI medical image through a convolutional neural network, and positioning a lesion area of the brain lacuna infarction; performing detail enhancement on the lesion area of the cerebral lacuna infarction, segmenting the lesion area and optimizing a segmentation result; and carrying out artifact restoration on the segmented image, and generating a three-dimensional focus visualization model based on the restored image. According to the method, through processing in multiple aspects such as quality evaluation, lesion recognition, segmentation, artifact repair and three-dimensional visualization of the MRI image, the lesion area can be recognized more efficiently and accurately, the image quality is improved, and an efficient and accurate brain lacuna infarction diagnosis auxiliary tool is provided.

The invention discloses a medical image enhancement method and system based on multi-modal fusion, and the method comprises the steps: obtaining medical image data which comprises a CT image, an MRI image, a PET image and an ultrasonic image; calculating a mutual information value of each semantic tag in the cross-modal feature vocabulary, and generating an inter-modal feature correlation matrix; performing non-rigid space registration on the unified dimension image set to generate a geometrically consistent multi-modal image set; extracting skeleton region features, soft tissue region features and high metabolism region features from the multi-modal image set, and generating a core feature set; a deep learning algorithm is adopted to train the core feature set, and a segmented image set containing a segmentation mask is generated; and dynamically adjusting the fusion weight according to the regional features of the segmented image set, and generating a fusion enhanced image. According to the invention, through feature extraction, spatial registration and deep learning fusion of the multi-modal medical image, effective integration of different-modal medical image information is realized.

The invention relates to the technical field of prostate MRI (Magnetic Resonance Imaging) image segmentation, in particular to a prostate MRI image segmentation method based on Mamb-Unet, which realizes effective fusion of features of different scales between an encoder and a decoder, and fuses multi-scale information on a space axis by using a space attention bridge segment to fuse fusion features of different levels from the encoder, so that the segmentation accuracy of the prostate MRI image is improved. And then global and local information is captured through 1D convolution and full connection in the channel attention bridge segment, so that a channel attention map is generated, channel information is better integrated, and multi-level different-scale feature fusion is realized.

The invention discloses a brain tumor MRI image segmentation model and method based on an improved Swin UNETR network, and belongs to the technical field of image segmentation. The brain tumor MRI image segmentation model is obtained by improving a Swin UNETR as a baseline model and comprises an encoder, a jump connection and a decoder, the decoder comprises a plurality of convolution fusion modules and an attention mechanism module, feature information of different levels is fused in the decoder by using the plurality of convolution fusion modules, and the attention mechanism module is used for identifying the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the convolution fusion modules. And the attention mechanism module is used to focus the feature information obtained by the plurality of convolutions. By adopting the scheme of the invention, the problem that the existing medical image segmentation technology based on deep learning is insufficient in brain tumor MRI image segmentation precision and is difficult to realize accurate segmentation especially in the face of different types of tumors and variability can be effectively solved, the calculation amount is small, and the image segmentation efficiency can be effectively improved.

The invention discloses a multi-modal medical image fusion diagnosis system based on artificial intelligence, and the system comprises the following steps: extracting shared features of CT and MRI images through a convolutional neural network, and mapping the shared features to the same feature space; a two-way step-by-step alignment strategy is adopted, a three-dimensional deformation field matrix is generated, and cross-modal image anatomical structure alignment is achieved; calculating modal feature weights and eliminating distribution differences through an attention mechanism and an adversarial domain adaptation layer; constructing a CT-MRI image block contrast learning task, and optimizing a shared feature encoder; a conditional generative adversarial network is used for generating a false image of a missing mode according to the semantic segmentation map, and data distribution is constrained through a Wasserstein distance; uniform feature extraction of multi-modal medical images is realized through a shared feature encoder, the cross-modal image alignment accuracy is improved in combination with a bidirectional deformation field prediction module, and the comprehensiveness and accuracy of fusion features are enhanced by using a multi-modal feature fusion module.

