Passive measurement type magnetic navigation system and method based on magnetic source imaging

Abstract

The invention discloses a passive measurement type magnetic navigation system and method based on magnetic source imaging, and belongs to the technical field of medical equipment. The system comprises a sensor array for detecting and feeding back magnetic field changes in an environment; the magnetic sensor array adopts a modular expansion design, a detection curved surface is formed by a plurality of magnetic sensing units, and each magnetic sensing unit comprises a planar substrate, a magnetic sensor chip and a slave MCU assembly; the adjacent magnetic sensing units are connected through the adjustable connecting mechanisms, and the included angle between the adjustable connecting mechanisms can be flexibly adjusted and locked within a certain range, so that the whole magnetic sensor array is adaptive to detection surfaces with different curvatures; the overall structure supports modular expansion, the number of the magnetic sensing units is increased or decreased according to actual detection requirements, and detection arrays of different scales and shapes are flexibly constructed; according to the invention, an accurate magnetic dipole model and a high-robustness pose resolving method are established, and millimeter-level and real-time three-dimensional tracking of the surgical instrument is realized.

Description

Technical Field

[0001] This invention belongs to the field of medical device technology, specifically relating to a passive measurement magnetic navigation system and method based on magnetic source imaging. Background Technology

[0002] Against the backdrop of an increasingly aging population and a high incidence of cardiovascular and cerebrovascular diseases, interventional surgery, as a highly efficient minimally invasive treatment, is gradually becoming the mainstream clinical approach. However, traditional interventional procedures heavily rely on X-ray fluoroscopy and angiography, exposing patients to significant radiation exposure, posing safety risks. Operators, long-term exposure to harmful environments can easily induce occupational diseases, severely restricting the safety and widespread adoption of these procedures. Furthermore, current catheter manipulation relies heavily on operator experience, lacking real-time, high-precision spatial positioning and navigation support, which can easily lead to puncture deviation, surgical delays, and even intraoperative complications, limiting its application in complex vascular pathways and deep target sites.

[0003] Existing technologies relying on strong magnetic field emission devices for intervention mainly present the following technical problems:

[0004] (1) Insufficient system balance: It is difficult to balance system cost, anti-interference ability and positioning accuracy. Especially when relying on strong magnetic field drive, the system deployment is complicated, the flexibility is poor, the interference to the intraoperative environment is large, and the safety is insufficient.

[0005] (2) High power consumption and large size: Traditional magnetic navigation systems have high power consumption and large size, which is not conducive to energy consumption control and equipment integration in minimally invasive surgery scenarios.

[0006] (3) Imbalanced sensor detection: In a planar sensor array, some sensor signals are too strong and easily saturate, while some signals are too weak and easily lost, resulting in uneven detection sensitivity and affecting the overall positioning accuracy.

[0007] (4) Poor structural adaptability: Most existing sensor arrays are fixed structures, which are difficult to adapt to different anatomical curvatures and diverse detection surfaces, thus limiting the scope of clinical applications.

[0008] (5) Insufficient anti-interference performance: In complex medical environments, there are strong background magnetic fields and electromagnetic noise interference. Traditional algorithms have weak anti-interference capabilities and poor positioning stability.

[0009] (6) Radiation risk: Current interventional surgeries mostly rely on X-ray navigation, which poses radiation risks to doctors and patients. At the same time, the surgery is highly dependent on the surgeon's experience, and the accuracy of treatment for complex vascular pathways and deep targets is difficult to guarantee. Summary of the Invention

[0010] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0011] A passive measurement-type magnetic navigation system based on magnetic source imaging, comprising:

[0012] A magnetic sensor array is used to detect and provide feedback on changes in the magnetic field in the environment. The array employs a modular expansion design, consisting of several magnetic sensing units forming a detection surface. Each magnetic sensing unit includes a planar substrate, a magnetic sensor chip, and a slave MCU component. Adjacent magnetic sensing units are connected by an adjustable connection mechanism. The angle between these mechanisms allows for flexible adjustment and locking within a certain range, enabling the entire magnetic sensor array to adapt to detection surfaces with different curvatures. The overall structure supports modular expansion, allowing for the addition or removal of magnetic sensing units according to actual detection needs, flexibly constructing detection arrays of different sizes and shapes.

