553 results about "Imaging study" patented technology

The invention discloses a benign and malignant nodule grading evaluation system based on large model fusion ultrasonic imaging and thyroid gene markers, which can organically fuse non-invasive examination and serological detection, can simulate and diagnose multi-grade risk probability information provided by a gold standard, realizes similar risk grading estimation in a non-invasive mode, and has a wide application prospect. The thyroid nodule risk assessment method can provide visual explanation conforming to clinical logic based on comprehensive information of iconography and molecular biology, can significantly improve the accuracy of thyroid nodule risk assessment, can also effectively improve clinical decision-making efficiency and patient credibility, and has important clinical application prospects. The system comprises a data acquisition module, an ultrasonic image feature extraction module, a gene marker feature extraction module, a multi-modal fusion and hierarchical reasoning module and a generation module.

The invention relates to the technical field of three-dimensional reconstruction, and provides a hepatic vessel three-dimensional reconstruction system based on image segmentation, which optimizes an encoder and a decoder of a U-Net by using an attention mechanism, a dynamic weighting uncertainty mechanism and an HS-Swish activation function in image segmentation processing. In combination with hierarchical attention enhancement jump connection and multi-scale cavity convolution, the liver image data is precisely processed; in the aspect of three-dimensional reconstruction, a classical Marching Cubes algorithm is improved, interlayer interpolation of an optical flow estimation method is optimized, intersection point calculation precision is improved by adopting a trisection method, and a high-quality three-dimensional reconstruction model is generated; the system effectively solves the problem that a traditional system is insufficient in precision, image segmentation and reconstruction effects are remarkably improved, and reliable support is provided for the field of liver iconography.

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 belongs to the technical field of medical data processing and artificial intelligence, and particularly relates to a cardiovascular disease risk prediction system based on multi-modal fusion, which comprises a multi-modal data acquisition and preprocessing module, a cross-modal association graph construction module, a dynamic fusion and prediction module based on a graph neural network and an interpretability analysis module. By constructing a heterogeneous graph fusing prior knowledge and data driving and utilizing a graph attention network to perform multi-level dynamic feature fusion, deep integration and interaction of multi-modal data such as genomes, iconography, clinical and intestinal flora metabolism are realized, so that the accuracy and interpretability of cardiovascular disease risk prediction are improved.

The invention discloses a device for tracking a tumor position in real time in radiotherapy, and solves the problems of delay and inaccuracy in a traditional tumor position prediction method through a multi-model fusion method combining a deep learning model and a mathematical prediction model. Different from a traditional prediction method depending on an in-vitro marker or iconography technology, the tumor position is predicted in real time by adopting a long-short-term memory network, and the change of the tumor position along with respiratory movement is accurately captured by utilizing the strong time sequence modeling capability of the long-short-term memory network. By introducing the mathematical prediction model to compensate the future position of the tumor, the method can effectively solve the prediction error caused by system delay, and ensures that rays can accurately irradiate the tumor area in dynamic tumor tracking radiotherapy, thereby improving the accuracy and treatment effect of radiotherapy.

The invention discloses a rigidity self-adaptive intravascular stent based on a three-dimensional virtual woven structure and a control method thereof. The method comprises the steps of reconstructing a preoperative tumor blood vessel three-dimensional model based on clinical iconography data, constructing a stent initialization model, starting a hexagonal knitting three-dimensional virtual algorithm, performing intravascular stent knitting modeling, realizing structural parameter optimization and adaptive stent expansion in combination with preoperative image data of a patient, and completing personalized deployment. The wire winding angle and the mesh density of each area of the stent are adjustable, the stent is adaptive to the complex intracranial aneurysm blood vessel form, accurate fitting and risk guiding are achieved, the treatment effect is improved, the postoperative complication risk is reduced, and the stent has good engineering suitability and clinical application prospects.