The invention provides a brain parenchyma MRI image-based infarction change prediction method and system, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a brain parenchyma MRI image, constructing a multi-modal image set, carrying out the screening according to a screening standard, carrying out the artifact recognition, calculating a quality score, carrying out the preprocessing, obtaining standard image data, carrying out the feature extraction, and carrying out the prediction of the infarction change of the brain parenchyma MRI image. Obtaining a brain region segmentation mask; performing feature extraction to obtain multi-modal feature data, constructing a topological relation among different brain regions, extracting high-order semantic features, obtaining brain region feature data, and performing selection to obtain high-quality feature data; constructing a time sequence characteristic matrix, distributing weights, generating residual output, performing prediction through a full-connection neural network, generating an infarction change prediction result, generating a prediction prognosis result, and generating a comprehensive prediction report.

The invention relates to a PET (Positron Emission Tomography) and MRI (Magnetic Resonance Imaging) multi-modal medical image fusion method and system based on a Kolmogorov-Arnold network. The method comprises the steps of collecting PET and MRI images for normalization processing, and extracting an initial feature map, a first scale feature map, a second scale feature map, a third scale feature map and a fourth scale feature map through a hierarchical feature extraction network of a multi-scale dynamic convolution kernel architecture; inputting the first-scale feature map, the second-scale feature map and the third-scale feature map into a KAN for pyramid coding, decomposition and reconstruction, multi-scale decomposition and other operations to obtain a decoded feature map; and inputting the fourth scale feature map into a frequency domain-spatial domain collaborative fusion framework based on dynamic sparse attention guidance for feature integration, performing frequency domain expansion based on reversible frequency domain up-sampling, and performing channel weighted splicing, spatial-channel decoupling processing and interactive fusion processing to obtain a final fused medical image. The image details can be obtained, noise can be effectively suppressed, and the accuracy and reliability of the fused image are improved.

The invention discloses an MRI medical image segmentation method and system based on deep learning, and the method comprises the steps: obtaining a mammary gland MRI image, carrying out the standardization processing of the mammary gland MRI image, and removing a non-region of interest to obtain a target MRI image; performing detail enhancement on the suspected lesion area to reconstruct an image to be preprocessed; performing noise reduction processing, contrast enhancement and edge enhancement preprocessing on the to-be-preprocessed image to generate three versions of input images, and inputting the three versions of input images into a pre-trained deep learning model as three channels to complete breast tumor identification; and carrying out image segmentation on the breast tumor region, and classifying segmented breast tumors. According to the MRI medical image segmentation method and system based on deep learning provided by the invention, accurate and efficient processing of the mammary gland MRI image is realized, efficient identification, accurate segmentation and accurate classification of the mammary gland tumor are realized, the accuracy and robustness of mammary gland tumor detection can be improved, and efficient and reliable auxiliary diagnosis is provided for clinic.

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention relates to the technical field of multi-modal medical image registration optimization methods, in particular to a medical image registration optimization method and system, and the method comprises the following steps: inputting a medical image data set comprising an MRI image data set, a CT image data set and a PET image data set into a trained deformable registration network; obtaining a deformation field by using the deformable registration network; obtaining a final registration result of the two groups of medical image data based on the deformation field, obtaining an optimized final registration result through an iterative optimization algorithm, and obtaining a quality evaluation coefficient at the same time; constructing an optimized objective function by using the quality evaluation coefficient, and realizing the adjustment of the hyper-parameter of the deformable registration network through the back propagation of the optimized objective function; prediction registration is carried out by using the deformable registration network after hyper-parameter adjustment, so that an optimized registration result is obtained, the position and the shape of a convolution kernel are adaptively adjusted based on a feature extraction network of deformable convolution, and nonlinear deformation of a local anatomical structure is better captured.

The invention discloses a brain tumor MRI image detection method based on improved YOLOv8n, and belongs to the crossing field of computer science and technology and medical image.The method comprises the steps that a brain tumor MRI image to be detected is obtained, and the brain tumor MRI image to be detected is input into a brain tumor MRI image detection network model to obtain a brain tumor detection result; the brain tumor MRI image detection network model adopts an improved YOLOv8n model; according to the improved YOLOv8n model, a lightweight backbone network improved based on StarNet is used as a backbone network of the model on the basis of an original YOLOv8n model, a simple and parameter-free attention module SimAM is introduced behind an SPPF layer, an improved C2fStar module is used for replacing a C2f module in a neck network, and the improved YOLOv8n model has higher efficiency and better adaptability while high precision is kept; especially in an environment where computing resources are limited.

