Medical ultrasonic detection probe posture adjusting method and system

Abstract

The invention relates to the technical field of medical ultrasonic detection, and discloses a medical ultrasonic detection probe posture adjusting method and system. Through an attitude sensor integrating an inertial measurement unit and an optical sensor, high-precision fusion attitude information is generated by using a sensor fusion algorithm. The system calculates attitude deviation based on a preset target attitude model, and drives the adjusting mechanism to automatically correct the probe attitude through a proportional-integral-derivative control algorithm. And a dynamic weighting function and an ultrasonic image feedback mechanism are introduced, so that accurate control of deviation quantification and adaptive attitude calibration are realized. The problems that a traditional method depends on manual adjustment and is low in precision are solved, the ultrasonic image quality and diagnosis efficiency are remarkably improved, and the biomedical engineering industry is promoted to develop towards the intelligent and precise direction.

Description

Technical Field

[0001] This invention relates to the field of medical ultrasound testing technology, specifically to a method and system for adjusting the posture of a medical ultrasound testing probe. Background Technology

[0002] In medical ultrasound examinations, the probe's orientation directly affects image quality and diagnostic accuracy. Traditional methods rely on physicians manually adjusting the probe's angle and position, which is not only time-consuming and susceptible to subjective factors, but also makes it difficult to maintain consistently optimized probe orientation throughout the examination. Especially when examining complex anatomical structures or deep tissues, manual adjustments struggle to precisely control the probe's spatial orientation, leading to blurred images, increased artifacts, and impaired lesion identification. Furthermore, prolonged probe handling can cause physician fatigue, further reducing operational stability. Existing automatic adjustment technologies often rely on a single sensor, resulting in data drift and weak anti-interference capabilities. These technologies fail to meet the dual clinical demands for real-time performance and accuracy, hindering the widespread adoption of ultrasound technology and the improvement of diagnostic efficiency. Summary of the Invention

[0003] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a method and system for adjusting the posture of a medical ultrasound probe, thereby solving the problems of low accuracy and poor efficiency in existing ultrasound testing techniques, which rely on manual adjustment of the probe posture.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for adjusting the posture of a medical ultrasound detection probe, the method comprising: The real-time attitude data of the probe is obtained using an attitude sensor, which includes an inertial measurement unit and an optical sensor. The angular velocity data of the inertial measurement unit and the position data of the optical sensor are combined through a sensor fusion algorithm to generate fused attitude information. Based on a preset target posture model, the fused posture information is processed to calculate the posture deviation, including coordinate system transformation and deviation quantization, wherein the coordinate system transformation maps the probe coordinate system to the global coordinate system. A control signal is generated, which drives an adjustment mechanism through a proportional-integral-derivative (PI-DE) control algorithm. The adjustment mechanism includes a stepper motor and a linkage mechanism. The adjustment mechanism automatically adjusts the probe's posture, reducing posture deviation. The process is based on ultrasound image feedback optimization, which includes real-time analysis of image quality indicators, detection of edge contrast using an image sharpness assessment module, and iterative adjustment of the target posture model based on the assessment results to achieve adaptive posture calibration.

[0005] Preferably, in one possible implementation of the first aspect, the sensor fusion algorithm employs an extended Kalman filter to convert the time output of the inertial measurement unit... angular velocity data Time with optical sensor output Location data To merge; Establish state vector ,in For quaternion pose, To achieve zero bias in the gyroscope, This is a transpose operation; Through state prediction equations Update the integral of angular velocity, where This is quaternion multiplication. For time angular velocity data at that time The derivative of the quaternion attitude; Constructing observation equations using optical sensor position data ,in, For time The observed value at that time, Let be a rotation matrix. For time Location data at that time To observe noise; By combining the Kalman gain-weighted angular velocity integral with the optical position data, fused attitude information is output. .

