137 results about "Preoperative imaging" patented technology

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

A robotic surgical system includes one or more robotic actuators configured to interact with biological tissue during a surgical procedure. A plurality of sensors include at least one of fiber Bragg grating sensors, piezoelectric strain sensors, or magnetostrictive sensors to capture real-time mechanical, elasticity, or deformation data from biological tissues. deep learning engine trained on a dataset comprising tissue mechanical responses across multiple tissue types, pathological states, and patient demographics. Pre-contact predictive adjustment profiles are generated for anticipated tissue interactions using preoperative imaging data registered to intraoperative coordinates. Intraoperative deviations are detected from predicted mechanical behavior and autonomously recalibrate actuator forces. Upcoming surgical maneuvers are anticipated based on prior task sequences and adjust actuator stiffness or damping properties in preparation for anticipated contact. An emergency override of actuator forces is provided via an anomaly detection module when real-time sensor data deviates beyond a threshold from the predicted safe mechanical response range. A feedback loop iteratively refines the deep learning engine during the procedure using supervised learning updates, anomaly detection, and reinforcement learning strategies. The reinforcement learning model is optionally shared across procedures to optimize distributed actuator force patterns for minimizing localized and cumulative tissue stress.

The invention relates to the field of cardiology department operations through vascular intervention, and discloses an interventional operation navigation system based on multi-modal image fusion, which comprises a preoperative three-dimensional reconstruction and planning module, which is mainly used for generating a three-dimensional model of a heart through preoperative image data, and planning the position and section of an intraoperative ICE ultrasonic probe based on the model, the intraoperative multi-modal fusion navigation module is mainly used for fusing a three-dimensional model generated before an operation with an intraoperative real-time image and providing accurate navigation support for a doctor; and the dynamic feedback optimization module is mainly used for updating a dynamic track in real time and performing safety protection. According to the invention, through the preoperative three-dimensional reconstruction and virtual path planning module, in combination with the improved 3D U-Net network and the high-precision segmentation algorithm, a three-dimensional model containing key anatomical markers is generated, 20 groups of ICE probe alternative paths are planned, seamless connection of preoperative three-dimensional anatomical information and intraoperative real-time images is realized, doctors can quickly call pre-planned paths, and the accuracy of the ICE probe anatomy is improved. Adjusting time during operation is shortened, and operation efficiency is improved.

The invention discloses a real-time image registration and path planning system in bronchoscope surgical navigation, relates to the technical field of image registration, and is used for solving the problems that in a thoracic and abdominal minimally invasive interventional operation, organ movement and deformation caused by breathing of a patient can cause obvious influence on the registration precision of a navigation system, and the navigation precision is poor. Space registration of preoperative images and an anatomical structure of a patient in an operation often has errors due to mismatching of breathing phase changes; multi-attitude data are fused, the respiration interference degree is comprehensively evaluated through double indexes, the problem that a traditional static deformation field cannot be matched with dynamic respiration is solved through respiratory cycle driving deformation prediction, displacement errors are corrected through tidal volume observation values, and B spline control point weights are reversely optimized through registration residual errors. A deformation field can adapt to breathing amplitude and frequency differences of different patients, online distillation learning is carried out by taking a registration error output by a filter as a teacher signal, and local residual deformation and chronic changes which are difficult to describe by a traditional global model are accurately captured.

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 relates to the technical field of navigation and positioning, and discloses an arthroscopic surgery navigation and positioning system based on artificial intelligence, which comprises a preoperative image processing and lesion analysis module for analyzing preoperative images, a multi-modal data fusion module for integrating image data from different sources in the surgery, and a navigation and positioning module for performing navigation and positioning on the integrated image data. The intra-operative real-time joint tracking and posture estimation module is used for tracking the joint position and soft tissue deformation in real time, the AI dynamic operation path planning module is used for intelligently optimizing the operation path, the robot auxiliary operation and automatic adjustment module is used for automatically adjusting the position and angle of an operation instrument, and the intra-operative AI voice and visual feedback module is used for providing real-time feedback. The operation data storage and intelligent learning module is used for storing and analyzing operation data. By means of the multi-modal data fusion technology, arthroscopic images, ultrasonic images and infrared images are combined, accurate visualization of an operation area is achieved, and the effect of improving operation accuracy and safety is achieved.

