Registration method and device in cardiac surgery and computer equipment

Abstract

The invention provides a registration method and device in a cardiac surgery and computer equipment, which are used for improving the safety and accuracy of the surgery. The registration method comprises the following steps: acquiring an electrocardiosignal and multi-modal physiological data of a surgical patient, and performing cardiac time phase division on a cardiac cycle based on the electrocardiosignal and the multi-modal physiological data; collecting multi-angle image data of the heart under each cardiac time phase, and synchronously collecting physiological parameter data; performing three-dimensional reconstruction based on the multi-angle image data corresponding to each cardiac time, and mapping the physiological parameter data corresponding to each cardiac time to a corresponding three-dimensional reconstruction result to obtain a multi-temporal three-dimensional heart model library; the method comprises the following steps: acquiring real-time electrocardiosignals and real-time multi-modal physiological data of a surgical patient in a surgical process to identify a current cardiac time phase, selecting a current time phase three-dimensional heart model from a multi-time phase three-dimensional heart model library according to the current cardiac time phase, and performing surgical registration by using the current time phase three-dimensional heart model.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a registration method, apparatus and computer equipment for cardiac surgery. Background Technology

[0002] In the field of interventional cardiac surgery, three-dimensional cardiac models are crucial tools for surgeons to plan surgical procedures, simulate surgical operations, and achieve precise instrument positioning. Traditional three-dimensional cardiac simulation technology primarily constructs static models, which can only present the morphological characteristics of the heart at a fixed moment, completely ignoring the heart's essential nature as a dynamic organ. During pacing, the heart continuously undergoes contraction and expansion, during which not only does the overall size of the heart and the volume of each chamber change significantly, but the diameter and direction of blood vessels such as the coronary arteries also fluctuate dynamically.

[0003] This disconnect between static models and the actual dynamic physiological state of the heart presents significant challenges for surgeons when simulating and registering core surgical procedures such as guidewire navigation and stent implantation. For example, a guidewire path planned on a static model may fail to pass smoothly due to vascular stenosis caused by cardiac contraction; and stent sizes selected based on static vessel diameters may result in poor adhesion between the stent and the vessel wall during cardiac expansion, increasing the risk of postoperative thrombosis or in-stent restenosis. Summary of the Invention

[0004] In view of this, the present disclosure provides a registration method, device and computer equipment for cardiac surgery, which can overcome the limitations of traditional static modeling, realize accurate replication of dynamic cardiac state, reduce the difficulty of surgical operation and improve surgical safety and accuracy.

[0005] Firstly, a registration method in cardiac surgery includes: Acquire electrocardiogram (ECG) signals and multimodal physiological data of surgical patients, and divide the cardiac cycle into cardiac phases based on the ECG signals and multimodal physiological data to obtain multiple cardiac phases; Multi-angle imaging data of the heart are acquired at each of the multiple cardiac phases, and corresponding physiological parameter data are acquired simultaneously. Three-dimensional reconstruction is performed based on multi-angle image data corresponding to each cardiac time, and physiological parameter data corresponding to each cardiac time is mapped to the corresponding three-dimensional reconstruction result to obtain a multi-temporal three-dimensional heart model library; the multi-temporal three-dimensional heart model library includes three-dimensional heart models corresponding to each cardiac time, and the three-dimensional heart model includes the three-dimensional reconstruction result and physiological parameter data; During the operation, real-time electrocardiogram (ECG) signals and real-time multimodal physiological data of the surgical patient are collected. Based on the real-time ECG signals and real-time multimodal physiological data, the current cardiac phase is identified. According to the current cardiac phase, the corresponding three-dimensional heart model is selected from the multi-phase three-dimensional heart model library to obtain the current phase three-dimensional heart model. Surgical registration was performed using the current phase three-dimensional cardiac model.

[0006] In one implementation, acquiring multimodal physiological data of a surgical patient includes: Hemodynamic parameters of the surgical patient were collected using a blood flow monitoring device, and intravascular pressure and blood flow velocity values ​​at different times were recorded to generate hemodynamic data. The respiratory signals of the surgical patient are collected using a respiratory monitoring device, and the respiratory rate and respiratory amplitude are recorded to generate respiratory data. The body temperature data of the surgical patient is collected using a body temperature monitoring device, and changes in body temperature are recorded to generate body temperature data. The multimodal physiological data are obtained based on the hemodynamic data, the respiratory data, and the body temperature data.

[0007] In one implementation, the cardiac cycle is divided into cardiac phases based on the electrocardiogram signal and the multimodal physiological data, including: Waveform features are extracted from the electrocardiogram (ECG) signal to generate ECG waveform feature data; The cardiac cycle is determined based on the electrocardiogram waveform feature data, where the cardiac cycle is the time interval between two adjacent R wave peaks; The peak time of intravascular pressure and the time of blood flow velocity change are extracted from the multimodal physiological data as auxiliary phase markers. Based on the cardiac cycle and the auxiliary phase markers, each cardiac cycle is divided into early systole, mid-systole, late systole, early diastole, mid-diastole and late diastole to obtain the multiple cardiac phases.

[0008] In one implementation, physiological parameter data corresponding to each cardiac event are collected synchronously, including: The intravascular pressure value in the multimodal physiological data is recorded synchronously at the image acquisition time of each cardiac phase. The intravascular pressure value is associated with the corresponding cardiac phase and acquisition angle to generate time-related pressure data. The blood flow velocity values ​​in the multimodal physiological data are recorded synchronously at the image acquisition time of each cardiac phase, and the blood flow velocity values ​​are associated with the corresponding cardiac phase and acquisition angle to generate time-related blood flow data; Elastic deformation data of the blood vessel wall is recorded synchronously at the time of image acquisition for each cardiac phase. The elastic deformation data is associated with the corresponding cardiac phase and acquisition angle to generate the associated elastic data. Based on the time-related pressure data, the time-related blood flow data, and the time-related elasticity data, the physiological parameter data corresponding to each cardiac time are obtained.

[0009] In one embodiment, the multi-angle imaging data includes X-ray multi-angle images and intravascular ultrasound multi-section images; Three-dimensional reconstruction was performed based on multi-angle image data of each cardiac phase, including: The heart contour is extracted from the X-ray multi-angle images at the same cardiac phase. Based on the heart contour, an image segmentation algorithm is used to extract the boundaries of the heart chambers and the boundaries of the coronary arteries in each angle image to generate multi-angle contour data. Based on the multi-angle contour data and the acquisition angle parameters of each image, the three-dimensional volume data of the heart is reconstructed using a back projection algorithm to generate preliminary three-dimensional volume data. The contours of the vascular wall and plaques in the intravascular ultrasound multi-section images under the same cardiac phase are extracted, and a three-dimensional mesh model of the blood vessel is reconstructed along the vascular axis using a shape interpolation algorithm to generate a three-dimensional mesh model of the blood vessel. The 3D vascular mesh model is registered and fused to the corresponding vascular region in the preliminary 3D volume data, and surface optimization processing is performed to obtain the 3D reconstruction result of the cardiac phase.