[0013] Several MCU components are used to receive, partially summarize, and transmit data from the magnetic sensor array;

[0014] The main MCU component is used to collect data received from the MCU component and transmit the data to the host computer.

[0015] The RS-485 bus is used for stable communication between the master MCU component and multiple slave MCU components.

[0016] The USB interface is used for high-speed data communication between the main MCU component and the host computer.

[0017] Host computer: Used to receive and process data from the magnetic sensor array in real time, manage the magnetic positioning and tracking system and the signal control system, and realize comprehensive monitoring and control of the navigation process;

[0018] Magnetic field generator: It consists of tiny energized coils, which are multi-layered cylindrical transmitting coils. It receives control signals from the magnetic positioning and tracking system and the signal control system to generate magnetic fields of a specific frequency band by adjusting the control current changes.

[0019] Magnetic positioning and tracking system and signal control system: respectively used to output precise positioning signals for the conduit based on the magnetic tracking method and to generate control signals to produce magnetic fields of specific frequency bands by adjusting the change of control current.

[0020] A passive measurement-based magnetic navigation method based on magnetic source imaging, used in the aforementioned passive measurement-based magnetic navigation system, comprising:

[0021] Step 1: Construct the passive measurement magnetic navigation system based on magnetic source imaging.

[0022] Step 2: Construct a theoretical model for magnetic dipole navigation;

[0023] Step 3: Perform noise suppression processing on the raw observation data of the detected magnetic field source;

[0024] Step 4: Allow the sensor to stand still for calibration;

[0025] Step 5: Generate the Sigma sampling point set and set the weights for each point when calculating the mean and covariance;

[0026] Step 6: Propagate the Sigma sampling point set using the process model to complete the state prediction;

[0027] Step 7: Use the observation model to correct the state prediction results of Step 6 to complete the observation update.

[0028] The present invention has the following beneficial effects:

[0029] This invention proposes a passive measurement-type magnetic navigation system based on magnetic source imaging. Through a "small magnetic source + magnetometer array" architecture, it establishes an accurate magnetic dipole model and a robust pose calculation method, achieving millimeter-level, real-time 3D tracking of surgical instruments. This solution significantly improves system deployment flexibility and safety without requiring a large magnetic field drive, greatly reduces interference with the intraoperative environment, and possesses good scalability and integration capabilities.

[0030] This invention adopts a "small magnetic source + magnetometer array" architecture, which realizes low power consumption and high precision magnetic navigation. The system is small in size, flexible in deployment, and can significantly reduce energy consumption.

[0031] This invention employs a curved array design to ensure that the distance from each sensor to the magnetic source is approximately equal, effectively preventing the problem of insufficient signals from some sensors while the signals from other sensors are saturated, thus ensuring the balance and consistency of the detection sensitivity of the entire system.

[0032] This invention adopts a modular hardware expansion design, supports multi-dimensional angle locking and dynamic adjustment, and can be adapted to detection surfaces with different curvatures, greatly improving the system's adaptability.

[0033] This invention employs the unscented Kalman filter (UKF) algorithm, combined with specific frequency band noise suppression technology, to achieve stable positioning of catheters in complex medical environments.

[0034] This invention provides a radiation-free magnetic navigation alternative for interventional surgery, which is expected to significantly reduce the risk of X-ray exposure for both doctors and patients. At the same time, by providing real-time, high-precision spatial positioning information, it can reduce the reliance on the surgeon's experience, provide technical support for interventional treatment of complex vascular pathways and deep targets, and promote the development of interventional medicine towards a safer and more precise direction. Attached Figure Description

[0035] Figure 1This is a schematic diagram of the passive measurement magnetic navigation system based on magnetic source imaging of the present invention, wherein 102-slave MCU component, 103-master MCU component, 104-RS-485 bus, 105-USB interface, 106-host computer, 107-magnetic field generator, and 108-magnetic positioning and tracking system and signal control system;