The invention provides a tumor detection data analysis method based on a peripheral blood immune marker, and belongs to the technical field of immunodetection.The tumor detection data analysis method comprises the steps that firstly, a peripheral blood sample of a patient is collected, and plasma and cell components are separated; analyzing various immune cell phenotypes by using flow cytometry; determining the concentration of key cell factors; analyzing the molecular expression level of the immune checkpoint; establishing an immune cell subset proportion empirical function; analyzing a cell factor expression mode by using an immune lineage transcriptome analysis model; establishing a tumor immune microenvironment score by combining the immune cell subset proportion and a model output result; the accuracy of the scoring system is verified through clinical follow-up visit and iconography examination; a standardized report template is formulated to provide a tumor early detection basis. Wherein the immune lineage transcriptome analytical model adopts a multi-modal attention fusion network architecture, three different immune data streams can be processed and integrated at the same time, and accurate evaluation of tumor-related immune states is realized.

The invention discloses a multi-modal iconography evaluation method suitable for atrial fibrillation cardiac stroke. The method comprises the following steps: acquiring and preprocessing heart and brain multi-modal image data; carrying out multi-modal image registration on the preprocessed data; feature extraction and fusion are carried out on the registered heart and brain multi-mode image data; performing cross-modal feature alignment and fusion on the heart and brain image data; constructing a heart and cerebral vessel integrated evaluation interaction model based on a graph neural network; predicting the risk of the end-to-end cardiac stroke; according to the multi-modal iconography evaluation method, multi-modal heart image data and multi-modal brain image data are combined, a heart and cerebral vessel integrated evaluation framework is established, image features and clinical data are fused through an artificial intelligence method, and end-to-end stroke risk prediction and evaluation are achieved. According to the method, artificial intelligence and medical technologies are comprehensively utilized, and the accurate cardiac stroke assessment method is provided.

The invention relates to the technical field of medical information, in particular to an aortic dissection rescue guidance method and system based on multi-dimensional data fusion, the method comprises the following steps: acquiring comprehensive medical data of a patient, the comprehensive medical data comprising at least one of real-time vital sign data, iconography data and medical history information; performing fusion processing on the comprehensive medical data, constructing a disease condition portrait based on a fusion data set, capturing a disease condition change trend, and obtaining a dynamic disease condition portrait of the patient; constructing a rule base based on historical data, and matching the patient's condition portraits with rules in the rule base to obtain condition features and risk indexes; a risk assessment standard is established based on the illness state characteristics and the risk indexes, the illness state severity is assessed through the risk assessment standard, possible complications are predicted, and personalized rescue suggestions and decision support are provided for medical staff.

The invention discloses a medical data analysis method based on an intelligent agent, and the method comprises the following steps: S1, obtaining a multi-dimensional heterogeneous data source which comprises an unstructured log, a structured attribute tag, time sequence sensor data and an iconography data report; s2, performing structured processing on the dynamic rule base document; s3, performing multiple rounds of iterative reasoning by using the data analysis agent to generate a preliminary analysis decision scheme; s4, constructing a data set for training a process reward model PRM; s5, training a process reward model as a verifier of a data analysis agent reasoning process, and optimizing the process reward model by using a cross entropy loss function; and S6, optimizing a decision scheme generation process of the data analysis agent based on a process reward model, sampling M candidate states for each decision step by the agent in a step-by-step reasoning process, scoring each candidate state by using a verifier, and selecting the state with the highest score as a final decision of the step.

The invention belongs to the technical field of image processing, and provides an iconography priori knowledge-guided craniocerebral CT (Computed Tomography) various anomaly real-time detection method, which comprises the following steps of: acquiring a craniocerebral CT image sequence in real time; inputting the craniocerebral CT image sequence into a pre-trained anomaly detection model to obtain an anomaly detection result; the anomaly detection model comprises a backbone network used for extracting multi-scale features of a craniocerebral CT image sequence, the backbone network comprising a plurality of cascaded feature extraction modules, and at least one feature extraction module being a symmetric information attention module; a neck network and a multi-detection-head network; the symmetric information attention module comprises an opposite-side difference chart attention network or more than two cascaded opposite-side difference chart attention networks, a difference information acquisition unit and a feature enhancement unit. The invention also discloses a computer program product and an electronic equipment system. According to the method, the matching flexibility of the symmetrical anatomical structures on the two sides is improved, and the accuracy and reliability of craniocerebral CT image anomaly detection are improved.

