Visual guidance of implantable myocardial contractility modulation device

Abstract

The application provides a visual guiding implantable myocardial contractility adjusting device, comprising: collecting image, posture, magnetic field and wireless coupling data; calculating real-time three-dimensional coordinates of an implant unit through a multi-source fusion algorithm, and calculating a real-time coupling coefficient through a coupling estimation algorithm; judging whether the coupling coefficient is lower than a threshold value, if yes, generating guiding instructions for restoring the coupling as a priority; if not, calculating a guiding adjustment amount based on implant deviation and generating guiding instructions, and finally performing visual presentation. The application improves positioning accuracy through multi-source information fusion, and establishes a power-guiding collaborative mechanism to ensure that the implant position takes into account positioning accuracy and power efficiency, thereby reducing system cost and operation complexity.

Description

Technical Field

[0001] This invention relates to the field of implantable medical device technology, and more specifically, to a visually guided implantable myocardial contractility modulation device. Background Technology

[0002] Implantable medical devices such as implantable myocardial contractility modulators (CCMs) typically require precise placement of their implanted units in specific locations within the body, ensuring close contact between their electrodes and target tissues to guarantee the effectiveness of subsequent wireless power supply and treatment.

[0003] In existing technologies, the implantation process of such devices mainly relies on the doctor's clinical experience and uses routine single imaging examinations for positioning. This approach has several drawbacks: First, due to the lack of real-time, multi-dimensional positional feedback, doctors find it difficult to accurately perceive the three-dimensional posture and position of the implanted unit within deep soft tissue, leading to potential implantation deviations and affecting electrode adhesion. Second, the implantation guidance process is independent of the wireless power supply system, making it impossible to monitor and provide real-time feedback on the impact of implantation position changes on energy transmission coupling. This can result in a significant decrease in wireless power supply efficiency due to poor coupling even when the position appears correct, posing a potential risk for postoperative use. Furthermore, while some existing wireless power supply systems include sensors for posture perception, their purpose is primarily to automatically adjust the energy beam to optimize energy transmission efficiency, without incorporating multi-source information such as external images and magnetic fields to provide doctors with intuitive and precise visual guidance for manual operation. Additionally, the dedicated guidance hardware introduced for precise positioning significantly increases the overall cost, size, and implantation complexity of the system. Therefore, there is a lack of low-cost solutions in the existing technology that can deeply integrate high-precision, multi-source information fusion positioning with wireless power status monitoring, and provide real-time, quantitative guidance for doctors during manual implantation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a visually guided implantable myocardial contractility modulation device.

[0005] According to the present invention, a visually guided implantable myocardial contractility modulation device is provided, the visually guided implantable myocardial contractility modulation device comprising an external transmitting unit and an internal implantation unit; The visualized guided implantable myocardial contractility modulator also includes: The multi-source information acquisition module is used to acquire image data of the implantation area, attitude data and magnetic field data acquired by sensors set on the implanted unit, and wireless coupling related data characterizing the coupling state between the external transmitting unit and the implanted unit. The real-time positioning and state estimation module is used to calculate the real-time three-dimensional coordinates of the implanted unit based on the image data, attitude data, and magnetic field data using a preset multi-source fusion positioning algorithm; and to calculate the real-time coupling coefficient based on the wireless coupling related data using a preset coupling state estimation algorithm. The collaborative guidance module is used to calculate the implantation deviation between the real-time three-dimensional coordinates and the preset target implantation position coordinates; determine whether the real-time coupling coefficient is lower than a preset coupling coefficient threshold; if the real-time coupling coefficient is lower than the coupling coefficient threshold, generate a guidance instruction aimed at restoring coupling stability; if the real-time coupling coefficient is not lower than the coupling coefficient threshold, calculate the guidance adjustment amount based on the implantation deviation, and generate a guidance instruction based on the guidance adjustment amount. And a display information generation module, used to generate display information for presenting the guidance instructions in real time on a visualization display device.

[0006] Furthermore, the wireless coupling related data includes: the transmission voltage and transmission current of the external transmitting unit, and the rectified voltage of the receiving coil of the internal implanted unit after rectification.