[0010] In one implementation, mapping the physiological parameter data corresponding to each cardiac event to the corresponding three-dimensional reconstruction result includes: The three-dimensional spatial coordinates and corresponding physiological parameter values ​​of each acquisition point are extracted from the physiological parameter data corresponding to each heartbeat, and a discrete physiological parameter point cloud is constructed to generate physiological parameter point cloud data. Based on the point cloud data of the physiological parameters and the grid node coordinates of the three-dimensional reconstruction results of the cardiac phase, the radial basis function interpolation method is used to calculate the physiological parameter values ​​at each grid node, generating a continuously distributed physiological parameter field. The physiological parameter field is mapped onto the surface of the three-dimensional reconstruction result of the cardiac phase using color encoding to obtain a three-dimensional model of the cardiac phase with physiological parameter distribution.

[0011] In one implementation, identifying the current cardiac phase based on the real-time electrocardiogram signal and real-time multimodal physiological data includes: Real-time R-wave peak detection and waveform feature extraction are performed on the real-time electrocardiogram signal to generate real-time electrocardiogram feature data; Calculate the relative position of the current moment in the cardiac cycle based on the real-time ECG feature data, and generate the cycle relative position; Based on the real-time electrocardiogram feature data and the intravascular pressure and blood flow velocity values ​​extracted from the real-time multimodal physiological data, a similarity match is performed with the physiological parameter data of each cardiac phase in the multi-temporal three-dimensional heart model library to generate the matching degree. The current cardiac phase is determined based on the relative position of the cycle and the time-matching degree.

[0012] In one implementation, surgical registration is performed using the current temporal three-dimensional cardiac model, including: The real-time position information of the interventional device is obtained, and the real-time position information of the interventional device is registered with the current phase three-dimensional cardiac model to obtain the registration result; Based on the registration results, the current phase three-dimensional cardiac model and the position of the interventional device are displayed in real time on the surgical navigation interface.

[0013] Secondly, embodiments of this disclosure provide a registration device for cardiac surgery, comprising: The acquisition module is used to acquire the electrocardiogram (ECG) signal and multimodal physiological data of the surgical patient, and to divide the cardiac cycle into cardiac phases based on the ECG signal and the multimodal physiological data to obtain multiple cardiac phases; The first acquisition module is used to acquire multi-angle image data of the heart at each of the multiple cardiac phases, and simultaneously acquire corresponding physiological parameter data. The 3D reconstruction module is used to perform 3D reconstruction based on multi-angle image data corresponding to each cardiac time, and to map the physiological parameter data corresponding to each cardiac time to the corresponding 3D reconstruction result to obtain a multi-temporal 3D heart model library; the multi-temporal 3D heart model library includes 3D heart models corresponding to each cardiac time, and the 3D heart model includes the 3D reconstruction result and physiological parameter data; The second acquisition module is used to acquire real-time electrocardiogram signals and real-time multimodal physiological data of the surgical patient during the operation. Based on the real-time electrocardiogram signals and real-time multimodal physiological data, the current cardiac phase is identified. According to the current cardiac phase, the corresponding three-dimensional heart model is selected from the multi-phase three-dimensional heart model library to obtain the current phase three-dimensional heart model. The registration module is used to perform surgical registration using the current phase three-dimensional cardiac model.

[0014] Thirdly, embodiments of this disclosure also provide a computer device, the computer device including a processor and a memory, the memory storing computer-readable instructions, and the processor being used to read the computer-readable instructions to execute the registration method described in any of the above embodiments.

[0015] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing computer instructions, which, when executed, are used to implement the registration method described in any of the above embodiments.

[0016] This disclosure utilizes electrocardiogram (ECG) signals combined with multimodal physiological data to segment cardiac phases. For each cardiac phase, multi-angle image data and physiological parameter data are collected. Based on this, a multi-temporal 3D cardiac model library is constructed that can completely cover the dynamic changes of the heart within a cardiac cycle, achieving synchronization between the 3D cardiac model state and the actual cardiac motion. This overcomes the limitations of traditional static modeling and enables accurate replication of the dynamic cardiac state. Furthermore, this disclosure is not limited to ECG signals but integrates multimodal physiological data for phase segmentation and model mapping, avoiding phase identification errors caused by individual patient differences (such as abnormal ECG signals in patients with myocardial infarction). Moreover, mapping physiological parameter data (such as intravascular pressure and blood flow velocity) to the 3D reconstruction results allows the 3D cardiac model to not only contain morphological information but also reflect the cardiac functional state, thus providing a more comprehensive decision-making basis for surgical registration (such as determining blood flow resistance at vascular stenosis sites to guide guidewire push force). During the surgery, the current cardiac phase is identified by real-time electrocardiogram signals and multimodal physiological data. The corresponding three-dimensional heart model is then called from the multi-temporal three-dimensional heart model library for registration, thereby matching the current morphology of the heart in real time, reducing registration errors and improving the accuracy of instrument operation. Attached Figure Description

[0017] Figure 1 A flowchart of a registration method in cardiac surgery provided by an embodiment of this disclosure; Figure 2 This is a schematic diagram illustrating multi-angle image acquisition in an embodiment of this disclosure; Figure 3 A schematic diagram of a registration device 300 in cardiac surgery provided in an embodiment of this disclosure; Figure 4 This is a schematic diagram of an electronic device 400 provided in an embodiment of the present disclosure. Detailed Implementation

[0018] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0019] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0020] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0021] Furthermore, the symbol “ / ” in this disclosure indicates that there is an “or” relationship between the related objects before and after the symbol, or that the related objects have an exemplary relationship that can coexist.

[0022] Studies have found that because patients require anesthesia during surgery, their breathing is very even, and their heartbeat can be considered a regular movement. Based on this, a dynamic three-dimensional cardiac model can be established to support interventional cardiac surgery. This disclosed embodiment first combines electrocardiogram (ECG) signals with multimodal physiological data to achieve precise segmentation of cardiac phases. Then, by fusing multi-angle imaging data and physiological parameter data at each cardiac phase, a multi-phase three-dimensional cardiac model library is constructed, including three-dimensional cardiac models corresponding to each cardiac phase. In this way, precise and real-time registration of surgical instruments such as guidewires with the three-dimensional cardiac model can be achieved during surgery, providing effective support for the safe and precise implementation of interventional cardiac surgery.