The invention provides an MRI brain tumor image segmentation method based on a state space model and a frequency domain, and relates to the field of image processing. The method comprises the following steps: acquiring a brain tumor MRI image to be segmented and preprocessing the brain tumor MRI image; and inputting the preprocessed brain tumor MRI image into a segmentation network based on a state space model and a frequency domain. The network is based on a U-Net architecture, and global context modeling is performed on feature information by introducing a state space model into a bottleneck layer. In order to optimize feature extraction, a dynamic weight mechanism is designed aiming at features input into a state space model, the importance of feature sequences in three directions is dynamically adjusted according to input data, and the attention to a brain tumor key area is enhanced. By adding a 3D frequency domain fusion module in jump connection, high-frequency noise is reduced, and perception of the overall structure of a brain tumor image is enhanced. According to the method, the state space model and the 3D frequency domain fusion module are introduced, and the importance of the feature sequence is dynamically adjusted, so that brain tumor MRI image segmentation with higher precision is realized.

Denoised magnetic resonance images are generated using a two-step process. An initial set of images is first denoised on a per channel basis using a locally low-rank-based denoising technique. The denoised coil channel images are transformed back into k-space and the denoised k-space data are then applied to a nonlinear image reconstruction. In some instances, the nonlinear image reconstruction can be implemented using a trained neural network. The neural network may be trained using a self-supervised learning technique.

The invention provides a prostate intervention guiding method and system based on PSMA PET, MRI and TRUS multi-mode fusion, a storage medium and electronic equipment, and the method comprises the steps: obtaining a preoperative MRI image and a PSMA PET / CT image, and carrying out the marking of a prostate focus; registering the PSMA PET image to a coordinate system corresponding to MRI (Magnetic Resonance Imaging); a TRUS image obtained in the operation is registered to a coordinate system corresponding to the MRI image, and an MRI-PSMA PET-TRUS fusion image is obtained; and based on the fused image, mapping the marking result of the prostate lesion to the TRUS image, and guiding the prostate interventional operation. Preoperative traditional MRI and PSMA PET are subjected to static fusion and then subjected to real-time fusion with intraoperative TRUS, multi-modal image features are combined, the reliability of an image focus area is improved, the positive effect on improving the accuracy of an interventional operation is achieved, and unnecessary injuries are reduced.

The invention belongs to the technical field of medical image processing, and particularly relates to an automatic positioning and typing method for cervical posterior longitudinal ligament ossification, which is characterized in that ossification focus spatial distribution characteristics of CT images and spinal cord morphological parameters of MRI images are synchronously analyzed based on CT and MRI images, a vertebral body-ossification-spinal cord spatial relationship is explicitly modeled through anatomical structure topological constraint, and a cervical vertebra-ossification-spinal cord spatial relationship is obtained. Diffuse calcification artifacts, heterogeneity signal interference and multi-modal data registration deviation are overcome, accurate positioning and typing diagnosis of the ossification are achieved, multi-dimensional feature fusion analysis is achieved, the limitation of traditional single iconography index diagnosis is broken through, space continuity and clinical parameters are integrated through a hybrid classifier, and the accuracy of diagnosis is improved. The accuracy and robustness of typing judgment are improved, a structured diagnosis report is automatically generated in the whole process, and the clinical decision-making efficiency and the standardization level are remarkably improved; the principle is scientific and reliable, and the ossification continuity index, the spinal canal invasion rate and the spinal cord compression grading parameters are automatically calculated according to the typing standard.

The invention discloses a method for identifying an autism brain image through multi-mode brain image information fusion, is applied to the field of image identification, and aims to solve the problems that multi-mode nuclear magnetic resonance image data are not fully utilized and the model interpretability is insufficient in the prior art. The method comprises the following steps: respectively extracting brain region features from three modal data of a structural magnetic resonance image, a functional magnetic resonance image and a phenotype, then carrying out multi-modal fusion, and constructing a brain region map structure; designing a drawing convolution module and a loss function to learn how to extract brain map depth features; on the basis of brain map depth features, a multi-layer perceptron classifier is trained through ABIDE data set label data, and better performance indexes such as accuracy, sensitivity and heterogeneity can be obtained; the trained multi-mode brain image fusion identification autism model can be used for assisting autism diagnosis and identification.