[0007] Furthermore, the multi-source fusion positioning algorithm specifically involves performing weighted fusion calculations on the image data, attitude data, and magnetic field data to obtain the real-time three-dimensional coordinates of the implanted unit.

[0008] Furthermore, the implantation deviation is the Euclidean distance between the real-time three-dimensional coordinates and the preset target implantation position coordinates; and, with two or more preset deviation thresholds, the collaborative guidance module further includes: comparing the Euclidean distance with the deviation thresholds to determine the deviation level; and, the guidance adjustment amount is determined based on the deviation level.

[0009] Furthermore, the guiding adjustment amount includes angle adjustment amount and displacement adjustment amount.

[0010] Furthermore, in the real-time positioning and state estimation module, when calculating the real-time three-dimensional coordinates and / or the real-time coupling coefficient, 16-bit fixed-point recursive calculation is used.

[0011] Furthermore, it also includes: Emergency handling module: When the value of any of the attitude data, magnetic field data, or wireless coupling related data exceeds the preset normal range, a prompt will be triggered.

[0012] Compared with the prior art, the present invention has the following beneficial effects: This invention effectively overcomes the shortcomings of inaccurate positioning in deep soft tissues by fusing data from three different sources: imaging, acceleration, and magnetic fields, and then weighting and calibrating them. This significantly improves positioning accuracy and ensures precise fit between the implanted unit and the target tissue.

[0013] This invention uses the coupling state of wireless power supply as a key parameter for guidance decision-making and establishes a "power supply priority" collaborative mechanism. When the implantation location affects the stability of power supply, the system can immediately issue a warning and prioritize the adjustment, ensuring that the final implantation location takes into account both positioning accuracy and power supply efficiency, and significantly improving the overall success rate of implantation.

[0014] This invention can reuse the original hardware of implantable systems, requiring only the integration of micro sensors, eliminating the need for expensive dedicated positioning equipment, thus significantly reducing hardware costs and system complexity, and making it easy to promote.

[0015] This invention transforms experience-dependent surgical procedures into standardized processes through precise algorithms and quantified guidance instructions. This effectively reduces the reliance on the surgeon's personal experience, shortens surgical time, and improves the consistency of implantation results. Attached Figure Description

[0016] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the architecture of a visually guided implantable myocardial contractility modulation device provided in an embodiment of this application.

[0017] Figure 2 This is a schematic diagram illustrating the workflow of a visually guided implantable myocardial contractility modulation device provided in an embodiment of this application.

[0018] Figure 3 This is a schematic diagram of signaling interaction timing provided for an embodiment of this application.

[0019] Figure 4 This is a schematic diagram of a visual guide interface provided in an embodiment of this application. Detailed Implementation

[0020] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0021] Example 1 This embodiment provides a visually guided implantable myocardial contractility modulator, which aims to provide visual guidance that balances high precision, real-time performance, and wireless power supply efficiency during the implantation process of the implantable myocardial contractility modulator.

[0022] Please see Figure 1 The diagram illustrates a system architecture for implementing embodiments of this application. The system includes an external visualization guidance unit 10 and an in vivo positioning assistance unit 20.

[0023] Specifically, the external visualization guidance unit 10 is the main interactive part for the operator during the surgery and can be designed as an integrated handheld device or console. This unit integrates a main control microcontroller unit (hereinafter referred to as the main control MCU 11), which serves as the core of the entire system's computing and control. It is responsible for executing the multi-source fusion positioning algorithm, coupled state estimation algorithm, and collaborative guidance decision logic, which will be detailed later. Connected to the main control MCU 11 are a visualization display module 12 (e.g., an LCD screen) for real-time presentation of guidance commands and system status information, and an interface for connecting to the image acquisition module 13. It should be noted that the image acquisition module 13 can be an external ultrasound probe used to acquire two-dimensional or three-dimensional image data of the implantation area (e.g., the patient's subclavian region) in real time. Furthermore, the external visualization guidance unit 10 also includes a transmitting coil 14, which functions to wirelessly power the device inside the body.