The invention discloses a liver transplantation post-operation intelligent management system and method based on multi-modal monitoring and AI. The method comprises the following steps that S1, physiological parameters, biomarker data, iconography data and self-reporting data of a patient are collected in real time; s2, carrying out preprocessing, feature extraction and fusion on the collected data, and predicting the postoperative complication risk of the patient by using a risk prediction model; s3, generating a personalized management strategy by using a reinforcement learning algorithm according to a risk prediction result, and providing early warning information and intervention suggestions for medical personnel and patients; s4, according to the postoperative management plan and the doctor's advice of the patient, a fixed-point medication reminding function is set, a personalized medication scheme is generated, and medication compliance of the patient is monitored; and S5, the patient makes an appointment for remote medical services, selects appropriate doctors and time to carry out video inquiry or online consultation, and records inquiry content.

The invention discloses an image recognition system for constructing osteoporotic vertebral fracture healing based on artificial intelligence, and particularly relates to the technical field of orthopedic treatment, which comprises a data preprocessing module for extracting and optimizing data from an original medical image. According to the method, the advantage of discipline crossing is developed, a recognition system on medical imaging is developed based on deep learning, the advantage that the recognition system can discover detail features and hidden rules which cannot be found by human beings is utilized, and then a large amount of structured data is utilized to automatically learn and train visual feature expression of abstract data, so that the clinical missed diagnosis rate is reduced, and the clinical diagnosis efficiency is improved. The artificial intelligence architecture is trained by a rigorous and scientific means, so that the artificial intelligence architecture has clinical experience and knowledge of high-age dominant doctors and is popularized in local hospitals to achieve an auxiliary diagnosis effect, the difference of medical differentiation is reduced, the technical and experience defects of local medical personnel are made up, and the technical and experience defects of the local medical personnel are overcome. The aim of improving the definite diagnosis rate of diseases and reducing the missed diagnosis rate is one of the purposes of the invention.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention relates to the technical field of image motion analysis, in particular to a rectal cancer radiotherapy effect dynamic monitoring system combined with iconography analysis. Determining a reference recovery index representing the recovery effect from the dimensions of edge change and area change according to the characteristic that the lesion area becomes smaller and smaller along with the better and better treatment effect; then, based on the reference recovery index and in combination with the characteristic that rectum tissue circularity can cause gradual inward contraction of a lesion area and the edge is smoother and smoother, determining a more accurate shape recovery degree representing the reference recovery index; therefore, the corrected radiotherapy recovery curve is corrected more accurately according to the shape recovery degree, and dynamic monitoring of the rectal cancer radiotherapy effect is more visual and accurate.

The invention relates to the technical field of medical equipment, and provides a multi-modal lymphedema assessment and surgical navigation system based on image processing, which comprises a 3D scanning modeling module used for acquiring three-dimensional point cloud data of the body surface of a patient through structured light 3D scanning equipment, an iconography examination analysis module used for performing U-Net image segmentation on CT / MRI image data, and an image processing module used for processing the CT / MRI image data. The ultrasonic result calculation module is used for carrying out Canny edge detection on an ultrasonic image to determine the boundary of a surgical site, and the data integration and navigation generation module is used for realizing multi-modal data fusion through mutual information maximization, generating a surgical path based on FMM and carrying out real-time navigation in combination with an AR technology. According to the method, objective evaluation of lymphedema and accurate navigation of the operation are realized through accurate integration of multi-modal data and an image processing technology, the diagnosis and treatment accuracy is effectively improved, the operation risk is reduced, complications are reduced, and a scientific basis is provided for personalized treatment.