[0006] Preferably, in one possible implementation of the first aspect, the coordinate system transformation specifically includes: Map the attitude data in the probe coordinate system to a global coordinate system based on the patient's body surface; Deviation quantification is achieved by calculating the Euler angle difference between the probe coordinate system and the global coordinate system; Based on the ideal Euler angle range in the preset target attitude model, determine the direction and magnitude of the pitch, yaw, and roll angle deviations in the real-time attitude.

[0007] Preferably, in one possible implementation of the first aspect, the deviation quantization introduces a dynamic weighting function to handle Euler angle differences: Define attitude deviation vector ,in , , These are pitch, yaw, and roll angle deviations, respectively. This is a transpose operation; Using weight matrix Weighted bias, where , , These are the weighting coefficients for pitch, yaw, and roll angle deviations, respectively. The weighting values ​​increase non-linearly with the deviation amplitude.

[0008] in , The gain coefficients are related to organizational characteristics, and the final output is the weighted bias norm. As a control input.

[0009] Preferably, in one possible implementation of the first aspect, the gain coefficients of the weight matrix are adaptively adjusted based on ultrasound image features: Establish image texture complexity Mapping function with gain coefficient:

[0010] in Calculated from the entropy value of the gray-level co-occurrence matrix of the image region. , , These are calibration constants; When the blood vessel boundary is detected The value enhances the sensitivity of the angle fine-tuning.

[0011] Preferably, in one possible implementation of the first aspect, when the proportional-integral-derivative control algorithm drives the adjustment mechanism: The attitude deviation is decomposed into three-dimensional translation and rotation components; The translation component controls the stepper motor to drive the probe to move linearly, while the rotation component controls the linkage mechanism to change the probe tilt angle. A dual closed-loop control structure is adopted, with the inner loop correcting displacement error in real time based on feedback from the motor encoder.

[0012] Preferably, in one possible implementation of the first aspect, the inner loop of the dual closed-loop control is designed with an anti-saturation integrator: Define the amplitude limit condition for the integral term:

[0013] in For the output of the anti-saturation integrator, The maximum value of the integral term. For displacement tracking error, It is a time-varying decay factor; When the linkage mechanism reaches the mechanical limit... The value is dynamically reduced by 50% to suppress the accumulation of integrals.

[0014] Preferably, in one possible implementation of the first aspect, the real-time image quality metrics include: Extracting organ boundary contours from ultrasound images using edge detection operators; The average gradient magnitude of the boundary pixels is calculated as the sharpness benchmark. When the baseline value is lower than the preset threshold, the target posture model iterative optimization process is triggered.

[0015] Preferably, in one possible implementation of the first aspect, the iterative adjustment of the target pose model employs gradient descent-based adaptive calibration: Building image sharpness With attitude parameters Response surface model:

[0016] in For image clarity Regarding attitude parameters gradient, Euler angles, , , These are pitch angle, yaw angle, and roll angle, respectively. This is a transpose operation; by Update target attitude. The learning rate, until Stop iteration, where For the updated target pose, For the current target posture, For image clarity exist gradient at, For gradient norm, This is the convergence threshold.

[0017] In a second aspect, the present invention provides a medical ultrasound probe posture adjustment system, the system comprising: The attitude acquisition module uses an attitude sensor to acquire real-time attitude data of the probe. The attitude sensor includes an inertial measurement unit and an optical sensor. The angular velocity data of the inertial measurement unit and the position data of the optical sensor are combined through a sensor fusion algorithm to generate fused attitude information. The deviation calculation module, based on a preset target attitude model, processes the fused attitude information to calculate the attitude deviation, including coordinate system transformation and deviation quantization, wherein the coordinate system transformation maps the probe coordinate system to the global coordinate system. A signal generation module generates a control signal, which drives an adjustment mechanism through a proportional-integral-derivative (PI-DE) control algorithm. The adjustment mechanism includes a stepper motor and a linkage mechanism. The attitude adjustment module automatically adjusts the attitude of the probe through the adjustment mechanism to reduce attitude deviation; The feedback optimization module is based on the ultrasound image feedback optimization and adjustment process. The optimization includes real-time analysis of image quality indicators, detection of edge contrast using the image sharpness assessment module, and iterative adjustment of the target posture model based on the assessment results to achieve adaptive posture calibration.