[0024] Accordingly, in one embodiment of this application, the in vivo positioning assistance unit 20 is integrated with the in vivo implantation unit of the implantable myocardial contractility modulation device. The core of this unit is a master control MCU 21, responsible for collecting and processing data from internal sensors and transmitting the data wirelessly to the external unit. This unit includes a receiving coil 22, whose main function is to receive energy from the external transmitting coil 14 to maintain its operation; simultaneously, the received electrical signals can also serve as a key data source for estimating the wireless coupling state. To achieve precise positioning, micro-sensors are integrated on the housing or internal circuit board of the in vivo positioning assistance unit 20, specifically including a triaxial accelerometer 23 and a magnetic field sensor 24. The triaxial accelerometer 23 is used to sense the device's attitude changes (such as tilting and rotation) and dynamic acceleration in three-dimensional space, while the magnetic field sensor 24 is used to sense the surrounding environmental magnetic field or a specific magnetic field generated by the external unit, thereby assisting in determining the device's position.

[0025] During operation, the transmitting coil 14 of the external visualization guidance unit 10 and the receiving coil 22 of the internal positioning assistance unit 20 form a resonant coupling to achieve wireless power transmission. At the same time, the image data acquired by the image acquisition module 13, the sensor data acquired by the triaxial accelerometer 23 and the magnetic field sensor 24 of the internal positioning assistance unit 20, and the electrical parameters characterizing the coupling state (such as the voltage and current of the transmitting coil 14 and the rectified voltage of the receiving coil 22) are collected to the external main control MCU 11 for unified processing via wired or wireless means.

[0026] The following will combine Figure 2 The workflow of the visually guided implantable myocardial contractility modulator in this embodiment is described in detail.

[0027] Step S101: Parameter Initialization. Before the guidance process begins, the system needs to load a series of preset parameters. Understandably, these parameters are obtained based on rigorous offline calibration experiments. For example, by performing imaging scans and wireless coupling tests on the subclavian region of 60 volunteers with different body types and subcutaneous fat thicknesses, a set of universally applicable optimized parameters was obtained through statistical analysis. These parameters specifically include: 1. Target implantation location coordinates (X0, Y0, Z0): This is the ideal three-dimensional spatial position that the myocardial contractility modulation device electrode needs to achieve in the standard human coordinate system to ensure the best treatment effect. 2. Weighting coefficients of the multi-source fusion localization algorithm: These coefficients determine the contribution of different data sources to the final localization result. In this embodiment, image data weights are set based on experimental data analysis. Acceleration data weights Magnetic field data weights This weighting setting reflects the high reliability of image data in deep soft tissue localization, supplemented by inertial and magnetic field data for fine-tuning of attitude and position. 3. Calibration Coefficients : Used to perform overall scaling correction on the preliminary results of the fusion calculation to compensate for systematic errors; in this embodiment, it is set to 1.05. 4. Coupling coefficient threshold : This is a critical value used to determine whether the wireless power supply coupling status is good. When the real-time calculated coupling coefficient is lower than this threshold, the system will determine that the coupling is abnormal. In this embodiment, based on a large number of experiments, this threshold is set to 0.3. 5. Implantation deviation grading threshold: This is used to classify the severity of positioning deviation. In this embodiment, two thresholds are set: Eth1=0.5mm and Eth2=2.0mm. When the deviation is less than 0.5mm, the positioning is considered to be sufficiently accurate; between 0.5mm and 2.0mm is a slight deviation that requires fine-tuning; and greater than 2.0mm is a serious deviation that requires significant adjustment. It should be noted that, to ensure the efficient operation of the system on the low-power embedded main control MCUs 11 and 21, all subsequent algorithms in this embodiment can use 16-bit fixed-point recursive operations, thereby avoiding resource- and time-consuming floating-point operations and ensuring the real-time response capability of the system.