[0023] The solutions of the present disclosure will be further described in detail below through specific embodiments.

[0024] like Figure 1 As shown, a registration method in cardiac surgery provided by an embodiment of this disclosure includes: S101: Acquire the electrocardiogram (ECG) signal and multimodal physiological data of the surgical patient, and divide the cardiac cycle into cardiac phases based on the ECG signal and the multimodal physiological data to obtain multiple cardiac phases.

[0025] Here, because the heart continuously contracts and relaxes with the cardiac cycle, its morphology (such as blood vessel diameter and cardiac chamber volume) and physiological state (such as blood flow velocity and intravascular pressure) vary greatly at different times. Therefore, by dividing the cardiac phase by combining electrocardiogram signals with multimodal physiological data, the continuous cardiac motion can be decomposed into multiple time slices, which lays the foundation for subsequent time-segmented modeling.

[0026] Specifically, electrocardiogram (ECG) signals, such as electrocardiograms, can be used to extract waveform features like the R-wave peak. Multimodal physiological data can include parameters such as peak intravascular pressure and the timing of blood flow velocity changes. Using ECG signals as the core time reference and combining them with multimodal physiological data as auxiliary markers allows for precise definition of the time boundaries of each phase. This avoids phase segmentation deviations caused by errors from a single signal (such as ECG signal interference), ensuring that subsequently acquired images and physiological data correspond to the same cardiac motion state.

[0027] In one embodiment, acquiring multimodal physiological data of a surgical patient may include: collecting hemodynamic parameters of the surgical patient using a blood flow monitoring device, recording intravascular pressure and blood flow velocity values ​​at different times, and generating hemodynamic data; collecting respiratory signals of the surgical patient using a respiratory monitoring device, recording respiratory rate and respiratory amplitude, and generating respiratory data; collecting body temperature data of the surgical patient using a body temperature monitoring device, recording body temperature changes, and generating body temperature data; and obtaining the multimodal physiological data based on the hemodynamic data, the respiratory data, and the body temperature data.

[0028] Here, hemodynamic data, besides serving as auxiliary phase markers to improve the accuracy of cardiac phase segmentation, can also provide data support for correcting the impact deviation of blood flow on the guidewire during registration. Respiratory data can be used to correct for the slight effects of respiratory movements on cardiac morphology, reducing the morphological differences between the model and the actual heart, and can help select stable image acquisition time windows to avoid image blurring or registration errors caused by respiratory interference. Body temperature data can be used to correct changes in the physical properties (such as elastic modulus) of the guidewire caused by temperature in real time, ensuring the accuracy of the guidewire mechanical model, and can also serve as a reference indicator for the stability of the patient's physiological state, providing timely warnings of the potential impact of abnormal body temperature on surgical safety.

[0029] In one embodiment, dividing the cardiac cycle into cardiac phases based on the electrocardiogram (ECG) signal and the multimodal physiological data may include: extracting waveform features from the ECG signal to generate ECG waveform feature data; determining the cardiac cycle based on the ECG waveform feature data, wherein the cardiac cycle is the time interval between two adjacent R-wave peaks; extracting the peak intravascular pressure time and the time of blood flow velocity change from the multimodal physiological data as auxiliary phase markers; and dividing each cardiac cycle into multiple cardiac phases according to the cardiac cycle and the auxiliary phase markers.

[0030] Here, waveform feature extraction of the electrocardiogram (ECG) signal may include R-wave peak detection and feature extraction of the P wave, QRS complex, and T wave. The cardiac cycle may also be referred to as the RR interval, where RR corresponds to the peak values ​​of two adjacent R waves in the ECG signal, and the interval refers to the time interval between the two peak values.

[0031] In one implementation, each cardiac cycle can be divided into several different phases, such as early systole, mid-systole, late systole, early diastole, mid-diastole, and late diastole, based on the cardiac cycle and auxiliary phase markers. Each cardiac phase can correspond to the specific functional state and morphological characteristics of the heart in these phases.

[0032] In another implementation, to achieve higher precision modeling and registration, the number of cardiac phases in the cardiac cycle can be determined based on the ECG signal acquisition frequency. For example, if the ECG signal is acquired at 1000Hz, a cardiac cycle can be divided into 1000 equal parts, resulting in 1000 cardiac phases, each with a corresponding time boundary. In practice, the number of cardiac phases can also be determined by combining the ECG signal acquisition frequency with the actual needs of the surgery (such as the precision requirements of the surgery).

[0033] S102: Collect multi-angle image data of the heart at each of the multiple cardiac phases, and simultaneously collect the corresponding physiological parameter data.

[0034] Here, multi-angle imaging data and physiological parameter data of the heart can be simultaneously acquired at each cardiac phase, matching morphological and physiological dual-dimensional data for each cardiac phase. For example, within the time window of each cardiac phase, multi-angle images can be acquired using equipment such as C-arm X-rays and intravascular ultrasound to capture subtle morphological features of the heart at that cardiac phase, such as the difference between the shrinking of vessel diameter during systole and the increase in vessel diameter during diastole. This imaging data is the core material for subsequent 3D reconstruction. In addition, physiological parameter data corresponding to the cardiac phase are recorded simultaneously and correlated with the multi-angle imaging data. The combination of multi-angle imaging data and physiological parameter data allows the subsequently reconstructed 3D heart model to not only include morphological structure but also integrate functional status (such as blood flow velocity and pressure distribution in a certain segment of the vessel at a certain time phase), thereby providing a more comprehensive decision-making basis for surgical registration.

[0035] In practical implementation, the multi-angle imaging data here may include, for example, X-ray multi-angle images and intravascular ultrasound multi-section images. In this case, acquiring multi-angle imaging data at each of the multiple cardiac phases includes: generating an image acquisition trigger signal based on the time boundaries of each cardiac phase, and sending the image acquisition trigger signal to each image acquisition device within a preset time window of each cardiac phase to perform the following image acquisition operation: The C-arm X-ray device is controlled to rotate at preset angle intervals to acquire X-ray projection images at different angles, generating multi-angle X-ray images; and an intravascular ultrasound probe is controlled to move axially within the coronary artery to acquire ultrasound images at different cross sections, generating multi-section intravascular ultrasound images; based on the multi-angle X-ray images and the multi-section intravascular ultrasound images, and marking the cardiac phase and acquisition angle corresponding to each image, the multi-angle image data is obtained.