The invention provides a dynamic approach planning method and system for an acupotomy scope, and relates to the technical field of approach planning of the acupotomy scope, and the technical scheme is characterized in that real-time image data of an articular cavity is acquired; the real-time image data and the preoperative image data are subjected to registration analysis, and anatomical structure changes in the articular cavity are recognized; according to the recognized anatomical structure change in the joint cavity and a preset anatomical partition rule, anatomical partition information is dynamically updated, and updated anatomical partition information is obtained; according to the updated anatomical partition information, an approach path is set before an operation, real-time path adjustment is carried out, and an optimized needle knife mirror safe approach path is generated; and displaying the real-time image data, the updated anatomical partition information and the optimized safe approach path of the acupotomy mirror in an overlapping manner so as to guide a doctor to operate. The dynamic approach planning method and system for the acupotomy scope have the advantages that the dynamic change of an anatomical structure in an intra-operative joint cavity can be adapted, and safe operation of surgical instruments is ensured.

The invention provides a Slicer-based deep learning fat muscle segmentation method and system, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing preoperative CT image data of a target patient to generate skeletal muscle image features, subcutaneous adipose tissue image features and visceral adipose tissue image features; preprocessing the training sample set, adjusting a window width and a window level, performing normalization processing, and generating target training image data; training a preset fat muscle segmentation model based on a deep learning network based on the target training image data to generate a target fat muscle segmentation model; processing the skeletal muscle image features, the subcutaneous adipose tissue image features and the visceral adipose tissue image features based on a target adipose muscle segmentation model to generate target image segmentation information; and processing the target image segmentation information and the gene detection information of the target patient to generate preoperative early warning information of the target patient.

The invention provides a control method of an intestinal tumor resection robot combining preoperative images and intraoperative navigation, and relates to the technical field of medical instrument control. The method comprises the following steps: acquiring preoperative multi-modal image data of the intestinal tract of a patient, and performing three-dimensional reconstruction on the image data to generate an initial three-dimensional model; dividing a navigation area in the enteric cavity according to the initial three-dimensional model; based on the core area, collecting a dynamic image of the intestinal tract inner cavity in real time through intraoperative navigation equipment, extracting anatomical mark points in the dynamic image, registering the dynamic image with the initial three-dimensional model, updating tissue deformation parameters in the three-dimensional model, and obtaining an updated three-dimensional model; on the basis of the updated three-dimensional model, obstacles in the intestinal tract are recognized, and the minimum space distance between the obstacles and the robot tail end instrument is calculated; and according to the spatial distribution and the minimum spatial distance of the obstacles, an initial motion path of the robot tail end instrument is planned. According to the invention, the efficiency and accuracy of the intestinal tumor resection operation can be improved.

The invention discloses an augmented reality-based standard artificial temporomandibular joint installation navigation system, which comprises the following steps: S1, carrying out image registration on a preoperative CT image of a patient and a target operative site to obtain a registration result, and S2, updating the registration result in real time, and readjusting registration based on AR display equipment to obtain a registration result; virtual-actual transformation between the virtual image and the actual model is realized; s3, calibrating a surgical instrument, respectively aligning the surgical instrument with the TMJ surgical site and the virtual model, and tracking the position and posture of a patient entity in real time; and S4, navigating the artificial joint installation path in real time based on the updated position and posture of the patient entity.

The invention relates to the field of data processing, and discloses a neurosurgery operation data processing method for precision medical treatment, which is used for solving the problem that mark points cannot be captured due to the fact that a navigation camera is shielded when intraoperative data is collected. The method comprises the following steps: acquiring preoperative image data, intra-operative navigation image data and spatial information of an intra-operative reference mark point, constructing a preoperative three-dimensional navigation model through the preoperative image data, obtaining a navigation coordinate system, obtaining an intra-operative image frame sequence through the intra-operative navigation image data, carrying out spatial registration, extracting intra-operative navigation information, and obtaining an intra-operative image frame sequence. The method comprises the following steps: acquiring intraoperative navigation information, judging whether occlusion occurs in an operation or not according to the intraoperative navigation information, if yes, generating missing image frame data during occlusion through a dynamic trajectory prediction model, and effectively judging the missing image frame data according to a newest instrument space pose, thereby effectively improving data integrity and coherence, improving operation data accuracy and improving operation accuracy. The operation risk is reduced.