[0028] Step S102: Synchronous Acquisition of Multi-Source Information. After system startup, the system performs synchronous acquisition of multi-source information in a fixed cycle (e.g., 100 milliseconds). Please refer to [link to relevant documentation]. Figure 3 As shown in the timing diagram, within a typical 100-millisecond cycle, the main control MCU 11 of the external unit simultaneously triggers multiple data acquisition actions: it sends a data acquisition request to the image acquisition module 13 to obtain the latest implantation area image data stream; simultaneously, through changes in the wireless field or a specific communication protocol, it triggers the main control MCU 21 of the internal unit to report its acquired data. Correspondingly, the internal unit packages and reports wirelessly coupled related data, such as real-time attitude data from the triaxial accelerometer 23, magnetic field data from the magnetic field sensor 24, and voltage data obtained from the rectifier circuit of the receiving coil 22, to the external unit. This synchronous acquisition mechanism ensures that all data used in subsequent fusion calculations correspond to the state at the same moment, thus providing a time-aligned data foundation for precise positioning. After acquiring the raw data, the main control MCU 11 first performs information preprocessing steps, such as applying a second-order recursive filtering algorithm to the continuously acquired sensor data to filter out high-frequency noise and invalid data jumps caused by minor hand tremors of the operator, making the data stream smoother.

[0029] Step S103: Multi-source fusion positioning calculation. This step is the core of achieving high-precision positioning. The main control MCU 11 uses the pre-processed data and a preset multi-source fusion positioning algorithm to calculate the real-time three-dimensional coordinates (X(t), Y(t), Z(t)) of the in-body positioning auxiliary unit 20. In this embodiment, the algorithm is specifically manifested as a weighted fusion formula. Taking the calculation of the X-axis coordinate as an example, the formula is as follows:

[0030] in, The fused X coordinates calculated at time t; These are the preset calibration coefficients; , , These are the weighting coefficients for each data source; The X-axis coordinate components of the implanted unit extracted from image data using an image recognition algorithm; The change in position on the X-axis is obtained by integration or attitude calculation based on data from the triaxial accelerometer 23. The position component on the X-axis is calculated based on data from the magnetic field sensor 24 and a magnetic field model. Similar formulas are used to calculate the coordinates Y(t) and Z(t) on the Y and Z axes. Thus, by fusing data from different modalities and with different physical characteristics, the inherent limitations of a single data source can be effectively compensated for (e.g., images may be obstructed by bones, long-term integration of inertial sensors can cause drift, and magnetic fields may be interfered with by metal instruments), achieving complementary advantages and obtaining stable and accurate positioning results.

[0031] Step S104: Coupling State Estimation. In parallel with the positioning calculation, the main control MCU 11 also needs to evaluate the efficiency of wireless power supply, i.e., the coupling state between the external transmitting coil 14 and the internal receiving coil 22. This function is implemented through a preset coupling state estimation algorithm. In this embodiment, the collected wireless coupling-related data specifically includes: the transmission voltage of the external transmitting unit. and emission current And the rectified voltage after rectification by the receiving coil 22 of the implanted unit. The formula for estimating the real-time coupling coefficient k(t) is:

[0032] This formula combines two factors—voltage transfer ratio and transmitter impedance variation—to evaluate the degree of coupling. Among them, It is a reference impedance related to the inherent characteristics of the system and is a preset constant. The coupling coefficient k(t) is a dimensionless number between 0 and 1. The larger the value, the better the coupling and the higher the energy transfer efficiency.

[0033] Step S105: Determine if the coupling coefficient is below the threshold. This step is a key decision point for achieving power supply-guidance coordination. The main control MCU 11 compares the real-time coupling coefficient k(t) calculated in step S104 with the preset coupling coefficient threshold in step S101. (For example, 0.3) for comparison. If k(t) is lower than This indicates that the current position or orientation of the implanted unit has severely affected the stability of wireless power supply. Even if the position is close to the target, implantation will fail if effective power supply is not available. Therefore, the system will prioritize generating guidance instructions aimed at restoring coupling stability, and the process will enter the priority processing branch of cooperative guidance. Conversely, if k(t) is not lower than... This indicates that the current power supply status is good, and the process enters the normal position guidance branch.