[0036] In one embodiment, the physiological parameter data may include, for example, intravascular pressure values, blood flow velocity values, and vessel wall elastic deformation data. In this case, synchronously acquiring the physiological parameter data corresponding to each cardiac phase may include: synchronously recording the intravascular pressure value from the multimodal physiological data at the image acquisition time of each cardiac phase, and associating the intravascular pressure value with the corresponding cardiac phase and acquisition angle to generate time-associated pressure data; synchronously recording the blood flow velocity value from the multimodal physiological data at the image acquisition time of each cardiac phase, and associating the blood flow velocity value with the corresponding cardiac phase and acquisition angle to generate time-associated blood flow data; synchronously recording the vessel wall elastic deformation data at the image acquisition time of each cardiac phase, and associating the elastic deformation data with the corresponding cardiac phase and acquisition angle to generate time-associated elastic data; and obtaining the physiological parameter data corresponding to each cardiac phase based on the time-associated pressure data, the time-associated blood flow data, and the time-associated elastic data.

[0037] S103: Perform three-dimensional reconstruction based on the multi-angle image data corresponding to each cardiac time, and map the physiological parameter data corresponding to each cardiac time to the corresponding three-dimensional reconstruction result to obtain a multi-temporal three-dimensional heart model library; the multi-temporal three-dimensional heart model library includes three-dimensional heart models corresponding to each cardiac time phase, and the three-dimensional heart model includes the three-dimensional reconstruction result and physiological parameter data.

[0038] like Figure 2 The diagram shown is a schematic of multi-angle image acquisition according to an embodiment of this disclosure. Here, multi-angle image data is acquired for three-dimensional reconstruction, and a three-dimensional dynamic model library containing morphology and physiology is established by mapping the three-dimensional reconstruction with physiological parameters. The three-dimensional heart model can intuitively reflect the three-dimensional morphology and physiological parameter field distribution of the heart under the corresponding cardiac phase, so that the surgeon can intuitively see the spatial morphology of the heart and understand the physiological state of a certain location in real time.

[0039] In one embodiment, the multi-angle image data may include X-ray multi-angle images and intravascular ultrasound multi-section images. In this case, three-dimensional reconstruction based on the multi-angle image data of each cardiac phase may include: extracting the heart contour from the X-ray multi-angle images of the same cardiac phase; based on the heart contour, using an image segmentation algorithm to extract the boundaries of the cardiac chambers and coronary arteries in each angle image to generate multi-angle contour data; based on the multi-angle contour data and the acquisition angle parameters of each image, using a back-projection algorithm to reconstruct the three-dimensional volume data of the heart to generate preliminary three-dimensional volume data; extracting the contours of the vessel wall and plaques from the intravascular ultrasound multi-section images of the same cardiac phase; using a shape interpolation algorithm to reconstruct a three-dimensional mesh model of the vessel along the vessel axis to generate a three-dimensional mesh model of the vessel; registering and fusing the three-dimensional mesh model of the vessel to the corresponding vessel region in the preliminary three-dimensional volume data, and performing surface optimization processing to obtain the three-dimensional reconstruction result of that cardiac phase.

[0040] The back-projection algorithm here, specifically the Feldkamp-Davis-Kress algorithm (FDK), can reconstruct a 360-degree image based on image acquisition data within a range of 180 degrees or greater. For example, image acquisition is performed at each angle within the range of -123° to 123° for each cardiac phase, or image acquisition is performed at each angle within the range of -123° to 123° for each cardiac phase within a cardiac cycle. Finally, the back-projection algorithm is used to perform 3D reconstruction for each cardiac phase based on the acquired multi-angle image data. Here, -123° to 123° is just a reference value and does not represent actual parameters. The image segmentation algorithm can be a traditional medical image segmentation algorithm (suitable for images with relatively clear structure and less noise), such as threshold segmentation or edge detection segmentation, or a deep learning-based segmentation algorithm (suitable for complex, low-quality images), with no specific restrictions. The surface optimization process addresses issues such as roughness, discontinuity, and redundancy on the surface of the fused 3D heart model. It uses algorithms to correct, smooth, and regularize the surface morphology of the model, thereby generating a smooth, continuous, and high-precision 3D reconstruction result that conforms to the real vascular anatomy.

[0041] In one implementation, mapping the physiological parameter data corresponding to each cardiac phase to the corresponding three-dimensional reconstruction result may include: extracting the three-dimensional spatial coordinates and corresponding physiological parameter values ​​of each acquisition point from the physiological parameter data corresponding to each cardiac phase, constructing a discrete physiological parameter point cloud, and generating physiological parameter point cloud data; calculating the physiological parameter values ​​at each grid node using the radial basis function interpolation method based on the physiological parameter point cloud data and the grid node coordinates of the three-dimensional reconstruction result of the cardiac phase, and generating a continuously distributed physiological parameter field; mapping the physiological parameter field to the surface of the three-dimensional reconstruction result of the cardiac phase through color encoding to obtain a three-dimensional model of the cardiac phase with physiological parameter distribution.

[0042] Here, the physiological parameter data may include intravascular pressure values, blood flow velocity values, and elastic deformation data of the blood vessel wall. The physiological parameter point cloud is a discrete data set composed of three-dimensional spatial coordinates and corresponding physiological parameter values. Each data point is essentially a spatial coordinate point with attribute labels. The label is a specific physiological parameter (e.g., intravascular pressure 20 mmHg, blood flow velocity 50 cm / s), and the spatial coordinates correspond to the actual acquisition location of that data point within the heart. Radial basis function interpolation is a method for calculating the values ​​of unknown blank areas using known discrete points. Its main logic is to use the distance from the unknown point to the known point as a weight, and to use the parameter values ​​of the known points to deduce the parameter values ​​of the unknown points, ultimately generating a continuous parameter distribution.

[0043] In one implementation, when mapping the physiological parameter data corresponding to each cardiac phase to the corresponding 3D reconstruction result to obtain a multi-temporal 3D heart model library, a phase index table can be established according to the chronological order of each cardiac phase. Using the phase index table (e.g., corresponding to "cardiac phase number - model identifier"), after identifying the current cardiac phase during surgery, the corresponding 3D heart model can be quickly located and called directly by the index, avoiding a full library search, significantly shortening model loading time, and meeting the low-latency requirement for real-time registration. Specifically, for each cardiac phase among multiple cardiac phases, 3D reconstruction and physiological parameter mapping operations are performed to generate a 3D model with physiological parameter distribution corresponding to that cardiac phase, generating 3D heart models for each phase; the 3D heart models for each phase are arranged according to the chronological order of the cardiac phases and indexed to generate a phase index table; the 3D heart models for each phase and the phase index table are stored in the model database to obtain the multi-temporal 3D heart model library.