[0036] Figure 2 This is a schematic diagram of the assembleable and expandable curved sensor array of the present invention, wherein 101-magnetic sensor array, 102-MCU component, 109-adjustable connection mechanism, and 1011-magnetic sensing unit;

[0037] Figure 3 This is a flowchart of the passive measurement magnetic navigation method based on magnetic source imaging according to the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0039] This invention relates to the field of medical devices, and specifically to a passive measurement-type magnetic navigation system and method based on magnetic source imaging, which is suitable for real-time precise positioning and navigation of medical devices such as catheters and guidewires during interventional surgery.

[0040] It should be noted that the illustrations provided in this embodiment are only schematic representations of the basic concept of the present invention. Therefore, the illustrations only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0041] Example 1:

[0042] like Figure 1 As shown, the improvement of this system lies in adopting a "small magnetic source + magnetometer array" architecture, combined with a curved modular sensor design and UKF noise suppression algorithm, to achieve low power consumption, miniaturization, and interference resistance with millimeter-level real-time positioning. Its innovation lies in simultaneously considering system cost, deployment flexibility, and high-precision navigation requirements. This embodiment 1 provides a passive measurement-type magnetic navigation system based on magnetic source imaging, such as... Figure 1 , Figure 2 As shown, this system achieves precise positioning and navigation control of the magnetic source by integrating multiple components, including:

[0043] Magnetic sensor array 101 (e.g.) Figure 2 As shown, it employs a modular, expandable design for detecting and responding to changes in the magnetic field in the environment;

[0044] The MCU (microcontroller) component 102 is used to receive, partially summarize, and transmit data from the magnetic sensor array 101;

[0045] Several main MCU components 103 are used to collect data received from MCU component 102 and transmit the data to host computer 106;

[0046] RS-485 bus 104 is used for stable communication between the master MCU component 103 and multiple slave MCU components 102;

[0047] USB (Universal Serial Bus) interface 105 is used for high-speed data communication between the main MCU component 103 and the host computer 106;

[0048] The host computer 106 is used to receive and process the data from the magnetic sensor array 101 in real time, manage the magnetic positioning and tracking system and the signal control system 108, and realize comprehensive monitoring and control of the navigation process.

[0049] Magnetic field generator 107: Composed of a tiny energized coil, designed as a multi-layer cylindrical transmitting coil, it receives the control signal from the magnetic positioning and tracking system and the signal control system 108 to generate a magnetic field of a specific frequency band by adjusting the control current change, and generates a corresponding magnetic field of a specific frequency band by adjusting the control current change.

[0050] Magnetic positioning and tracking system and signal control system 108: respectively used to output precise positioning signals for the conduit based on the magnetic tracking method and to generate control signals to produce magnetic fields of specific frequency bands by adjusting the change of control current.

[0051] As an example, such as Figure 2 As shown, the magnetic sensor array 101 adopts a modular expansion design, and its mechanical architecture includes the following core features: a detection surface is formed by several magnetic sensing units 1011, each magnetic sensing unit 1011 includes a planar substrate, a magnetic sensor chip, and a slave MCU component 102; adjacent magnetic sensing units 1011 are connected by an adjustable connection mechanism 109, and the included angle between the adjustable connection mechanisms 109 allows it to be flexibly adjusted and locked within a certain range, thereby enabling the entire magnetic sensor array 101 to adapt to detection surfaces with different curvatures; the overall structure supports modular expansion, and the number of magnetic sensing units 1011 can be increased or decreased according to actual detection needs to flexibly construct detection arrays of different sizes and shapes.