[0034] Step S106: Calculate the implantation deviation. Under normal power supply conditions, the system focuses on positioning accuracy. The main control MCU 11 calculates the spatial difference between the current real-time three-dimensional coordinates (X(t), Y(t), Z(t)) and the preset target implantation position coordinates (X0, Y0, Z0), i.e., the implantation deviation E(t). In this embodiment, this deviation is quantified by calculating the Euclidean distance between the two:

[0035] Step S107: Generate guidance instructions. The system generates and presents guidance instructions to the operator based on the decision result. If the judgment result of step S105 is "yes" (i.e., the coupling coefficient is lower than the threshold), the generated guidance instructions will be suggestive, such as displaying prominent messages like "Coupling abnormal, please adjust posture!" or "Coupling too low, please lift slightly!" on the visualization display module 12. At this time, the system will temporarily ignore the position deviation and guide the operator to solve the power supply problem first. If the judgment result of step S105 is "no" (i.e., the coupling coefficient is normal), the system will calculate the guidance adjustment amount based on the implantation deviation E(t) calculated in step S106. First, E(t) is compared with the preset deviation thresholds Eth1 and Eth2 to determine the deviation level (e.g., "severe deviation" or "slight deviation"). Then, based on the direction of the deviation (i.e., the vector from the current point to the target point) and the deviation level, a specific guidance adjustment amount is calculated using a preset guidance correction formula. This adjustment amount may include an angle adjustment amount (e.g., the degree to rotate left) and a displacement adjustment amount (e.g., the millimeter to translate upwards). Finally, guidance commands are generated based on the calculated guidance adjustment amount, such as "Up: 1.2mm, Left: 8°".

[0036] The system then generates display information for presenting these guidance instructions in real time on the visualization display module 12. Please refer to... Figure 4 This is a schematic diagram of a visual guidance interface. The interface can be divided into different areas: the guidance adjustment display area 401 clearly displays the angle and displacement adjustments using numbers and arrows; the coupling status display area 402 displays the value of the coupling coefficient k(t) in real time using a dashboard or a color-changing progress bar, and indicates the threshold value. The location; and the system status prompt area 403, which is used to display text information such as "Locking in progress", "Coupling abnormality" or "Implantation complete".

[0037] Step S108: Determine if implantation is complete. At the end of each cycle, the system checks whether the conditions for implantation completion are met. In this embodiment, these conditions can be set as follows: within 5 consecutive acquisition cycles (i.e., 500 milliseconds), the implantation deviation E(t) is always less than or equal to Eth1 (0.5 mm), and the real-time coupling coefficient k(t) is always not less than... (0.3). If this condition is met, the system determines that the implantation is successful and displays "Implantation Complete" in the system status prompt area 403, and the process ends. If the condition is not met, the process returns to step S102 and begins the next cycle of information collection and guidance update.

[0038] In addition, an emergency handling mechanism not shown in the main flowchart is included throughout the process. If any data anomaly is detected during data acquisition, such as signal loss from the image acquisition module 13, readings from the triaxial accelerometer 23 or the magnetic field sensor 24 exceeding the preset normal range, or complete interruption of wireless coupling, the system will immediately trigger a prompt and display the corresponding warning message in the system status prompt area 403 to ensure operational safety.

[0039] To better understand the technical solution of this application, a specific work scenario will be used for illustration below. A doctor is performing an implantation surgery on a patient with a myocardial contractility modulator. The operator holds an implantation tool with an in-vivo positioning assistance unit 20 and advances it through a small incision into the patient's subclavian region. The visualization display module 12 in front of the operator displays guidance information in real time. Initially, the guidance adjustment display area 401 displays "Down: 8mm, Right: 5mm", and the doctor adjusts the direction and depth of the implantation tool according to this instruction. During the adjustment process, the operator observes that the gauge pointer in the coupling status display area 402 suddenly enters the red warning zone, and at the same time, the system status prompt area 403 displays the text "Coupling abnormal, please lift slightly". This indicates that although the current position may be closer to the target, the poor posture has caused a sharp drop in energy supply efficiency. Following this priority instruction, the doctor lifts and rotates the implantation tool slightly, and observes that the pointer of the coupling gauge quickly returns to the safe green zone. After this, the guidance instruction for positional deviation becomes dominant again, and the operator continues to make fine adjustments according to instructions such as "Down: 3mm, Left: 2°". After several adjustments, the value in the guidance adjustment display area gradually approached zero. Finally, the system status indicator 403 displayed the green text "Implantation Complete" and emitted a notification sound. Compared to the traditional method that relies on touch and intraoperative X-rays, the entire process is more intuitive, quantitative, and efficient, and the positioning accuracy and energy supply efficiency of the final implantation location are both guaranteed.