The invention discloses an MRI (Magnetic Resonance Imaging) image segmentation method based on WKA-Unet. The MRI image segmentation method comprises the following steps: 1) data preprocessing; 2) constructing a WKA-Unet network; and 3) testing. According to the technical scheme, on the basis of a U-Net network structure, an MRI image segmentation model is improved; a traditional Unet network is combined with a self-designed WKA module, multi-scale features are fully captured by using wavelet transform convolution and KAN convolution, and the loss of traditional Unet global information is relieved; an attention mechanism is introduced to pay attention to key parts, and redundant information is removed; according to the technical scheme, the network is trained more flexibly and accurately, a more accurate segmentation result is obtained, the segmentation precision is high, and the model structure is efficient and stable.

The invention relates to the technical field of medical image processing, and discloses a craniofacial dynamic reconstruction method and system based on multi-modal data fusion, and the method comprises the steps: arranging a multi-modal data collection device in a target craniofacial region, and obtaining a static CT image, a static MRI image, a dynamic expression video sequence and a surface electromyogram signal; preprocessing the static CT image, the static MRI image, the dynamic expression video sequence and the surface electromyogram signal; inputting the preprocessed data into a multi-scale finite element model, and simulating a coupling relationship between muscle contraction force and skin deformation by adopting a biomechanical driving strategy to generate a dynamic craniofacial model; and fusing the geometric error and the motion consistency score of the real data by adopting a linear regression method, and outputting a comprehensive reconstruction quality index. According to the method, the problems of low craniofacial dynamic modeling accuracy and poor robustness in the prior art can be solved.

The invention relates to the technical field of image processing, in particular to a craniocerebral disease area identification and detection method and system based on an MRI (Magnetic Resonance Imaging) image, and the method comprises the steps: obtaining a plurality of sub-images with different scales according to a gray level image of the craniocerebral MRI image; performing multi-scale analysis on the gradient value of any pixel point according to each sub-image to obtain a multi-scale gradient coefficient of any pixel point; obtaining a multi-scale local anomaly degree according to the gray values of the pixel points in different local ranges of any pixel point and the distribution in the gradient direction; optimizing the gradient value of any pixel point according to the multi-scale gradient coefficient and the multi-scale local anomaly degree to obtain a self-adaptive gradient value, and performing image enhancement on the grayscale image by using an anisotropic diffusion filtering algorithm according to the self-adaptive gradient value of each pixel point so as to identify a craniocerebral disease region. And the effect of performing image enhancement on the MRI image by using the anisotropic diffusion filtering algorithm is improved.

The invention provides a multi-modal large language model for generating a hepatocellular carcinoma key pathological diagnosis report, a framework main body is a visual coding module, and a multi-modal feature alignment module, a multi-head low-rank attention mechanism, an enhanced medical MoE mechanism and a structured output decoding layer are also introduced. The visual coding module is constructed on the basis of a Swin Transform architecture, visual pre-training is completed on hepatocellular carcinoma MRI data, and after a task specific classification head is stripped, a trunk feature extraction network is reserved to serve as an image modal representation encoder. The multi-modal feature alignment module guides the model to learn a cross-modal semantic mapping relation between a hepatocellular carcinoma MRI image and a key pathological diagnosis report language, image modal input is a visual feature sequence, and text modal output is a structured description text; and the structured output decoding layer generates six types of liver cancer focus attributes. According to the method, the pre-operative multi-parameter and multi-stage enhanced MRI image is utilized, and the open-source large model is finely adjusted to generate a matched liver cancer postoperative pathology report.

The invention discloses a cervical cancer close-range radiotherapy high-risk target area sketching method fused with a multi-modal image. The method comprises the following steps of collecting an MRI image scanned before radiotherapy and a CT image during radiotherapy of a cervical cancer close-range radiotherapy patient and annotation data of the MRI image and the CT image; the method comprises the following steps: preprocessing an MRI image scanned before radiotherapy and a CT image during radiotherapy, and converting annotation data into a three-dimensional tag image; on the basis of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, registration of the MRI image and the CT image is conducted through a pre-constructed registration neural network model, feature extraction and fusion are conducted on the registered MRI image and the registered CT image through a pre-constructed high-risk target area automatic segmentation neural network model of a multi-scale cross-modal attention mechanism, and the high-risk target area automatic segmentation neural network model of the multi-scale cross-modal attention mechanism is obtained. Automatic delineation of a cervical cancer close-range radiotherapy high-risk target area is realized; according to the method, automatic segmentation of HR-CTV in close-range radiotherapy of cervical cancer is realized, high efficiency, accuracy and generalizability are realized, and intelligent support can be provided for clinical work.