[0044] In the above implementation, based on multi-angle image data of each temporal phase, three-dimensional volume data can be reconstructed using algorithms such as back projection, thereby transforming two-dimensional images into a three-dimensional model of the heart structure (such as the three-dimensional morphology of the heart chambers and coronary arteries), providing surgeons with an intuitive three-dimensional spatial reference. Furthermore, interpolation algorithms can map discrete physiological parameters (such as pressure and blood flow velocity at each acquisition point) onto the grid nodes of the three-dimensional reconstruction results, generating a continuous physiological parameter field (such as the distribution of pressure in the vessel wall and the distribution of blood flow velocity within the vessel), which can be visualized using color coding and other methods. In this way, doctors can not only see the spatial morphology of the heart but also understand the physiological state of a certain location in real time (such as whether there is abnormal pressure or slow blood flow in a certain vessel segment), providing functional guidance for surgical operations (such as guidewire advancement and stent selection). Moreover, organizing the three-dimensional heart models of each cardiac phase into a multi-temporal model library in chronological order is equivalent to reserving dynamic model resources covering the entire cardiac cycle for the surgical process. Subsequent surgeries do not require real-time model reconstruction; only the corresponding temporal model needs to be called, significantly improving real-time registration efficiency.

[0045] S104: During the operation, real-time electrocardiogram (ECG) signals and real-time multimodal physiological data of the surgical patient are collected. Based on the real-time ECG signals and real-time multimodal physiological data, the current cardiac phase is identified. According to the current cardiac phase, the corresponding three-dimensional heart model is selected from the multi-phase three-dimensional heart model library to obtain the current phase three-dimensional heart model.

[0046] This step involves collecting real-time electrocardiogram signals and multimodal physiological data during surgery to analyze the current cardiac motion state (such as the specific cardiac phase) and ensure that the subsequent 3D cardiac model is a perfect match for the current cardiac motion state.

[0047] In specific implementation, the acquisition of real-time electrocardiogram (ECG) signals and real-time multimodal physiological data during the surgical procedure can be achieved as follows: ECG signals from the surgical patient are acquired in real-time using an ECG monitoring device at a preset sampling frequency, and real-time ECG signal waveforms are output to generate real-time ECG signals; intravascular pressure and blood flow velocity from the surgical patient are acquired in real-time using a blood flow monitoring device, and real-time hemodynamic parameters are output to generate real-time hemodynamic data; X-ray fluoroscopy images or ultrasound images of the surgical patient are acquired in real-time using an imaging device, and real-time image frames are output to generate real-time image data; based on the real-time hemodynamic data and the real-time image data, real-time multimodal physiological data are obtained.

[0048] In one embodiment, identifying the current cardiac phase based on the real-time electrocardiogram (ECG) signal and real-time multimodal physiological data may include: performing real-time R-wave peak detection and waveform feature extraction on the real-time ECG signal to generate real-time ECG feature data; calculating the relative position of the current moment in the cardiac cycle based on the real-time ECG feature data to generate a cycle relative position; performing similarity matching between the real-time ECG feature data and the intravascular pressure and blood flow velocity values ​​extracted from the real-time multimodal physiological data and the physiological parameter data of each cardiac phase in the multi-temporal three-dimensional cardiac model library to generate a temporal matching degree; and determining the current cardiac phase based on the cycle relative position and the temporal matching degree.

[0049] Here, a cardiac cycle can be considered as an RR interval, such as the complete time period from the beginning of one R wave peak to the end of the next R wave peak. When calculating the relative position of the current moment within the cardiac cycle based on real-time ECG characteristic data, the position corresponding to the current moment can be located in the waveform within the RR interval corresponding to a cardiac cycle based on the current real-time ECG characteristic data, thereby obtaining the relative position of the current moment within the cycle.

[0050] When determining the current cardiac phase based on the relative position of the cycle and the time matching degree, the weights of the relative position of the cycle and the time matching degree can be set, for example, the time matching degree accounts for 60% and the relative position of the cycle accounts for 40%. Then, based on the time matching degree and its weight, as well as the overlap between the relative position of the cycle and the position range corresponding to the target cardiac phase and its weight, the matching score of the target cardiac phase is calculated, and finally the cardiac phase with the highest score is selected as the current cardiac phase.

[0051] In practice, when selecting a corresponding 3D heart model from a multi-temporal 3D heart model library based on the current cardiac phase, a model query request can be generated based on the current cardiac phase. The model identifier matching the current cardiac phase can be retrieved from the temporal index table of the multi-temporal 3D heart model library to determine the target model identifier. Based on the target model identifier, the corresponding 3D heart model is loaded from the multi-temporal 3D heart model library to generate a preliminary selection model. A morphological comparison is performed between the preliminary selection model and the real-time image data. When the morphological difference exceeds a preset threshold, an elastic registration algorithm is used to fine-tune the preliminary selection model to obtain the current temporal 3D heart model.

[0052] S105: Perform surgical registration using the current phase three-dimensional cardiac model.

[0053] During surgery, the current phase of the 3D cardiac model can be matched with the real-time position of surgical instruments (such as guidewires and stents) to provide doctors with precise navigation references, reducing the difficulty and risk of surgical procedures. Firstly, registration allows the real-time position of instruments such as guidewires and stents to be superimposed onto the 3D cardiac model, enabling doctors to visually see the spatial position of the instruments within the heart (e.g., whether the guidewire has reached the target vessel segment, and whether the stent is in contact with the vessel wall). Furthermore, based on the registration results, doctors can clearly determine the relationship between the instruments and surrounding structures (e.g., the distance between the guidewire and the vessel wall, and whether it is close to important tissues), avoiding complications such as vascular damage and stent misalignment caused by positioning errors. Simultaneously, by combining physiological parameter data from the model (e.g., blood flow velocity, pressure distribution), surgical strategies can be optimized (e.g., avoiding areas with rapid blood flow and reducing the impact of the guidewire on the vessel wall), further improving surgical safety and effectiveness.

[0054] Based on this, in one embodiment, surgical registration is performed using the current phase three-dimensional cardiac model, including: acquiring the real-time position information of the interventional device, registering the real-time position information of the interventional device with the current phase three-dimensional cardiac model to obtain a registration result; and displaying the current phase three-dimensional cardiac model and the position of the interventional device in real time on the surgical navigation interface based on the registration result.