[0040] Example 2 As an optional implementation, this embodiment provides a modified visualization presentation scheme based on embodiment 1, aiming to further reduce the cognitive load of the operator and make the guidance process more intuitive. The system hardware architecture, parameter initialization, information collection, positioning and state estimation algorithms (steps S101 to S104) of this embodiment are the same as those of embodiment 1. The core difference lies in the way the display information is generated in the collaborative guidance step (step S107) and the interface presented on the visualization display module 12.

[0041] In this embodiment, the visualization display module 12 no longer displays the same information as in Embodiment 1. Figure 4 Instead of a two-dimensional interface dominated by numbers and dashboards, the interface rendered is a three-dimensional virtual space. In this three-dimensional space, the preset target implantation position coordinates (X0, Y0, Z0) are rendered as a fixed, semi-transparent geometric shape, such as a "bullseye" or "target bounding box".

[0042] Simultaneously, based on the three-dimensional coordinates (X(t), Y(t), Z(t)) of the in vivo positioning assistance unit 20 calculated in real time in step S103, and the real-time attitude obtained through attitude calculation, the system renders a solid model representing the implanted unit in three-dimensional space. Thus, the implantation deviation is intuitively represented as the difference in spatial distance and attitude angle between the solid model and the semi-transparent "bullseye." Correspondingly, the operator's task changes from interpreting abstract numbers and arrows to intuitively aligning the moving solid model with the fixed "bullseye" in three-dimensional space.

[0043] Furthermore, this embodiment deeply integrates the real-time coupling coefficient k(t) calculated in step S104 with the visualization model. The color of the entity model can dynamically change according to the value of k(t). For example, the following rule can be set: - When k(t) ≥ When the value is 0.3 (for example), the solid model is displayed in green, indicating that the power supply is good and the operator can focus on the alignment of position and orientation.

[0044] when >k(t) ≥ 0.1 When this happens, the physical model turns yellow as a warning, indicating that the power supply efficiency at the current location has decreased and adjustments need to be made.

[0045] When k(t) < 0.1 When the solid model turns bright red and may flash, it indicates a severe abnormality in the coupling state. At this time, no matter how small the positional deviation is, the operator should prioritize adjusting the posture and position to restore the model to green or yellow.

[0046] In a work scenario employing this embodiment, the doctor's attention can be fully focused on the 3D display interface. They see a green model representing the implantable unit floating on the screen, with a semi-transparent blue target frame in the distance. Their task is to precisely "place" the green model into the blue target frame using the implantation tool in their hand. As they move towards the target frame, if the model's color changes from green to yellow, they will instinctively fine-tune the tool's angle until the model returns to green, and then continue moving towards the target frame. This "what you see is what you get" interaction simplifies the complex multi-dimensional adjustment task into a process similar to a 3D game, greatly reducing the operational difficulty and learning curve. This allows doctors to complete implantations more quickly and confidently, especially in difficult cases requiring implantation along complex paths, where the advantages of this intuitive feedback are even more pronounced.

[0047] Example 3 This embodiment provides an optimization scheme for the positioning algorithm in Embodiment 1, aiming to obtain smoother and more robust positioning results. The system architecture and other workflow steps (such as parameter initialization, information collection, coupling estimation, and collaborative guidance) of this embodiment are basically consistent with Embodiment 1. Its core improvement lies in the specific implementation method of step S103, "multi-source fusion positioning calculation".