[0052] This invention employs a curved array design instead of a traditional planar array. The advantages of this design are as follows: First, the curved array structure allows each magnetic sensor (the coil contained in the magnetic sensor array 101) to be positioned approximately at the same distance from the magnetic field transmitter 107. This characteristic is particularly important because the magnetic dipole field strength increases with the cube of the distance (1 / r²). 3 First, by setting the distance *r* from each magnetic sensor to the magnetic source to be approximately equal, attenuation can be effectively prevented from some magnetic sensors having insufficient signals while others saturate, ensuring the balance and consistency of the overall system's detection sensitivity. Second, compared with traditional planar arrays, the curved array of this invention can comprehensively sample the magnetic field from multiple directions. Planar arrays can only observe the magnetic field from one side. When the positioned magnetic source or medical device tools such as catheters / capsules deviate from the normal axis, the Jacobian matrix that associates voltage measurements with six-degree-of-freedom attitude becomes ill-conditioned, leading to a sharp increase in directional error. The curved array of this invention covers a larger solid angle, allowing the Fisher information matrix (used to measure the amount of information about the parameter to be estimated contained in the observation data) to maintain good characteristics under all attitudes within the tracking volume, significantly improving the positioning accuracy and stability of the magnetic field transmitter. Third, this curved array design effectively overcomes the inherent depth bias problem of planar layouts. In planar layouts, sources closer to the circuit board dominate the least-squares fitting, causing deeper points to appear systematically farther away. The curved array spatial layout structure employed in this invention allows the spatial positioning sensitivity to decrease more slowly with depth. Therefore, the same calibration model can simultaneously cover superficial tissues and targets 5-10 cm away, which is crucial for clinical applications such as laparoscopy or percutaneous procedures, eliminating the need for additional empirical correction factors. Furthermore, the curved array design significantly enhances anti-interference capabilities. By pairing magnetic sensors at both ends of the housing's diameter, spatially uniform interference fields (such as mains interference or interference from elevator motors) induce voltages of equal amplitude but opposite phase in these pairs. A simple subtraction circuit can reduce the common-mode signal by more than 20 dB. Large-scale optically pumped magnetometer (OPM) flux module testing shows that in three-dimensional cap-shaped studies, pairing and averaging the injected 9 Hz tone can suppress interference by approximately 500 times. This advantage greatly reduces the reliance on bulky magnetic shielding in magnetic navigation systems, improving the system's practicality and adaptability.

[0053] The MCU component 102 integrates a microcontroller, such as an STM32F103RET6 (a 32-bit high-performance ARM Cortex-M3 core-based microcontroller), and multiple (e.g., 4-6) triaxial digital magnetometer sensors.

[0054] The data transmission between the master and slave MCUs is achieved through an RS-485 bus 104 constructed by the MAX485ESA+T (low-power half-duplex RS-485 transceiver) chip. The RS-485 bus 104 is equipped with a 120Ω impedance matching resistor at the end and uses the Modbus RTU protocol (a serial communication protocol for remote terminal unit mode) to transmit preprocessed data at an adaptive rate of 1-12Mbps.

[0055] The main MCU component 103 also uses an STM32F103RET6 processor as its microcontroller, employing the same hardware configuration as the slave MCU component 102. Data transmission uses a bulk transfer mode, with each data packet being 512 bytes in size, containing a 64-byte frame header, a 384-byte payload, and 64 bytes of checksum information. The main MCU component 103 implements a USB composite device configuration, supporting both CDC communication (a standard protocol class for MCUs to implement virtual serial communication via USB) and custom bulk transfer endpoints. The former is used for debug information output, while the latter is used for high-speed data transmission. The data transmission system employs a double-buffering mechanism, with data collection in one buffer and USB transmission in the other, achieving seamless data flow.

[0056] The host computer 106's software is developed based on the Qt framework (a cross-platform C++ graphical user interface application development framework), and integrates a UKF (unscented Kalman filter) positioning algorithm module. It calculates three-dimensional coordinates in real time by analyzing the magnetic field gradient data collected by the three-axis digital magnetometer sensor, with a positioning accuracy of 1m. 3 Within the space, the accuracy is ±0.8mm. The Qt architecture adopts the Model-View-Controller (MVC) design pattern. The main interface is implemented based on Qt Quick (Qt framework's declarative interface development module), displaying the magnetic source's position and attitude information in real time. The data processing module adopts a multi-threaded design, with USB data reception, decryption, parsing, and algorithm processing executed in independent threads, and inter-thread communication is achieved through lock-free queues.