[0055] For example, when the interventional device is a guidewire, to obtain the real-time position information of the guidewire, a position marker device can be set on the guidewire. The three-dimensional spatial coordinates of the position marker device can be tracked in real time by the positioning system to generate guidewire key point coordinate data. In addition, the image contour of the guidewire can be extracted from the real-time image data, and the contour line of the guidewire can be identified by the edge detection algorithm and converted into three-dimensional spatial coordinates to generate guidewire contour coordinate data. Then, the Kalman filter algorithm can be used to perform data fusion based on the guidewire key point coordinate data and the guidewire contour coordinate data to obtain the real-time position information of the guidewire.

[0056] In one implementation, when registering the real-time position information of the guidewire with the current phase 3D heart model, the guidewire tip position and the position of the maximum curvature point of the guidewire can be extracted from the real-time position information of the guidewire as guidewire feature points to generate a guidewire feature point set; and the vascular bifurcation points and key points of the vascular centerline can be extracted from the current phase 3D heart model as vascular feature points to generate a vascular feature point set; then, based on the guidewire feature point set and the vascular feature point set, the optimal rigid body transformation matrix is ​​calculated using the iterative nearest point algorithm to minimize the distance between the guidewire feature points and the vascular feature points, thereby obtaining the registration result.

[0057] Optionally, after registering the real-time position information of the guidewire with the current phase 3D heart model, the registration accuracy can be improved. For example, the blood flow velocity vector at the guidewire location can be extracted from the current phase 3D heart model, and the force exerted by the blood flow on the guidewire can be calculated based on the cross-sectional area of ​​the guidewire and the blood flow velocity vector to generate blood flow force data. Then, the positional offset of the guidewire under the action of blood flow can be calculated based on the blood flow force data and the physical parameters of the guidewire to generate a positional offset correction amount. Finally, the guidewire position in the registration result is corrected by superimposing the positional offset correction amount to obtain the corrected registration result.

[0058] In one implementation, when the current phase three-dimensional heart model and guidewire position are displayed in real time on the surgical navigation interface, a three-dimensional graphics rendering engine can be used to render the current phase three-dimensional heart model in real time to generate a three-dimensional heart display screen. Then, the three-dimensional position and guidewire posture of the registration result can be superimposed on the three-dimensional heart display screen, and the distribution of physiological parameters in the current phase three-dimensional heart model can be displayed by color coding.

[0059] Furthermore, the ideal surgical path of the guidewire in a multi-temporal 3D cardiac model library can be pre-planned, and the ideal surgical path can be associated with each cardiac phase and stored to generate a time-associated ideal path. Then, the ideal path segment corresponding to the current time can be extracted from the time-associated ideal path according to the current cardiac phase, and the ideal path segment can be superimposed and displayed on the surgical navigation interface to generate a path guidance display. Moreover, the deviation vector between the actual position of the guidewire in the registration result and the ideal path segment can be calculated in real time, and the direction can be adjusted by arrows on the surgical navigation interface, which can provide real-time navigation guidance information for surgeons.

[0060] In one implementation, deviation warnings can also be provided based on the registration results. For example, the distance between the actual position of the guidewire in the registration results and the pre-planned surgical path can be calculated in real time to generate a deviation distance value. It can then be determined whether this deviation distance value exceeds a safety threshold. If it does, a warning message can pop up on the surgical navigation interface, along with an audio-visual alert, and the deviation area can be highlighted in red on the 3D display. This deviation warning system can prevent surgical risks such as vascular damage and instrument misalignment caused by accumulated deviations, reducing the probability of postoperative complications. Furthermore, intuitive interface prompts (such as pop-ups, audio-visual alerts, and red highlighting) allow doctors to quickly locate the position and degree of deviation without manually comparing data, saving reaction time for adjustments.

[0061] Furthermore, as surgical procedures begin to support remote operation technologies, some surgical procedures may be performed remotely, or in some cases, doctors may need to conduct simulated surgeries. In such cases, one embodiment of this disclosure can also provide force feedback based on the registration results. For example, the contact point positions and contact pressures between the guidewire and the blood vessel wall are extracted from the current three-dimensional cardiac model, and the contact pressures at each contact point are vector-synthesized to generate guidewire resultant force data. Then, the guidewire resultant force data is converted into a signal according to the control protocol of the force feedback device to generate a force feedback control signal. Finally, the force feedback control signal can be sent to the force feedback device at the surgical end and converted into tactile feedback that the operator can perceive. Through this force feedback mechanism, the contact pressure between the guidewire and the blood vessel wall can be converted into perceptible tactile feedback (such as increased resistance when encountering vascular stenosis), allowing doctors to obtain a tactile sensation close to that of actual surgery during remote or simulated operations, avoiding vascular damage caused by imperceptible operation.

[0062] In other embodiments, the interventional device described above can also be a stent. Similarly, registering the real-time position information of the stent with the current three-dimensional cardiac model can include: acquiring the real-time position information of the stent, extracting stent feature points from the real-time position information of the stent, extracting vascular segment feature points from the current three-dimensional cardiac model, and performing registration based on the stent feature points and vascular segment feature points to obtain a stent registration result; then, extracting the mechanical information of the stent's location from the current three-dimensional cardiac model, and correcting the stent registration result based on the mechanical information to obtain a corrected stent registration result; thereby, the corrected stent registration result can be displayed in real time on the surgical navigation interface.

[0063] In one implementation, before obtaining a multi-temporal 3D heart model library, a mechanical model can be constructed. For example, deformation data of the blood vessel wall under different pressures can be extracted from the physiological parameter data corresponding to each cardiac phase. The elastic modulus of the blood vessel wall can be calculated back based on the laws of elasticity, generating the mechanical parameters of the blood vessel wall. The material property parameters of the guidewire, including the elastic modulus and bending stiffness, can be obtained. A mechanical model of the guidewire can be established based on beam theory, generating a guidewire mechanical model. By coupling the mechanical parameters of the blood vessel wall and the mechanical model of the guidewire, setting the contact boundary conditions between the guidewire and the blood vessel wall, and solving them using the finite element method, a guidewire-blood vessel interaction mechanical model can be obtained. Then, the guidewire-blood vessel interaction mechanical model can be integrated into the 3D heart model of each cardiac phase, resulting in a multi-temporal 3D heart model library containing mechanical information. By constructing a mechanical model of the blood vessel wall and the guidewire, the 3D heart model not only has shape but also reflects actual mechanical properties (such as blood vessel wall elasticity and guidewire bending stiffness), providing a mechanical basis for surgical registration (such as guidewire posture adjustment). This model library, which includes mechanical information, can simulate the interaction between the guidewire and blood vessel under different cardiac phases (such as the change in force on the guidewire when the blood vessel is compressed during systole), helping doctors predict operational resistance, adjust surgical strategies, and reduce the risks of intraoperative vascular injury and guidewire entrapment.