The invention discloses a three-dimensional visualization head and neck tumor preoperative imaging system based on deep learning. The method comprises the following steps: image analysis and preprocessing; carrying out image denoising and spatial-temporal feature extraction based on a deep learning convolutional neural network architecture, and introducing a u-Net model to realize image segmentation and semantic annotation; performing three-dimensional reconstruction and surface rendering by adopting a Marking Cubes algorithm, and realizing visual interaction image presentation and personalized operation by combining volume rendering and surface rendering; and data is output through multiple ports, and data viewing and interactive operation of a web terminal and a mobile terminal are supported. The method has the advantages that according to the technical scheme, through fusion of a three-dimensional attention mechanism and multi-phase data, the tumor segmentation Dice coefficient reaches 0.92 + / -0.03, and the method is superior to a traditional threshold segmentation method.

The invention relates to an interventional therapy target accurate positioning method and system based on an ultrasonic image. According to the method, a binary mask is generated by extracting an acoustic shadow area in an ultrasonic image, contrast enhancement parameters of different areas are dynamically adjusted based on the binary mask, and target spot edge details are enhanced while shadow area noise is suppressed; a shadow mask attention mechanism is combined to guide a neural network to accurately segment a target point probability graph, a segmentation boundary is evolved and optimized by integrating a level set constrained by a shadow region shape, and local smooth compensation is performed on registration deviation of a three-dimensional reconstruction model and a preoperative image by using a shadow perception deformation field correction strategy; and finally generating a puncture path for avoiding the dangerous structure. The problems of fuzzy target boundary, segmentation error accumulation and deformation compensation misalignment of a traditional method under acoustic shadow interference are effectively solved, and the pertinence of image enhancement, the accuracy of target positioning and the safety of an intervention path are remarkably improved.

The invention provides an intraoperative path optimization method and system based on solid tumor preoperative image analysis, and the method comprises the steps: constructing a three-dimensional model of a tumor and peripheral blood vessels and nerves according to a preoperative image, and carrying out the dissection risk marking of the three-dimensional model through a deep learning model, generating an anatomical risk image with risk gradient value distribution; taking the minimum intraoperative bleeding amount and the highest tumor excision rate as a reward function, combining the three-dimensional model, an anatomical risk image and historical similar case data, and simulating multiple operation paths based on an adversarial reinforcement learning algorithm to obtain an optimal operation path; the method comprises the following steps: acquiring the pose, ultrasonic image and fluorescence image of a surgical instrument in real time during surgery, matching the preoperative image with the intraoperative image based on a biomechanical model driven non-rigid registration algorithm, predicting organ displacement, and correcting an optimal surgical path, thereby avoiding accidental injury of key tissues, ensuring that an infiltration area is brought into the surgical path in time, and improving the surgical efficiency. Residues are reduced, and static planning failure is avoided.

The invention discloses an intraoperative ultrasound and preoperative image non-rigid registration method based on topological high-order MRF (Markov Random Field). The method comprises the following steps: firstly, taking iUS as a target image T, taking a preoperative image as a floating image M, and respectively processing the target image T and the floating image M; then according to the Markov property of the MRF, firstly defining a neighborhood system N and a sub-group C, and then respectively defining context constraints of the processed target image T and the floating image M through the defined neighborhood system N and the defined sub-group C; establishing a non-rigid registration energy function of the target image T and the floating image M based on the topological high-order MRF on the basis of the defined neighborhood system N and the sub-group C; and finally, realizing calculation of a non-rigid registration energy function of the target image T and the floating image M based on the topological high-order MRF based on a multi-resolution additional-variable-free reduced-order energy optimization algorithm. According to the method, the intraoperative dynamic decision-making capability of surgical navigation is improved, precise three-dimensional visual surgical guidance is provided for doctors clinically, and the surgical precision is improved.