[0057] The magnetic field generator 107 uses a coil with a winding diameter of 1.5 mm and a length of 10 mm, wound with 500 turns, using copper wire with a diameter of 0.05 mm wound around a transparent synthetic resin core. Changes in current are precisely adjusted by a magnetic positioning tracking system and a signal control system 108 to generate a magnetic field in a specific frequency band, adapting to different environmental requirements.

[0058] The magnetic positioning and tracking system and signal control system 108 employ a high-precision constant current source design to precisely control the current in the magnetic field generating coil. This constant current source is implemented based on a precision current source chip.

[0059] Example 2:

[0060] like Figure 3 As shown, this embodiment provides a passive measurement-type magnetic navigation method based on magnetic source imaging, including:

[0061] Step 1: Construct a passive measurement magnetic navigation system based on magnetic source imaging as described in Example 1;

[0062] Step 2: Construct a theoretical model for magnetic dipole navigation;

[0063] The magnetic field generator 107 is placed in the working area of ​​the magnetic sensor array 101. The magnetic field generator 107 is approximated as a magnetic dipole, and its main state variables include: Current position of magnetic source Speed ​​of movement and magnetic moment .in, Representing time respectively The position coordinates of the magnetic field generator 107 in three-dimensional space; Representing time respectively Velocity components of magnetic field generator 107 in three-dimensional coordinate directions; Representing time respectively The components of the magnetic moment of magnetic field generator 107 in the three-dimensional coordinate direction.

[0064] Assuming the magnetic field source 107 moves according to a uniform motion model, meaning its position is the previous position plus the velocity multiplied by the time interval (the time interval between each position estimation), and the velocity and magnetic moment remain constant, the corresponding process model can be written as follows:

[0065] ;

[0066] in, Let the system process state vector be... This is a state propagation model for uniform motion; For the magnetic field source 107 at time The position vector, where three elements are the magnetic field source 107 at time 107. Position coordinates in three-dimensional space; For the magnetic field source 107 at time The velocity vector, where three elements are the magnetic field source 107 at time 107. Velocity components in three-dimensional coordinate directions; The time interval between two adjacent state estimates; For the magnetic field source 107 at time The magnetic moment vector, where three elements are the magnetic moments of the magnetic field source 107 at time t. Components in three-dimensional coordinate directions; process noise Follows a normal distribution Process noise covariance matrix Used to simulate unmodeled acceleration and magnetic moment disturbances.

[0067] The observation model of the magnetic sensor array 101 adopts the magnetic dipole magnetic field formula, that is, the first... Theoretical magnetic field observations from each sensor for:

[0068] ;

[0069] in, Position of magnetic dipole and the Sensor locations The distance between them; For the first The observation noise of each sensor follows a mean of 0 and a covariance of . Gaussian distribution; The vacuum permeability; It is the Euclidean norm.

[0070] Step 3: Perform noise suppression processing on the raw observation data of the detected magnetic field source 107;

[0071] During magnetic field detection, noise originates from various sources, such as interference from other magnetic fields in the environment, thermal noise from the sensor, or electromagnetic interference. Since the magnetic field generated by the miniature coil is limited, this noise significantly impacts the observation data and reduces positioning accuracy. Therefore, before proceeding to subsequent steps, noise suppression processing is required on the raw observation data of the detected magnetic field source 107. Considering that the magnetic field source 107 is related to the input signal characteristics of the signal generator, only the magnetic field signal within a specific frequency range of the signal generator's input signal is retained during magnetic field detection, while noise in other frequency bands is filtered out.