The invention belongs to the technical field of medical image intelligent diagnosis, and provides a breast cancer focus benign and malignant discrimination method based on a gated multi-expert mechanism. The method comprises the following steps: firstly, carrying out standardization and semantic preprocessing on a mammary gland X-ray image, a BI-RADS imaging report and structured clinical data, embedding age, mammary gland density and focus position information into a text template in a natural language form, and realizing unified expression of multi-modal input; secondly, extracting image features by utilizing a ResNet network and a simplified CLIP model, obtaining a text semantic vector by adopting a Bio-ClinicalBERT model, and establishing two sub-paths of a lump expert and a calcification expert in a Transform structure; further, an expert weight is dynamically generated through a gating routing mechanism, and soft routing fusion is executed; and finally, outputting benign and malignant results of the breast cancer focus by the binary classification module. According to the method, deep fusion and dynamic collaboration of the mammary gland X-ray image, the BI-RADS text and the clinical information are realized, and the accuracy and interpretability of breast cancer discrimination can be remarkably improved.

The invention relates to the technical field of medical data, in particular to an acute infection rapid diagnosis method, device and equipment, and the method comprises the steps: collecting an electronic medical record, blood routine, inflammation indexes, a microbiological detection result and imaging data of a target patient; respectively extracting corresponding target basic features based on the electronic medical record, the blood routine, the inflammation index, the microbiological detection result and the imaging data; based on the basic features, a convolutional neural network and a long-short-term memory network, target depth features are extracted; obtaining an acute infection prediction model, wherein the acute infection prediction model is obtained through model training; based on the target depth feature and the acute infection prediction model, the acute infection type of the target patient is determined, the accuracy and efficiency of diagnosis are further improved, scientific antibiotic use guidance is provided for doctors, waste of medical resources is reduced, and prognosis of the patient is improved.

The invention discloses a risk assessment system and method for analyzing abnormal sleep breathing of children based on CBCT dual-channel, and the method comprises the steps: collecting local three-dimensional image data of a maxillofacial region through CBCT, and extracting the sagittal area, volume and multi-dimensional angle and distance parameters of an upper airway through head correction and anatomical mark positioning; and constructing a logistic regression clinical prediction model combining single-factor and multi-factor logistic regression to realize risk prediction. Standardization and equal-interval sampling are synchronously carried out on image data, a multi-channel three-dimensional image data cube is generated, and deep learning classification is realized by inputting the multi-channel three-dimensional image data cube into a 3D ResNet network based on identical fast connection. And finally, performing weighted collaborative analysis on results of the two diagnosis channels, and outputting children obstructive sleep apnea risk classification. Through the parameter driving and image learning dual-channel fusion design, the accuracy and applicability of early screening and risk assessment of children obstructive sleep apnea are improved.

The invention relates to the technical field of medical instruments, and provides a steep pulse-based ablation system and method for liver cancer cells, and the system comprises a tumor feature recognition module which is used for obtaining imaging images of the liver cancer cells and recognizing tumor features, blood supply conditions and tumor centers of the liver cancer cells; the electrode inlet setting module is used for setting a safety boundary of liver cancer cells and setting an ablation area and a focus skin surface of the liver cancer cells based on the safety boundary and tumor characteristics; the ablation effect analysis module is used for calculating the electrode depth and the electrode angle of the liver cancer cells and analyzing the coverage effect of an ablation area; the electric field intensity identification module is used for analyzing cell membrane characteristics of the hepatoma cells, identifying the electroporation effect of the hepatoma cells and identifying the electric field intensity of the hepatoma cells; and the steep pulse ablation module is used for performing ablation treatment on the liver cancer cells in combination with the electric field intensity and a pulse generator to obtain an ablation result. The invention aims to avoid heat injury of surrounding tissues and improve the ablation effect of liver cancer cells.

The invention discloses an image text report quality control method and system based on LLM and a structured report, relates to the technical field of medical text report quality control, and solves the problems that a current text report quality control method is difficult to accurately extract key fields and is difficult to judge complex diagnostic reasoning logic. According to the technical scheme, the method is characterized in that iconography feature description related to a diagnosis conclusion is extracted from a text report, cue words are constructed according to a matched structured report template to analyze the iconography feature description into structured data, and an interface is called to fill the structured data into the structured report template. And obtaining a template diagnosis conclusion according to the built-in judgment logic of the structured report template, and comparing the template diagnosis conclusion with the current diagnosis conclusion to realize quality control auditing of the text report.