The invention discloses an ovarian tumor cross malignancy classification system based on a hybrid radiomics model. On one hand, a backbone network in the Med3D network is used as a depth feature extractor to construct a target network, and multi-modal 3D magnetic resonance image data is used to carry out fine tuning training on the target network to obtain a corresponding ovarian tumor classification network for obtaining depth features and tumor classification results. On the other hand, omics characteristics of the tumor region of interest are extracted, and a tumor classification result corresponding to the omics prediction model is obtained. In addition, the depth features and the omics features are fused, and a corresponding classification result is obtained through a KNN classification algorithm. And finally, obtaining a final ovarian tumor cross malignancy classification result according to weighted average of the three classification results. According to the invention, by integrating various models and algorithms, more comprehensive feature mining and analysis are carried out on the multi-modal MRI image data of the ovarian tumor patient, so that the final ovarian tumor classification effect is improved.

The invention provides a magnetic resonance image self-supervision super-resolution reconstruction method and system based on a prior guide diffusion model. The method comprises the steps of image preprocessing, pseudo-pairing data set construction, prior condition construction, model training, model testing and three-dimensional isotropic volume reconstruction. According to the method, the dependence on a real high-resolution image is remarkably reduced by constructing a self-supervised pseudo-pairing mechanism, and high-quality reconstruction from anisotropic MRI to an isotropic image is realized; meanwhile, by introducing multiple medical priori including high-frequency residual, edge structure and region-of-interest guidance, detail retention and structural boundary definition of the image are effectively improved, the convergence speed and robustness of the model are improved while anatomical fidelity is guaranteed, and the method is suitable for large-scale popularization and application. And a technically feasible and clinically applicable solution is provided for high-resolution reconstruction of medical images.

The invention proposes a myocardial magnetic resonance image fibrosis classification method based on a convolutional neural network, and relates to the technical field of image processing, and the method comprises the steps: obtaining an original magnetic resonance image of a to-be-detected target from a magnetic resonance imaging device, and carrying out the preprocessing of the original magnetic resonance image; segmenting the preprocessed magnetic resonance image into a plurality of sub-regions based on the connected features, each sub-region representing an independent connected part in the image; constructing a gray scale measurement function for each sub-region, quantifying the gray scale distribution characteristics of each sub-region, and identifying the sub-region with the minimum gray scale measurement function value as a fibrosis tissue sub-region by comparing the gray scale measurement function values of the sub-regions; features are extracted from the identified fibrosis tissue sub-regions to serve as input of the convolutional neural network model, the fibrosis tissue sub-regions are classified, the fibrosis stages to which the fibrosis tissue sub-regions belong are output, and the accuracy of classification of the fibrosis tissue is improved.

The invention discloses a compressed sensing MRI (Magnetic Resonance Imaging) reconstruction method and system based on a double-domain fusion expansion model. The method comprises the following steps: acquiring an MRI image, preprocessing the MRI image to obtain a preprocessed image and a corresponding compressed sensing measurement value, and constructing a data set; training, testing and verifying the double-domain fusion expansion model by using the data set; and outputting a reconstructed image from the MRI image after mask sampling by using a double-domain fusion expansion model. According to the double-domain fusion expansion model, a complex convolutional neural network and a K-space attention mechanism are introduced for complex value data processing and double-domain information fusion in compressed sensing MRI reconstruction. The complex value data is processed through the complex network, the amplitude and phase information of the complex field is fully utilized, the detail recovery capability and the noise suppression effect of the model are enhanced, and particularly, excellent robustness is shown at a low sampling rate. The method is excellent in performance in complex MRI data reconstruction, and has high reconstruction precision and generalization ability.