[0064] The multi-temporal three-dimensional cardiac model library constructed using the embodiments of this disclosure can completely cover the dynamic changes of the heart within a cardiac cycle, achieving synchronization between the state of the three-dimensional cardiac model and the actual cardiac motion. Furthermore, the three-dimensional cardiac models in the constructed multi-temporal three-dimensional cardiac model library not only contain morphological information but also reflect the functional state of the heart, providing a more comprehensive decision-making basis for surgical registration. When dividing cardiac phases, electrocardiogram signals and multimodal physiological data are combined, improving the accuracy of cardiac phase division. Further, during surgery, the current cardiac phase is identified by real-time electrocardiogram signals and multimodal physiological data, and the corresponding three-dimensional cardiac model is called from the multi-temporal three-dimensional cardiac model library for registration, effectively avoiding registration drift caused by cardiac motion. For example, if a static model is used during surgery, the diameter of the blood vessel increases as the heart transitions from systole to diastole, which may lead to guidewire positioning deviation. However, the embodiments of this disclosure can match the current morphology of the heart in real time, effectively reducing registration errors. It can also improve the accuracy of instrument operation. For example, in coronary intervention surgery, the registration accuracy between the guidewire and the blood vessel directly affects the stent implantation position. Dynamic registration can ensure that the stent accurately fits the blood vessel wall, reducing the risk of complications such as postoperative thrombosis and in-stent restenosis.

[0065] In summary, the embodiments disclosed herein can significantly improve the accuracy of guidewire registration in interventional surgery, reduce the interference of cardiac motion on the surgery, and effectively improve the safety, precision, and efficiency of interventional cardiac surgery.

[0066] like Figure 3The diagram shown is a schematic of a registration device 300 for cardiac surgery provided in an embodiment of this disclosure, comprising: The acquisition module 31 is used to acquire the electrocardiogram (ECG) signal and multimodal physiological data of the surgical patient, and to divide the cardiac cycle into cardiac phases based on the ECG signal and the multimodal physiological data to obtain multiple cardiac phases; The first acquisition module 32 is used to acquire multi-angle image data of the heart in each of the multiple cardiac phases, and simultaneously acquire corresponding physiological parameter data. The 3D reconstruction module 33 is used to perform 3D reconstruction based on the multi-angle image data corresponding to each cardiac time, and to map the physiological parameter data corresponding to each cardiac time to the corresponding 3D reconstruction result to obtain a multi-temporal 3D heart model library; the multi-temporal 3D heart model library includes 3D heart models corresponding to each cardiac time, and the 3D heart model includes the 3D reconstruction result and physiological parameter data. The second acquisition module 34 is used to acquire real-time electrocardiogram signals and real-time multimodal physiological data of the surgical patient during the operation. Based on the real-time electrocardiogram signals and real-time multimodal physiological data, the current cardiac phase is identified. According to the current cardiac phase, the corresponding three-dimensional heart model is selected from the multi-phase three-dimensional heart model library to obtain the current phase three-dimensional heart model. The registration module 35 is used to perform surgical registration using the current phase three-dimensional heart model.

[0067] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0068] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0069] Based on the same technical concept, this disclosure also provides an electronic device 400, referring to... Figure 4 The diagram shown is a schematic representation of the structure of an electronic device according to an exemplary embodiment of this disclosure, comprising: The processor 410, memory 420, and bus 430 are included. The memory 420 is used to store execution instructions and includes main memory 421 and external memory 422. The main memory 421, also known as internal memory, is used to temporarily store the operation data in the processor 410 and the data exchanged with external memory 422 such as hard disk. The processor 410 exchanges data with external memory 422 through main memory 421.

[0070] In this embodiment, the memory 420 is specifically used to store application code that executes the scheme of this disclosure, and its execution is controlled by the processor 410. That is, when the electronic device 400 is running, the processor 410 communicates with the memory 420 through the bus 430, or the processor 410 communicates with the memory 420 through other means, so that the processor 410 executes the application code stored in the memory 420, and then performs the steps of the registration method in cardiac surgery described in any of the foregoing embodiments.

[0071] The memory 420 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0072] Processor 410 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0073] It is understood that the structures illustrated in the embodiments of this disclosure do not constitute a specific limitation on the electronic device 400. In other embodiments of this disclosure, the electronic device 400 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0074] This disclosure also provides a computer-readable storage medium including instructions stored thereon, wherein, when executed by a processor, the instructions perform a registration method in cardiac surgery as described in any of the preceding embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0075] This disclosure also provides a computer program product storing a computer program. When the computer program is run by a processor, it executes the steps of the registration method in cardiac surgery provided in any of the above embodiments of this disclosure. For details, please refer to the above method embodiments, which will not be repeated here.

[0076] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium, which can be a volatile or non-volatile computer-readable storage medium. In another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0077] Furthermore, embodiments of the subject matter and functional operation described in this specification can be implemented in the following ways: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0078] The processing and logic flows described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flows can also be executed by dedicated logic circuits—such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), and the device can also be implemented as dedicated logic circuits.

[0079] Suitable computers for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.

[0080] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0081] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily intended to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0082] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0083] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0084] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A registration method in cardiac surgery, characterized in that, include: Acquire electrocardiogram (ECG) signals and multimodal physiological data of surgical patients, and divide the cardiac cycle into cardiac phases based on the ECG signals and multimodal physiological data to obtain multiple cardiac phases; Multi-angle imaging data of the heart are acquired at each of the multiple cardiac phases, and corresponding physiological parameter data are acquired simultaneously. Three-dimensional reconstruction is performed based on multi-angle image data corresponding to each cardiac phase, and physiological parameter data corresponding to each cardiac phase is mapped to the corresponding three-dimensional reconstruction result to obtain a multi-temporal three-dimensional heart model library; the multi-temporal three-dimensional heart model library includes three-dimensional heart models corresponding to each cardiac phase, and the three-dimensional heart model includes the three-dimensional reconstruction result and physiological parameter data; During the operation, real-time electrocardiogram (ECG) signals and real-time multimodal physiological data of the surgical patient are collected. Based on the real-time ECG signals and real-time multimodal physiological data, the current cardiac phase is identified. According to the current cardiac phase, the corresponding three-dimensional heart model is selected from the multi-phase three-dimensional heart model library to obtain the current phase three-dimensional heart model. Surgical registration was performed using the current phase three-dimensional cardiac model.