[0018] The beneficial effects of the present invention are as follows: The intelligent probe posture adjustment system of the present invention realizes real-time, high-precision automatic calibration of probe posture in ultrasound detection through multi-sensor fusion and adaptive control algorithm, which significantly improves image quality and diagnostic efficiency.

[0019] The system combines data from the inertial measurement unit and optical sensors, and uses an extended Kalman filter algorithm to eliminate accumulated errors, ensuring high reliability of attitude information. Simultaneously, an adaptive calibration mechanism based on ultrasound image feedback can dynamically optimize and adjust the strategy according to tissue characteristics, enhancing the system's adaptability to complex clinical scenarios.

[0020] This technology has driven the development of the biomedical engineering industry toward intelligence and precision, provided core technical support for the upgrading of ultrasound diagnostic equipment, and helped improve the quality and accessibility of medical services. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This application provides a flowchart of a method for adjusting the posture of a medical ultrasound probe.

[0023] Figure 2 This application provides a structural diagram of a medical ultrasound probe posture adjustment system.

[0024] Explanation of reference numerals in the attached diagram: 1-Attitude acquisition module, 2-Deviance calculation module, 3-Signal generation module, 4-Attitude adjustment module, 5-Feedback optimization module. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1: As Figure 1 As shown, the present invention provides a method for adjusting the posture of a medical ultrasound probe, comprising: The real-time attitude data of the probe is obtained by using an attitude sensor, which includes an inertial measurement unit and an optical sensor. The angular velocity data of the inertial measurement unit and the position data of the optical sensor are combined by a sensor fusion algorithm to generate fused attitude information.

[0027] In this embodiment, acquiring real-time attitude data of the probe using an attitude sensor is a key step in achieving automatic adjustment. The attitude sensor consists of a high-precision inertial measurement unit (IMU) and an optical positioning sensor. The IMU is a nine-axis sensor module (MPU-9250), and the optical sensor uses the OptiTrack Prime 41 positioning system based on infrared markers. The sensor synchronously acquires data at a sampling frequency of 200Hz: the IMU outputs angular velocity data. The optical sensor outputs three-dimensional position data of the probe. ,in For time Angular velocity data vector at time, , , Representing the coordinate system around the probe respectively axis, axis, angular velocity components of the axis, For time Location data vector at time, , , These represent the probes measured by the optical sensor at... axis, axis, Position coordinates on the axis. To eliminate time delay differences in multi-sensor data, the system uses hardware trigger signals to achieve timestamp synchronization, ensuring that the data frame alignment accuracy is less than [value missing]. .

[0028] The sensor fusion algorithm employs an extended Kalman filter architecture. First, a state vector is established. ,in A quaternion representing the current attitude of the probe. This is the gyroscope's zero-bias vector, used to compensate for the cumulative drift error of the high-precision inertial measurement unit. This is a transpose operation. The prediction phase uses state equations. Update posture, among which This represents quaternion multiplication. This is the derivative of the quaternion attitude. In practice, the second-order Runge-Kutta method is used to discretize and solve the differential equation.

[0029] Simultaneously construct the observation equation ,in It is a rotation matrix derived from quaternions. For time Location data at that time It has a mean of zero and a covariance of Gaussian observation noise. Dynamic weighted prediction of the Kalman gain matrix compared to optical observations: When the optical sensor is obstructed, the weighting coefficient of the high-precision inertial measurement unit is automatically increased to 0.85 to ensure stable output of the fused attitude even under surgical environment interference. .