[0048] In Example 1, a fusion formula based on static weighted average is used. This scheme is simple and efficient, but in some cases, such as when the data from a certain sensor experiences a large jump due to sudden interference, the positioning result may exhibit brief fluctuations. To solve this problem, this example introduces a Kalman filter; specifically, an extended Kalman filter can be used instead of the original weighted fusion algorithm.

[0049] In the extended Kalman filter-based scheme, the system state is defined as a state vector containing more information. This state vector includes not only the three-dimensional position (x, y, z) of the implanted unit, but also its three-dimensional velocity (vx, vy, vz) and quaternions or Euler angles representing the attitude.

[0050] The filtering process follows a classic "prediction-update" cycle: 1. Prediction Step: At the beginning of each time period, the filter predicts the current state of the implanted unit based on the state of the previous moment and the system's kinematic model. For example, assuming the implanted unit moves at a constant velocity in a straight line for a short time, the current position can be predicted by adding the product of the velocity and the time interval to the position of the previous moment. This predicted value is a preliminary estimate of the current true state. 2. Update Step: Subsequently, the system acquires the latest measurements from the image acquisition module 13, the triaxial accelerometer 23, and the magnetic field sensor 24. These measurements (e.g., the position extracted from the ultrasound image, the position increment obtained by the second integral of the accelerometer, and the position calculated by the magnetometer) are considered as observations of the true state. The extended Kalman filter compares the predicted value with these actual observations, calculates the difference between them (i.e., the innovation), and then calculates the Kalman gain based on the pre-set noise covariance of each sensor (i.e., a quantification of the reliability of each sensor measurement). Finally, the predicted state is weighted and corrected using the Kalman gain to obtain the optimal state estimate for the current moment. The optimal estimate is the filtered, smoother, and more accurate positioning result.

[0051] Using the system of this embodiment, the operator can obtain smoother and more stable guidance commands during the implantation procedure. Even during the implantation process, slight physiological movements in the implantation area due to the patient's breathing or heartbeat, or occasional artifacts in the ultrasound image, may cause spikes or small jumps in the raw sensor data. However, thanks to the inherent prediction and data smoothing mechanism of the Kalman filter, these noises and sudden interferences can be effectively suppressed, outputting a smooth and continuous motion trajectory. Reflected on the visualization display module 12, the value of the guidance adjustment will not exhibit meaningless and frequent back-and-forth fluctuations, allowing the doctor to follow the commands more stably and calmly, further improving the reliability and final accuracy of the implantation.

[0052] Example 4 This embodiment provides a variant at the system hardware architecture level, aiming to achieve system miniaturization, cost reduction, and ease of upgrade. Its core idea is to separate the main computing and display functions from dedicated external hardware and transfer them to general-purpose mobile smart terminals (such as tablets or smartphones).

[0053] In this embodiment, the integrated in vitro visualization guidance unit 10 in the original embodiment 1 is split into three more functionally independent parts: 1. An independent wireless ultrasound probe: This probe is responsible for acquiring image data and transmitting the image data stream to a mobile smart terminal in real time via a standard wireless communication protocol (e.g., Wi-Fi). 2. A simplified external transmitter box: The function of this transmitter box is greatly simplified, and its main responsibilities include only two: First, providing wireless power to the implanted unit in the body through its built-in transmitter coil 14; Second, acting as a data relay station through a low-power Bluetooth module, communicating with the in vivo positioning assistance unit 20 on the one hand, receiving sensor data and coupling-related data reported by the latter, and forwarding this data along with its own transmitter electrical parameters (transmission voltage, current) to the mobile smart terminal via Bluetooth. 3. A mobile smart terminal running a dedicated guidance application: This can be a commercially available tablet computer with sufficient performance. All core computing tasks, including multi-source information fusion, coupling state estimation, and collaborative guidance decision-making performed by the main control MCU 11 in embodiment 1, are completed by a dedicated application running on this tablet computer. The application receives image data streams from the wireless ultrasound probe and all other sensor and electrical data from the external transmitter. Once the calculations are complete, the application renders and displays a guidance interface on the tablet's screen. This interface can be the digital dashboard style of Embodiment 1, the 3D model style of Embodiment 2, or a combination of both.