The invention belongs to the technical field of medical image processing and analysis, and relates to an MRI (Magnetic Resonance Imaging) brain tumor segmentation method based on multi-modal feature fusion, which comprises the following steps of: constructing an image segmentation model comprising an encoder and a decoder; according to the encoder part, firstly, preliminary feature extraction is carried out on input MRI images of four modes of T1, T1c, T2 and Flair through a standard convolution normalization activation module and a cavity convolution encoding enhancement module, and then importance weighting and selection are carried out on extracted features through a mode-level feature fusion module to obtain fused features; according to the decoder part, feature representation is further extracted and optimized through a space and channel-level feature fusion module, and finally a prediction segmentation image is output through an up-sampling channel and space feature fusion convolution module. According to the method, the MRI brain tumor segmentation precision is improved, and powerful technical support is provided for clinical accurate diagnosis, treatment plan making and patient monitoring.

A magnetic resonance image reconstruction device according to an embodiment is a magnetic resonance image reconstruction device that reconstructs magnetic resonance image data in which an artifact due to undersampling is removed or reduced based on undersampled k-space data, and includes a reconstruction unit reconstructing the magnetic resonance image data using a reconstruction network having a correction module. The correction module includes a regularization block generating second image data by performing a regularization process on first image data using a first neural network, and a data consistency block generating third image data by performing a data consistency process so that k-space data corresponding to the second image data approaches the undersampled k-space data. The correction module further includes at least one of a data consistency adjustment block adjusting the data consistency process and a regularization adjustment block adjusting the regularization process.

The invention relates to the technical field of image processing, in particular to a spinal arthritis medical image feature auxiliary identification method, which comprises the following steps: acquiring a T1 weighted image and a T2 weighted image of a spinal MRI image of a patient, and recording the T2 weighted image as an original image; obtaining a target area in the original image; obtaining a mapping adjustment coefficient of each target area according to the edge shape difference and the gray scale difference of each target area and the corresponding area of each target area in the T1 weighted image; acquiring a local enhanced image according to the mapping adjustment coefficient; obtaining a correction enhancement coefficient of each target area; and performing gray stretching on the local enhanced image according to the correction enhancement coefficient of each target area to obtain a final enhanced image. The invention aims to improve the contrast ratio of the area capable of reflecting the spinal arthritis, ensure the difference between the T1 weighted image and the T2 weighted image, and reduce the reading difficulty of the MRI image when a doctor recognizes the spinal arthritis.

The invention provides a magnetic resonance image reconstruction method and device, a model training method and device, equipment and a medium. The method comprises the following steps: inputting acquired to-be-reconstructed under-sampling k-space data into a pre-constructed image reconstruction model, and carrying out continuous k-space reconstruction processing for multiple times to obtain a magnetic resonance reconstruction image; wherein the k-space reconstruction processing in the image reconstruction model comprises the steps of performing frequency domain restoration on input k-space data based on a Transformer encoder introduced with symmetric weight, performing image domain signal restoration on the k-space data after frequency domain restoration, and performing data consistency processing on the k-space data after image domain signal restoration. According to the method, the image reconstruction model is constructed in an end-to-end expansion training optimization mode, so that the frequency domain and image domain information of the undersampled data can be fully utilized, the accuracy of image reconstruction is effectively improved, the computing resource overhead and the time cost can be effectively reduced, and the high efficiency of the reconstruction process is ensured.

The invention discloses a 4D-Flow-CFD simulation model construction method and system for quantitative evaluation of cirrhosis portal vein hydrodynamics, and belongs to the technical field of medical image processing, the construction method comprises the following steps: step 1, placing a patient on a scanner, and sequentially performing structural image and 4D-Flow MRI (four-dimensional blood flow magnetic resonance) scanning; 2, integrating the 4D-Flow MRI and CFD (computational fluid dynamics) technology, performing post-processing on a 4D-Flow MRI image by adopting Circle software, and screening out hemodynamic characteristic parameters capable of representing the liver cirrhosis process; and 3, constructing a liver portal vein system finite element model by using the screened haemodynamic characteristic parameters so as to establish a quantitative evaluation model of portal vein haemodynamics of the patients with cirrhosis, so that a new haemodynamic detection method is explored for clinical systematic evaluation of cirrhosis, and a new thought is provided for accurate evaluation of cirrhosis.