2. The method according to claim 1, characterized in that, Acquire multimodal physiological data of surgical patients, including: Hemodynamic parameters of the surgical patient were collected using a blood flow monitoring device, and intravascular pressure and blood flow velocity values ​​at different times were recorded to generate hemodynamic data. The respiratory signals of the surgical patient are collected using a respiratory monitoring device, and the respiratory rate and respiratory amplitude are recorded to generate respiratory data. The body temperature data of the surgical patient is collected using a body temperature monitoring device, and changes in body temperature are recorded to generate body temperature data. The multimodal physiological data are obtained based on the hemodynamic data, the respiratory data, and the body temperature data.

3. The method according to claim 1, characterized in that, Based on the electrocardiogram signal and the multimodal physiological data, the cardiac cycle is divided into cardiac phases, including: Waveform features are extracted from the electrocardiogram (ECG) signal to generate ECG waveform feature data; The cardiac cycle is determined based on the electrocardiogram waveform feature data, where the cardiac cycle is the time interval between two adjacent R wave peaks; The peak time of intravascular pressure and the time of change of blood flow velocity are extracted from the multimodal physiological data as auxiliary phase markers. Based on the cardiac cycle and the auxiliary phase markers, each cardiac cycle is divided into multiple cardiac phases.

4. The method according to claim 1, characterized in that, The physiological parameter data are collected synchronously, including: The intravascular pressure value in the multimodal physiological data is recorded synchronously at the image acquisition time of each cardiac phase. The intravascular pressure value is associated with the corresponding cardiac phase and acquisition angle to generate time-related pressure data. The blood flow velocity values ​​in the multimodal physiological data are recorded synchronously at the image acquisition time of each cardiac phase, and the blood flow velocity values ​​are associated with the corresponding cardiac phase and acquisition angle to generate time-related blood flow data; Elastic deformation data of the blood vessel wall is recorded synchronously at the time of image acquisition for each cardiac phase. The elastic deformation data is associated with the corresponding cardiac phase and acquisition angle to generate the associated elastic data. Based on the time-related pressure data, the time-related blood flow data, and the time-related elasticity data, the physiological parameter data corresponding to each cardiac time are obtained.

5. The method according to claim 1, characterized in that, The multi-angle imaging data includes X-ray multi-angle images and intravascular ultrasound multi-section images; Three-dimensional reconstruction was performed based on multi-angle image data of each cardiac phase, including: The heart contour is extracted from the X-ray multi-angle images at the same cardiac phase. Based on the heart contour, an image segmentation algorithm is used to extract the boundaries of the heart chambers and the boundaries of the coronary arteries in each angle image to generate multi-angle contour data. Based on the multi-angle contour data and the acquisition angle parameters of each image, the three-dimensional volume data of the heart is reconstructed using a back projection algorithm to generate preliminary three-dimensional volume data. The contours of the vascular wall and plaques in the intravascular ultrasound multi-section images under the same cardiac phase are extracted, and a three-dimensional mesh model of the blood vessel is reconstructed along the vascular axis using a shape interpolation algorithm to generate a three-dimensional mesh model of the blood vessel. The 3D vascular mesh model is registered and fused to the corresponding vascular region in the preliminary 3D volume data, and surface optimization processing is performed to obtain the 3D reconstruction result of the cardiac phase.

6. The method according to claim 1, characterized in that, Mapping the physiological parameter data corresponding to each heartbeat to the corresponding 3D reconstruction results, including: The three-dimensional spatial coordinates and corresponding physiological parameter values ​​of each acquisition point are extracted from the physiological parameter data corresponding to each heartbeat, and a discrete physiological parameter point cloud is constructed to generate physiological parameter point cloud data. Based on the point cloud data of the physiological parameters and the grid node coordinates of the three-dimensional reconstruction results of the cardiac phase, the radial basis function interpolation method is used to calculate the physiological parameter values ​​at each grid node, generating a continuously distributed physiological parameter field. The physiological parameter field is mapped onto the surface of the three-dimensional reconstruction result of the cardiac phase using color encoding to obtain a three-dimensional model of the cardiac phase with physiological parameter distribution.

7. The method according to claim 1, characterized in that, Identifying the current cardiac phase based on the real-time electrocardiogram signal and real-time multimodal physiological data includes: Real-time R-wave peak detection and waveform feature extraction are performed on the real-time electrocardiogram signal to generate real-time electrocardiogram feature data; Calculate the relative position of the current moment in the cardiac cycle based on the real-time ECG feature data, and generate the cycle relative position; Based on the real-time electrocardiogram feature data and the intravascular pressure and blood flow velocity values ​​extracted from the real-time multimodal physiological data, a similarity match is performed with the physiological parameter data of each cardiac phase in the multi-temporal three-dimensional heart model library to generate the matching degree. The current cardiac phase is determined based on the relative position of the cycle and the time-matching degree.

8. The method according to claim 1, characterized in that, Surgical registration using the current temporal 3D cardiac model includes: The real-time position information of the interventional device is obtained, and the real-time position information of the interventional device is registered with the current phase three-dimensional cardiac model to obtain the registration result; Based on the registration results, the current phase three-dimensional cardiac model and the position of the interventional device are displayed in real time on the surgical navigation interface.

9. A registration device for cardiac surgery, characterized in that, include: The acquisition module is used to acquire the electrocardiogram (ECG) signal and multimodal physiological data of the surgical patient, and to divide the cardiac cycle into cardiac phases based on the ECG signal and the multimodal physiological data to obtain multiple cardiac phases; The first acquisition module is used to acquire multi-angle image data of the heart at each of the multiple cardiac phases, and simultaneously acquire corresponding physiological parameter data. The 3D reconstruction module is used to perform 3D reconstruction based on multi-angle image data corresponding to each cardiac time, and to map the physiological parameter data corresponding to each cardiac time to the corresponding 3D reconstruction result to obtain a multi-temporal 3D heart model library; the multi-temporal 3D heart model library includes 3D heart models corresponding to each cardiac time, and the 3D heart model includes the 3D reconstruction result and physiological parameter data; The second acquisition module is used to acquire real-time electrocardiogram signals and real-time multimodal physiological data of the surgical patient during the operation. Based on the real-time electrocardiogram signals and real-time multimodal physiological data, the current cardiac phase is identified. According to the current cardiac phase, the corresponding three-dimensional heart model is selected from the multi-phase three-dimensional heart model library to obtain the current phase three-dimensional heart model. The registration module is used to perform surgical registration using the current phase three-dimensional cardiac model.

10. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing computer-readable instructions, and the processor being configured to read the computer-readable instructions to execute the registration method according to any one of claims 1 to 8.

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