[0030] The system handles sensor anomalies through a dual-buffer mechanism: if optical data is lost for more than 5 consecutive frames, it switches to a pure high-precision inertial measurement unit prediction mode and triggers a warning; when the gyroscope has zero bias... The covariance exceeds the threshold At that time, the zero-bias calibration procedure is initiated, and the probe is used in a static state. The zero-bias mean is recalculated after data sampling. The fused attitude information is stored in quaternion form and converted to Euler angles for use by subsequent modules, with the conversion error controlled within a specified range. Within the range.

[0031] To improve clinical applicability, the system incorporates a temperature compensation module within the probe handle. When the high-precision inertial measurement unit's temperature sensor detects a temperature change exceeding [a certain threshold], [the module compensates for the temperature change]. At that time, the gyroscope zero-bias model is automatically corrected:

[0032] in To achieve zero bias in the gyroscope after temperature compensation. Zero bias at the reference temperature, Temperature coefficient (calibrated value) ), The current temperature. Reference temperature .

[0033] Based on the preset target attitude model, the attitude deviation is calculated by processing the fused attitude information, including coordinate system transformation and deviation quantization. The coordinate system transformation maps the probe coordinate system to the global coordinate system.

[0034] In this embodiment, a global coordinate system based on the patient's anatomical structure is first established. Specifically, before the ultrasound examination begins, three reference points are marked on the patient's body surface (in this embodiment, the xiphoid process, the lower edge of the left costal arch, and the midpoint of the right clavicle are selected). The three-dimensional coordinates of these marked points are acquired using an optical sensor to construct a right-handed Cartesian coordinate system: with the xiphoid process as the origin O, the direction pointing to the midpoint of the right clavicle is the positive X-axis (transverse), the direction pointing to the lower edge of the left costal arch is the positive Y-axis (longitudinal), and the Z-axis is perpendicular to the body surface plane and points outward (normal). The probe coordinate system is fixed at the geometric center of the probe, with its Z-axis along the direction of the probe's sound beam emission, and its X / Y axes parallel to the probe's minor and major axes, respectively.

[0035] Coordinate system transformation is achieved through a homogeneous transformation matrix. Let the coordinates of a point in the probe coordinate system be... Its coordinates in the global coordinate system Calculated by the following formula:

[0036] in , , They are respectively around Rotation matrix of the axis (using ZYX Euler angle order). This is the translation vector of the probe center in the global coordinate system. Rotation angle. It is obtained directly from the quaternion transformation in the fused attitude information. The system pre-stores standard target attitude models for different examination sites (such as the pitch angle of the target in a liver scan). Yaw angle Roll angle This model is automatically generated based on the patient's position data.

[0037] The attitude deviation quantification process consists of three steps. First, the difference vector between the real-time Euler angles and the target value is calculated: ,in , , These are pitch, yaw, and roll angle deviations, respectively. To accommodate the elastic characteristics of different structures, a dynamic weighting function is used to handle the deviation values. A weighting matrix is ​​defined. ,in , , These are the weighting coefficients for pitch, yaw, and roll angle deviations, respectively. The weighting coefficients for each axis are dynamically generated by an exponential function.

[0038] in , The gain coefficients are related to organizational characteristics, and the final output is the weighted bias norm. As a control input. The weighted deviation norm of the final output. As a control input, the gain coefficient. , The adaptive adjustment is achieved through real-time analysis of ultrasound images.

[0039] System each Extract the central region of interest (ROI) from the current ultrasound image and calculate its gray-level co-occurrence matrix features. Texture complexity factor. Determined by the GLCM entropy value:

[0040] in These are the element values ​​of the gray-level co-occurrence matrix. The grayscale level is denoted by . A larger value indicates a more complex tissue texture.

[0041] Calibration constant is taken , , When a vascular structure is detected, the system activates a dedicated boundary enhancement algorithm: first, a Frangi filter is used to extract tubular features; if a diameter greater than [a certain value] is detected... For blood vessels, elevation occurs at the normal angle of the vessel's course (e.g., the yaw axis corresponding to the portal vein). Value 50%. For example, the portal vein area. At that time, the foundation After vascular examination The value was increased to 3.1, which improved the sensitivity of the axial control.