[0072] Step 4: Allow the sensor to stand still for calibration;

[0073] A millimeter-scale cubic spatial grid is constructed with the geometric center of the magnetic sensor array 101 as the origin. The magnetic field generator 107 is fixed, meaning the initial velocities in all directions are set to zero. Point-by-point scanning and fitting are initiated, and the lead field matrix L is constructed by combining theoretical magnetic field observations. The optimal state parameters are then searched using least-squares estimation. and The search target is set as follows:

[0074] ;

[0075] in, This is the sum of the variances between the theoretically calculated observed magnetic field vector and the actual observed magnetic field vector. The first one calculated based on the theoretical model Each sensor at time Magnetic field observation vector; For the first Each sensor at time The actual observed magnetic field vector; It is the identity matrix; Here is the lead field matrix; for The pseudo-inverse matrix; It is the square of the Frobenius norm, used to measure the overall error of the matrix.

[0076] This allows for iterative acquisition of the optimal initial magnetic moment and position information. During the static period of the magnetic field generator 107, samples are simultaneously acquired to estimate the observation covariance. Process noise covariance The initial values ​​of the state variable covariance matrix P need to be determined based on actual working conditions and experience to ensure convergence of subsequent iterations.

[0077] Step 5: Generate the Sigma sampling point set and set the weights for each point when calculating the mean and covariance;

[0078] Calculate scaling factor :

[0079] ;

[0080] In the formula, This is the scaling parameter for UKF, used to control the degree of dispersion of Sigma points relative to the state distribution; The dimension of the state vector is given. In this invention, the state variables include three three-dimensional vectors: position, velocity, and magnetic moment. ; This is a secondary scaling parameter used to further adjust the distribution of Sigma points.

[0081] Sigma sampling point set Generate as:

[0082] ;

[0083] in, For the element's first List, For the element's first List, To indicate time The mean vector of the state estimate Indicates time The state covariance matrix.

[0084] The weights are generated as follows:

[0085] ;

[0086] in, Indicates mean weight, Indicates the covariance weights; , These represent the mean weight and covariance weight of the Sigma point with index 0, respectively. , The first 1 (serial number is) The mean weight and covariance weight of the Sigma points; The value range is [0.0001, 1]; This is a higher-order error compensation parameter, which is related to prior information (taken as 2 for Gaussian distribution). It is important to note the process parameters. , , The scope of experience is well known in the field and will not be elaborated here.

[0087] Step 6: Propagate the Sigma sampling point set using the process model to complete the state prediction;

[0088] Propagation of Sigma sampling point set through process model :

[0089] ;

[0090] in, Indicates at time The generated first Sigma points, The state transition function represents the system. This is process noise.

[0091] Calculation based on time The state prediction time State prediction mean and according to time The state prediction time State prediction covariance matrix :

[0092] ;

[0093] in, This is the transpose of the matrix.

[0094] Step 7: Use the observation model to correct the state prediction results of Step 6 to complete the observation update;

[0095] Calculate the first using the observation model The Sigma point at time 1 Sensor predicted measurement value:

[0096] ;

[0097] in, To measure noise, This is the observation model.

[0098] Based on this, the statistics of the predicted observations are calculated, including the predicted observation mean. Predicted observation covariance and the cross covariance between state and observation :

[0099] ;

[0100] Calculate Kalman gain :

[0101] ;

[0102] Update status:

[0103] ;

[0104] in, For Kalman gain, The updated state covariance;

[0105] Corrected estimation results:

[0106] ;

[0107] in, For the updated state estimate, To predict the state mean, For observation purposes.

[0108] The final estimated location of the magnetic source is displayed in real time on the host computer 106.

[0109] The magnetic source tracking and positioning method provided in this embodiment can achieve high-precision estimation of magnetic source state quantities (including position, velocity, magnetic moment, etc.) through steps such as constructing a theoretical model, static calibration, state prediction, and observation correction.

[0110] The method steps of the present invention are implemented at least in part by computer program instructions, which can be stored in a computer-readable storage medium and executed by a processor to perform functions such as magnetic field data acquisition, noise suppression, state estimation and positioning correction.

[0111] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0116] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0117] The above description is merely an embodiment of the present invention and does not limit the scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related system fields, are similarly included within the protection scope of the present invention.