The invention relates to the field of medical imaging analysis, and discloses a bone trabecula feature construction method and system, and the method comprises the steps: preferentially extracting a fragile region from an obtained vertebral image sequence, and generating an initial feature set representing the stability of a fine-grained structure; performing coherence comparison on the initial feature set in a cross-slice dimension to obtain a composite feature vector capable of reflecting the vulnerability level of the structure; based on the composite feature vector, a connectivity change curve of the bone trabecula in a loading environment is extracted by adopting a multi-layer progressive clustering mode, and candidate bone trabecula units with potential micro-damage trends are marked; classifying the candidate bone trabecula units according to risk gradients, and reconstructing a bone trabecula local topology network with weight levels; and according to the local topology network, identifying high-risk nodes showing an imbalance tendency in a specific mechanical simulation period, and generating a feature construction path finally used for micro-fracture risk prediction. The method has the advantage of improving the micro-fracture risk prediction sensitivity.

The present invention belongs to the field of rehabilitation therapy technology and discloses a longitudinal analysis method and system for magnetic resonance imaging data of mild brain injury. The method extracts BOLD signals from the magnetic resonance imaging data of subjects; constructs a symmetric positive definite sparse brain functional connectivity network set of subjects based on sparse inverse covariance matrix estimation; determines the brain functional connectivity network dictionary and sparse coefficient matrix in kernel space based on Riemannian manifold sparse coding; performs spatial distribution analysis of brain functional connectivity atomic networks; and performs longitudinal analysis of magnetic resonance imaging data of mild brain injury. By analyzing the differences in the spatial distribution of these highly present brain functional connectivity atomic networks in the brain, the present invention digs out brain functional connectivity imaging markers for distinguishing the three mild brain injury rehabilitation treatment stages: acute phase, subacute phase, and complete recovery, thereby realizing longitudinal analysis of the mild brain injury rehabilitation process.

The invention discloses a multi-modal-based medical algorithm model construction system, which comprises a multi-modal data acquisition and preprocessing module for collecting data related to infectious lung diseases from multiple data sources, a multi-modal feature extraction module for carrying out deep feature extraction on different modal data, a multi-modal fusion module for carrying out multi-modal fusion on the multi-modal data, and a multi-modal feature extraction module for carrying out multi-modal fusion on the multi-modal data. A self-attention mechanism is adopted to integrate different modal features including images, texts, structuralization, time sequences and associated features extracted by a large language model, a multi-task learning and decision module constructs a multi-task learning framework, and two core tasks of infectious lung disease risk prediction and decision support based on the prediction are executed in parallel. According to the multi-modal-based medical algorithm model construction system, by integrating outpatient service and emergency treatment multi-time-point data and chest radiography imaging data, the death rate of infectious lung disease patients, the ICU admission rate and the breathing machine requirement are predicted in advance, and key decision nodes are preposed.

This invention discloses a system for predicting the efficacy of inflammatory bowel disease based on multimodal data, relating to the field of medical information processing. The system includes: a multimodal data acquisition module for receiving multimodal data; a text feature extraction module for determining text feature vectors using a text feature extraction model; an endoscopic intestinal mucosal feature recognition module for determining intestinal mucosal features using an endoscopic intestinal mucosal feature recognition model; an image feature recognition module for determining imaging features using an image feature recognition model; a pathological feature extraction module for determining pathological features using a pathological recognition model; and an efficacy prediction module for predicting the efficacy of biologics based on multimodal data features using a Transformer model and fully connected layers. Compared to existing technologies, this invention achieves efficacy prediction of biologics based on the fusion of clinical, pathological, endoscopic, and imaging multimodal features, improving the accuracy and clinical rationality of diagnostic and treatment decisions.