The invention relates to the technical field of image processing, and particularly discloses a spine motion real-time tracking system and method, and the system comprises a vertebra registration module which carries out the registration of an image of a target vertebra and a preoperative image; the camera module is used for acquiring a depth image and a visible light image of the target vertebra in the operation after registration, and reconstructing according to the depth image to obtain a three-dimensional surface point cloud of the target vertebra in the operation; the feature point recognition module is used for performing two-dimensional feature point recognition on the visible light image acquired by the camera module and acquiring three-dimensional associated points in the three-dimensional surface point cloud of the target vertebra in the operation; the optical flow tracking module is used for setting position templates of the two-dimensional feature points and the corresponding three-dimensional association points, and performing optical flow tracking between the current frame image and the previous frame image acquired by the camera module according to the position templates of the two-dimensional feature points and the corresponding three-dimensional association points; and the motion tracking module calculates the motion amplitude of the target vertebra according to optical flow tracking. According to the method, real-time motion tracking can be directly carried out on the target vertebra, the calculation complexity is reduced, and the algorithm instantaneity is ensured. And the calculation complexity is low, and the algorithm instantaneity is ensured.

The invention provides a spinal pedicle screw implantation system based on a multi-modal image and MR navigation, and relates to the technical field of screw implantation, the system comprises a preoperative image processing module used for identifying bony characteristics of a patient and delimiting a screw implantation safety boundary according to the bony characteristics; and the bone image fusion navigation module is used for analyzing to obtain anatomical data of the patient, and calculating a screw implantation path according to a crown porcupine optimization algorithm in combination with bony characteristics. And the body position correction module is used for calculating the body position deviation value and synchronously adjusting the screw implantation path according to the body position deviation value and the screw implantation safety boundary. And the path risk early warning module is used for calculating the distance between the surgical tool and the key tissue in real time, and performing collision risk assessment and early warning. And the postoperative evaluation module is used for quantitatively evaluating the screw implantation precision and generating a postoperative report. According to the method, more accurate positioning can be provided for screw implantation, the accuracy of a screw implantation path is ensured, early warning is performed through extraction, a doctor is reminded to take measures in time, and operation safety is ensured.

The invention provides a training method of a CT and DSA real-time automatic fusion registration system for structural heart disease intervention and a storage medium. The training method comprises the following steps: establishing a registration system data set; establishing an initial registration system; based on the registration system data set, training an initial registration system to obtain a trained CT and DSA intraoperative registration system; and inputting the preoperative CT image data and the intraoperative real-time DSA image data of the person to be identified into the trained CT and DSA intraoperative registration system to obtain an intraoperative real-time fusion navigation image and intraoperative risk graphical prompt information. According to the method, cross-modal and cross-time-point dynamic registration can be realized, higher-precision intraoperative navigation support is provided for structural heart disease intervention, the operation safety is improved, and radiation exposure is reduced.

The invention relates to a gynecological pelvic and abdominal lymphatic cleaning prediction model training method and a lymph node model.The training method comprises the steps that preoperative image data and intraoperative instrument operation parameters of historical patients are subjected to modeling processing, and a dynamic mechanics-fluid coupling lymph node model based on bionic materials is constructed; mechanical deformation and fluid flow of a lymphatic system in various clinical scenes are simulated; performing data acquisition processing on the used lymph node model, and quantifying the dynamic influence of the surgical operation on the lymphatic system; the relevance between historical operation and injury risk is deeply learned, and the lymphatic vessel rupture probability is output; real-time feedback control processing is carried out based on the lymphatic vessel rupture probability, a training correction instruction and a risk thermodynamic diagram are generated, and medical workers are guided to optimize surgical skills. Through fusion analysis of the high-fidelity bionic model and multi-modal data, dynamic simulation of the mechanical-fluid coupling effect in the operation and real-time quantification of the operation risk are achieved, and the accuracy and clinical transformation value of operation training are improved.