[0118] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A passive measurement-based magnetic navigation system based on magnetic source imaging, characterized in that, Comprise: a magnetic sensor array for detecting and feeding back the magnetic field changes in the environment; the magnetic sensor array adopts a modular expansion design and is composed of a plurality of magnetic sensing units for detecting a curved surface, each magnetic sensing unit comprises a planar substrate, a magnetic sensor chip and a slave MCU assembly; the adjacent magnetic sensing units are connected through an adjustable connecting mechanism, the included angle between the adjustable connecting mechanisms is flexibly adjusted and locked within a certain range, so that the whole magnetic sensor array is adapted to detection surfaces with different curvatures; the overall structure supports modular expansion, and the number of magnetic sensing units can be increased or decreased according to actual detection needs, so that detection arrays of different sizes and shapes can be flexibly constructed; a plurality of slave MCU assemblies for receiving, locally aggregating and transmitting data of the magnetic sensor array; a master MCU assembly for aggregating data received by the slave MCU assemblies and transmitting the data to an upper computer; an RS-485 bus for stable communication between the master MCU assembly and the plurality of slave MCU assemblies; a USB interface for high-speed data communication between the master MCU assembly and the upper computer; the upper computer is used for receiving and processing data of the magnetic sensor array in real time, managing a magnetic positioning and tracking system and a signal control system, and realizing comprehensive monitoring and control of a navigation process; a magnetic field generating source composed of a small powered coil, which is a multi-layer columnar transmitting coil, receives the adjustment control current change of the magnetic positioning and tracking system and the signal control system to generate a specific frequency band magnetic field control signal, and generates a corresponding adjustment control current change to generate a specific frequency band magnetic field; the magnetic positioning and tracking system and the signal control system are respectively used for outputting a precise positioning signal of the catheter based on the magnetic tracking method and generating a control signal of the adjustment control current change to generate a specific frequency band magnetic field.

2. The passive measurement-based magnetic source imaging navigation system of claim 1, wherein, The slave MCU assembly integrates a microcontroller and a plurality of three-axis digital magnetometer sensors; the master MCU assembly and the slave MCU assembly adopt the same kind of microcontroller.

3. The passive measurement-based magnetic source imaging navigation system of claim 1, wherein, The software of the upper computer is based on a Qt framework positioning algorithm module, which calculates three-dimensional coordinates in real time by analyzing magnetic field gradient data collected by the three-axis digital magnetometer sensor; the Qt framework adopts a model-view-controller design pattern, and the main interface is realized based on Qt Quick to display the position and attitude information of the magnetic source in real time.

4. The passive measurement-based magnetic source imaging navigation system of claim 1, wherein, The coil of the magnetic field generating source is wound with copper wire on a transparent synthetic resin core; the current change is accurately adjusted by the magnetic positioning and tracking system and the signal control system.

5. A passive measurement-based magnetic navigation method based on magnetic source imaging, for the passive measurement-based magnetic navigation system based on magnetic source imaging according to any one of claims 1 to 4, characterized in that, Comprise: Step 1, building the passive measurement type magnetic navigation system based on magnetic source imaging; Step 2, constructing a magnetic dipole navigation theoretical model; Step 3, performing noise suppression processing on the original observation data of the detected magnetic field generating source; Step 4, calibrating the sensor; Step 5, generating a Sigma sampling point set and setting the weight of each point when calculating the mean and covariance; Step 6, propagating the Sigma sampling point set using the process model to complete state prediction; Step 7, correcting the state prediction result of step 6 using the observation model to complete observation update.