[0054] In a typical work scenario, the operating physician can place a tablet computer with a dedicated guidance application installed next to the operating table. With one hand, they operate the implantation tool (which contains an in vivo positioning assistance unit 20), while the other hand, or an assistant, operates a small wireless ultrasound probe to scan the patient's subclavian region. The tablet's screen can display a split-screen view: one side shows real-time ultrasound images from the wireless probe, and the other side displays the guidance interface generated by the application. The physician can simultaneously observe anatomical structures and quantified guidance information. Since all algorithmic logic is implemented in software, future upgrades to the algorithm, parameter adjustments, or interface optimizations can be achieved simply by updating the application through the app store, without replacing any hardware. This architecture significantly reduces the research and development and manufacturing costs of dedicated hardware, making the entire system more portable, flexible, and easy to deploy and promote quickly in medical institutions of different levels and configurations.

[0055] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0056] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A visually guided implantable myocardial contractility modulation device, characterized in that, The visually guided implantable myocardial contractility modulator includes an external transmitting unit and an internal implantation unit. The visualized guided implantable myocardial contractility modulator also includes: The multi-source information acquisition module is used to acquire image data of the implantation area, attitude data and magnetic field data acquired by sensors set on the implanted unit, and wireless coupling related data characterizing the coupling state between the external transmitting unit and the implanted unit. The real-time positioning and state estimation module is used to calculate the real-time three-dimensional coordinates of the implanted unit based on the image data, attitude data, and magnetic field data using a preset multi-source fusion positioning algorithm; and to calculate the real-time coupling coefficient based on the wireless coupling related data using a preset coupling state estimation algorithm. The collaborative guidance module is used to calculate the implantation deviation between the real-time three-dimensional coordinates and the preset target implantation position coordinates; determine whether the real-time coupling coefficient is lower than a preset coupling coefficient threshold; if the real-time coupling coefficient is lower than the coupling coefficient threshold, generate a guidance instruction aimed at restoring coupling stability; if the real-time coupling coefficient is not lower than the coupling coefficient threshold, calculate the guidance adjustment amount based on the implantation deviation, and generate a guidance instruction based on the guidance adjustment amount. And a display information generation module, used to generate display information for presenting the guidance instructions in real time on a visualization display device.

2. The visually guided implantable myocardial contractility modulation device according to claim 1, characterized in that, The wireless coupling-related data includes: the transmission voltage and transmission current of the external transmitting unit, and the rectified voltage of the receiving coil of the internal implanted unit.

3. The visually guided implantable myocardial contractility modulation device according to claim 1, characterized in that, The multi-source fusion localization algorithm specifically involves weighted fusion calculation of the image data, attitude data, and magnetic field data to obtain the real-time three-dimensional coordinates of the implanted unit.

4. The visually guided implantable myocardial contractility modulation device according to claim 1, characterized in that, The implantation deviation is the Euclidean distance between the real-time three-dimensional coordinates and the preset target implantation position coordinates; and there are two or more preset deviation thresholds. The collaborative guidance module further includes: comparing the Euclidean distance with the deviation thresholds to determine the deviation level; and the guidance adjustment amount is determined based on the deviation level.

5. The visually guided implantable myocardial contractility modulation device according to claim 1, characterized in that, The guidance adjustment includes angle adjustment and displacement adjustment.

6. The visually guided implantable myocardial contractility modulation device according to claim 1, characterized in that, In the real-time positioning and state estimation module, 16-bit fixed-point recursive calculation is used when calculating the real-time three-dimensional coordinates and / or the real-time coupling coefficient.

7. The visually guided implantable myocardial contractility modulation device according to claim 1, characterized in that, Also includes: Emergency handling module: When the value of any of the attitude data, magnetic field data, or wireless coupling related data exceeds the preset normal range, a prompt will be triggered.

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