The invention provides a dental implant data monitoring analysis method based on deep learning, and the method comprises the steps: collecting the multi-modal structural data of a patient, such as preoperative images, clinical examination and surgical parameters, carrying out the standardized preprocessing, and inputting the data into a deep neural network model with a double-branch structure, thereby achieving the extraction of the implant failure risk score and local causal association strength; on the basis of model output, a dynamic individualized causal map is constructed, key clinical intervention variables serve as regulation and control nodes, anti-fact disturbance simulation is carried out, variable intervention sensitivity is quantified, and a key causal path is analyzed; according to the method, a priority intervention suggestion list and an auxiliary decision report are generated, the causal atlas can be adaptively optimized according to actual follow-up feedback, long-term closed-loop evolution is realized, the accuracy of implant failure risk prediction and the pertinence and operability of an intervention scheme are improved, and clinical individualized treatment decision support is enhanced.

Various systems and methods are provided for generating an initial transformation matrix for registering intraoperative imaging data with preoperative imaging data. Position data of a tracked instrument that is moved in a trajectory corresponding to a predefined shape relative to a patient may be received. A first set of coordinates of the position data corresponding to the predefined shape may be determined. Preoperative imaging data of the patient may be received. A second set of coordinates of the preoperative imaging data corresponding to the predefined shape may be determined. The initial transformation matrix may be generated based on the first set of coordinates and the second set of coordinates. The intraoperative imaging data may be registered with the preoperative imaging data based on the initial transformation matrix.

The invention discloses a pig brain deep brain stimulation DBS electrode planning and reconstruction system and method, and belongs to the technical field of medical image processing and neurosurgery path planning. An automatic registration module based on a magnetic resonance (MR) image and a pig brain standard map is used for accurately identifying a target brain region; the in-target optimal electrode trajectory planning module based on principal component analysis (PCA) and the electric field simulation module based on a Hodgkin-Huxley model are used for optimizing the treatment effect and predicting the tissue activation volume (VTA); and a multi-modal hybrid registration module based on postoperative computed tomography (CT) and preoperative MR images, which is used for accurately reconstructing the actual position of the electrode. According to the invention, through combination of automatic and data-driven planning and accurate postoperative verification, the accuracy, reliability and efficiency of electrode implantation in pig brain DBS research are significantly improved.

The invention discloses a complication prediction method and system based on an SHAP-random forest, and relates to the technical field of medical data mining. The method comprises the following steps: acquiring preoperative CT images and puncture path planning data, and calculating risk factors; performing numerical value standardization, nonlinear transformation and interactive feature generation on the risk factors to obtain feature vectors, calculating mutual information scores of feature values in the feature vectors, if the mutual information scores are greater than an experience threshold, retaining the feature values, and after traversal is finished, obtaining updated feature vectors; on the basis of a random forest model, taking the updated feature vector as an input value, and calculating a complication probability; quantizing the contribution degree of the characteristic value based on a Shapley value; according to clinical indexes, the risk threshold is dynamically corrected, the complication risk level is divided, and complication prediction is completed, the problems that a static threshold ignores the blood coagulation state difference of a patient and a black box model cannot provide a decision basis are solved, and the complication misjudgment probability is reduced.

The invention provides a method, a device and equipment for identifying path deviation in a screw implanting process, and belongs to the field of data processing. The method comprises the steps that target stress information and pose information of the tail end of a target robot used for implanting a target screw are obtained, and the tail end of the target robot holds the target screw; determining a current implantation path of the target screw according to the target stress information and the pose information; obtaining a target implantation path, wherein the target implantation path is generated based on preoperative image data of a patient; and determining a path offset condition according to the current implantation path and the target implantation path, wherein the path offset condition is used for indicating whether path offset exists or not. The method and the device are used for recognizing path deviation in the nail implanting process based on a robot, and the path precision judgment capability and the operation safety of a robot operation system are improved. And meanwhile, additional visual navigation equipment is not needed, so that the system complexity and cost are reduced.

The invention provides an orthopaedic implantation post-operation X-ray film simulation method and system based on a preoperative CT image, a medium, a program product and a terminal, and aims to accurately convert and present results of three-dimensional segmentation, key point identification and prosthesis positioning based on preoperative CT into a standard post-operation X-ray film simulation effect conforming to clinical routine. A doctor can intuitively and clearly foresee the form projection and the specific spatial position of selected prostheses including an acetabular cup, a lining, a ball head, a femoral stem and the like on a post-operation standard orthotopic X-ray film and the accurate relative anatomical relation between the prostheses and key bony markers of the pelvis and the femur in the pre-operation planning stage. By providing an intuitive and quantitative post-operation X-ray film simulation effect, a doctor is effectively assisted in selecting an optimal operation scheme and adjusting setting of parameters such as the size, the position and the angle of an implant, the predictability and the accuracy of operation planning are improved, and dependence on pure experience is reduced.