6. The passive measurement-based magnetic source imaging method of claim 5, wherein, Step 2 includes: putting the magnetic field source into the working area of the magnetic sensor array, approximating the magnetic field source as a magnetic dipole, and its main state quantities include: the position of the magnetic source at the moment , the motion speed , and the magnetic moment ; wherein respectively represent time instants position coordinates of the magnetic field generating source in a three-dimensional space; respectively represent time instants velocity components of the magnetic field generating source in the three-dimensional coordinate directions; respectively represent time instants components of the magnetic moment of the magnetic field generating source in the three-dimensional coordinate directions; Assuming that the magnetic field generating source moves according to a uniform motion model, the corresponding process model is: ； in, Let the system process state vector be... This is a state propagation model for uniform motion. For the source of the magnetic field at time The position vector, where three elements represent the magnetic field source at time t. Position coordinates in three-dimensional space; For the source of the magnetic field at time The velocity vector, where three elements represent the magnetic field source at time t. Velocity components in three-dimensional coordinate directions; The time interval between two adjacent state estimates; For the source of the magnetic field at time The magnetic moment vector, where the three elements are the magnetic moments of the magnetic field source at time t. Components in three-dimensional coordinate directions; process noise Follows a normal distribution Process noise covariance matrix Used to simulate unmodeled acceleration and magnetic moment disturbances; The theoretical magnetic field observation value of the first sensor of the magnetic sensor array in the constructed magnetic dipole navigation theoretical model is: Bx1= -B0 / (2πr2) sinθ ​ ； in, Position of magnetic dipole and the Sensor locations The distance between them; For the first The observation noise of each sensor follows a mean of 0 and a covariance of . Gaussian distribution; Permeability of free space; It is the Euclidean norm.

7. The passive measurement-based magnetic source imaging method of claim 6, wherein, Step 4 includes: taking the geometric center of the magnetic sensor array as the origin, constructing a millimeter-level cubic space grid; fixing the magnetic field source, starting point-by-point scanning fitting, combining with the theoretical magnetic field observation value, constructing the lead field matrix L, and using the least square estimation to search for the optimal state parameters and ; the search target is set as: ； in, This is the sum of the variances between the theoretically calculated observed magnetic field vector and the actual observed magnetic field vector. The first one calculated based on the theoretical model Each sensor at time Magnetic field observation vector; For the first Each sensor at time The actual observed magnetic field vector; It is the identity matrix; Here is the lead field matrix; for The pseudo-inverse matrix; The square of the Frobenius norm; The optimal initial magnetic moment and position information are obtained by iteration; during the static period of the magnetic field generating source, the sample is synchronously collected to estimate the observation covariance ; the initial value of the process noise covariance and the state variable covariance matrix P needs to be determined according to the actual working condition and experience to make the subsequent iteration converge.

8. The passive measurement-based magnetic source imaging method of claim 7, wherein, Step 5 includes: Sigma sample point set Generated as: ； wherein, represents the first column of the element, represents the first column of the element, is a state estimation mean vector at time , and is a state covariance matrix at time . The weight generation is: ； wherein, denotes the mean weight, denotes the covariance weight; , denote the mean weight and the covariance weight of the Sigma point with index 0, respectively; , denote the mean weight and the covariance weight of the Sigma point with index i, respectively; denote the mean weight and the covariance weight of the Sigma point with index i, respectively; the value range of is [0.0001, 1]; is a high-order error compensation parameter, ; scaling factor : ; wherein, is a scaling parameter of the UKF for controlling the degree of spread of the Sigma points with respect to the state distribution; is the dimension of the state vector; is a secondary scaling parameter for further adjusting the distribution of the Sigma points.

9. The passive measurement-based magnetic source imaging method of claim 8, wherein, Step 6 includes propagating the set of Sigma sample points through the process model : ； wherein, is the state of the system at time is the state of the system at time is the state of the system at time is the state of the system at time is the state of the system at time the state prediction at time the state prediction mean at time the state prediction covariance matrix at time the state prediction at time the state prediction at time the state prediction at time : ； wherein is the transpose of the matrix.

10. The passive measurement-based magnetic source imaging method of claim 9, wherein, Step 7 comprises calculating sensor predicted measurements at time from the set of Sigma points by the observation model: ​ ； wherein, to measure the noise, is the observation model; computing statistics of the predicted observations, including predicted observation mean , predicted observation covariance , and cross-covariance between state and observations : ； Computing the Kalman gain : ； The updated state is: ； wherein, is the Kalman gain, is the updated state covariance; The corrected estimation result is: ； wherein, is the updated state estimate, is the predicted state mean, is the observation.

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