The invention provides a teenager temporomandibular joint disorder disease risk prediction method and device, and relates to the technical field of medical treatment, the method comprises the following steps: collecting basic information and health data of a target group, obtaining pathological data of temporomandibular joints of the target group through imaging examination, and preprocessing to generate comprehensive health data of a target patient; key factors related to the temporomandibular joint disorder disease are recognized, and feature variables influencing the temporomandibular joint disorder disease are screened out; and establishing a personalized TMD disease risk prediction model, performing TMD disease risk assessment on teenagers, performing risk grade division according to a prediction result, performing risk analysis according to different growth stages of the teenagers, and generating a personalized TMD risk assessment report. According to the method, the limitation of a traditional diagnosis means can be effectively overcome, earlier and more accurate TMD disease risk prediction is realized, and a more scientific health management tool is provided for clinic.

The invention relates to the field of medical information technology and medical data analysis, in particular to a VTE risk prediction method and system based on multi-modal data fusion and dynamic risk modeling, and the system comprises a first feature extraction module, a second feature extraction module, a two-stage risk assessment module and a final risk judgment module. According to the application, through integration of clinical diagnosis and treatment, iconography and genomics data, a deep learning algorithm, a dynamic weight adjustment mechanism and a graph neural network technology are utilized to realize accurate prediction of the VTE risk of the active-stage lung cancer patient. And meanwhile, the problem of cross-mechanism data distribution difference is solved by adopting transfer learning, and the generalization ability of the model is improved. The method can significantly improve the prediction accuracy, reduces the manual intervention demands, and optimizes the medical resource distribution efficiency.

The invention relates to the technical field of medical data analysis and machine learning. The invention discloses a far-end metastasis prediction system based on various examinations of a gastric cancer patient. The far-end metastasis prediction system comprises a data acquisition module for acquiring preoperative data of the gastric cancer patient; the preprocessing module cleans the normalized data; the feature selection module is used for screening overlapped features by using LassoCV, recursive feature elimination and Boruta algorithms to establish vectors; the model training module takes the feature vectors as input, and builds various models through automatic grid parameter searching and layered nested cross validation; the screening module selects an optimal model and performs multi-dimensional evaluation optimization; and the decision support module develops an online prediction or intranet generation result. The gastric cancer patient imaging, endoscope and blood multi-modal data are integrated, a multi-algorithm collaborative feature screening and machine learning model is constructed, through cross validation, parameter optimization and multi-dimensional evaluation, the prediction accuracy and reliability are improved, and the problem that traditional single inspection is insufficient in precision is solved.

The invention relates to the field of medical data analysis, in particular to a patient multi-dimensional feature clustering analysis method for a mental health database. The method comprises the following steps: acquiring multi-modal data of a patient from a mental health database, and performing missing value filling and standardization processing; constructing a biomarker pathway mapping knowledge domain, mapping the genetic information of the patient and biomarker data to nodes of the mapping knowledge domain, and extracting pathway activation features; fusing the dimensionality-reduced iconography and clinical features with the pathway features by adopting a multi-modal embedding method to obtain a comprehensive feature vector of the patient; grouping the patients by using a metric learning clustering algorithm and combining with pathway consistency constraints to obtain subtypes with biological significance; and further analyzing the characteristic contribution degree and the molecular mechanism difference of each subtype by adopting an interpretability method. According to the method, multi-modal data can be effectively integrated, the clustering precision and interpretability are improved, and a basis is provided for subtype recognition and personalized intervention of mental health diseases.

The invention discloses a control method of a diagnosis catheter and a data collection system, and relates to the technical field of medical instruments, the system comprises a sheath tube, a core part and a controller, the sheath tube is provided with a pressure sensor used for measuring an FFR value, the core part comprises an optical transducer and an acoustic transducer, the optical transducer is used for carrying out OCT imaging, the acoustic transducer is used for carrying out IVUS, and the controller is connected with the optical transducer. The optical transducer and the acoustic transducer are both arranged in the sheath tube, and the controller is arranged at the near end of the sheath tube and used for being held by medical staff. The FFR, the IVUS and the OCT are integrated in the diagnosis catheter, the FFR functional visual evaluation, the penetration depth of ultrasonic imaging and the high definition of optical imaging can be considered, the advantages of the FFR functional visual evaluation, the penetration depth of the ultrasonic imaging and the high definition of the optical imaging are complementary, strong combination of the function science and the imaging science is achieved, and disease diagnosis is better carried out.