A guide for placing a landmark(s) in a patient tissue comprises a central portion defining at least one guiding bore configured to receive and guide a tool for placing the at least one landmark in the patient tissue. Extended portions project away from the central portion, each of the extended portions having respective lower base surfaces contoured to mate with the patient tissue, at least the respective lower base surfaces being customized as a function of preoperative imaging of the patient tissue, whereby the guide is specific to the patient.

The invention belongs to the technical field of medical equipment, and discloses a non-image hip replacement navigation system and method based on transverse ligament positioning, and the system comprises a navigation positioning module, a human body horizontal plane and central axis positioning module, a transverse ligament spatial position positioning module and an acetabulum reconstruction module; the navigation positioning module is used for positioning the pose of the surgical instrument in real time; the human body horizontal plane and central axis positioning module is used for calculating an abduction angle and a front inclination angle; the transverse ligament spatial position positioning module is used for planning the shape and position of the acetabular prosthesis; and the acetabulum reconstruction module is used for implanting a prosthesis according to an intraoperative plan. According to the method, the position of the prosthesis is planned by positioning the acetabular transverse ligament, accurate intraoperative guidance is provided for acetabular grinding and prosthesis placement of a doctor, and the problems that an existing navigation system highly depends on preoperative images and intraoperative registration, so that hip replacement is complex in operation, low in efficiency and the like are solved.

The application discloses a risk prediction method for subsidence of an intervertebral fusion cage after lumbar interbody fusion, and comprises the following steps: acquiring preoperative image data and postoperative image data within one week after operation of a target patient, wherein the preoperative image data and the postoperative image data comprise multiple image features reflecting local anatomical morphology, sagittal balance and bone conditions of the spine; inputting the multiple image features into a pre-trained subsidence risk prediction model to obtain a risk probability of the target patient suffering from the subsidence of the intervertebral fusion cage; wherein the subsidence risk prediction model is obtained by screening a core prediction feature set through a "funnel type" feature screening process of fusing preoperative native anatomical parameters and postoperative early image parameters, and by training the core prediction feature set by using a machine learning algorithm based on a historical patient data set containing subsidence samples and non-subsidence samples; and the application reduces the occurrence of postoperative complications.

The invention relates to a calibration method, device and equipment, a medium and a program product. A first part of the image acquisition equipment is provided with a first tool, the fixing position of the first tool and the relative position of the rotation center of the image acquisition equipment are not changed, and a second tool is arranged in the imaging range of the image acquisition equipment. Based on a marker used for calibrating the position of the second tool and a marker used for imaging under the image acquisition equipment in the second tool; the method comprises the following steps: acquiring a conversion relation between a preoperative image coordinate system and a calibration navigator coordinate system; and obtaining a calibration relation between the preoperative image coordinate system and a first tool coordinate system on the image acquisition equipment based on the first tool and a conversion relation between the preoperative image coordinate system and a calibration navigator coordinate system. By adopting the method, the registration efficiency of multiple medical images can be improved.

The invention discloses a PCI (Peripheral Component Interconnect) drug balloon postoperative effect prediction method, device and equipment based on a multi-modal image and a storage medium. The method comprises the following steps: firstly, collecting historical preoperative digital subtraction angiography (DSA) images of a plurality of patients, corresponding intracavitary image data and postoperative effect label data; further extracting vascular morphological parameters of a target lesion segment from the DSA image, extracting plaque microstructure features from the intracavity image, combining the vascular morphological parameters, the plaque microstructure features, clinical data of a patient and parameters of a drug balloon instrument into an input feature vector, and taking postoperative effect label data as a regression target; training an ensemble learning regression model; and finally, for a to-be-evaluated patient, acquiring a preoperative DSA image and an intracavity image, extracting features in the same way, inputting the features into the trained model, and outputting an immediate and / or long-term vasodilation gain prediction value of the patient after the PCI drug balloon operation so as to improve objectivity and reliability of a DCB preoperative decision.