[0042] To ensure clinical safety, the system employs multiple protection mechanisms. When the weighted bias norm... If a single-axis deviation continues to exceed the limit, the motor drive will automatically pause and an audible and visual alarm will be issued. Simultaneously, weight parameters can be manually adjusted via the user interface: "Fine Mode" can be enabled when exploring deep structures (settings...). When exploring superficial organs, switch to "fast mode" ( All dynamic parameter adjustments are recorded in the system log and used to generate operation optimization reports.

[0043] A control signal is generated, which drives the adjustment mechanism through a proportional-integral-derivative (PID) control algorithm. The adjustment mechanism includes a stepper motor and a linkage mechanism. The probe's attitude is automatically adjusted through the adjustment mechanism to reduce attitude deviation.

[0044] In this embodiment, the control system employs a dual-closed-loop architecture to achieve precise motion control. The outer loop is the attitude control loop, which adjusts the weighted deviation norm with a period of 10ms. Decomposed into three-dimensional translation components With rotational components The translation component is achieved through a three-axis precision stepper motor drive system: The shaft uses a 57HS22 type two-phase hybrid stepper motor, paired with... The C7-grade ball screw with a lead has a linear displacement resolution of up to [missing information]. The Z-axis uses an integrated linear motor module. The drive circuit uses a DM542 microstepping driver, set to a microstepping mode of 12800 pulses / revolution, controlling the coordinated movement of each axis through pulse / direction signals. The rotational component is executed by a parallel linkage mechanism, which consists of a kinematic chain of three servo motors, a harmonic reducer, and a swing arm. Each motor output shaft is equipped with a 17-bit absolute encoder to realize the pitch angle (…). ), yaw angle ( ), roll angle ( Independent adjustment of tilt angle, with a range of [missing information]. Repeatability .

[0045] The proportional-integral-derivative (PID) control algorithm designs independent parameters for different degrees of freedom. Translation control uses a PID controller.

[0046] in To account for translation tracking error, typical parameters are tuned as follows: , , Rotational control incorporates feedforward compensation:

[0047] in The moment of inertia of the probe. Angular acceleration, To address rotational tracking error, the parameters are configured as follows: , , The control signals are output as PWM waves and direction signals via the timer of the STM32H743 main control chip. The stepper motor driver receives a maximum pulse frequency of 200kHz, while the servo motor transmits position commands via the CANopen bus.

[0048] Inner loop displacement control For periodic operation, an anti-saturation mechanism is constructed based on high-precision encoder feedback. An integral term limiting condition is defined:

[0049] Where the time-varying decay factor When tracking error Exceed Automatically activated. The mechanical protection strategy includes a dual response: when the linkage mechanism triggers the travel limit sensor, it immediately activates... Value dynamic reduction Simultaneously inject reverse braking current (of the rated current) To achieve an emergency stop; if the limit is continuously exceeded... The system automatically switches to torque limiting mode, limiting the motor output torque to [specific value]. Within the safety threshold. The inner loop synchronously implements speed feedforward compensation, and uses a Kalman filter to predict the motor speed, reducing trajectory tracking lag error.

[0050] The adjustment mechanism incorporates multiple safety protections. At the hardware level, it is installed at the end of the leadscrew. Mechanical stop, motor driver equipped with 1.5 times overcurrent protection; software-level real-time monitoring of winding temperature, when the temperature exceeds... Automatic drooping operation. Three levels of safety boundaries are set during clinical operation: soft limit range (translation). / rotation (Compared to mechanical limit reduction) When the probe contacts the pressure sensor and detects a pressure greater than [a certain value], An emergency stop is triggered under pressure. During system initialization, a self-test process is executed: driving each axis... The speed limit switch establishes a zero point, followed by a sinusoidal frequency sweep test (frequency). Verify the resonance characteristics of the mechanism to ensure that there is no risk of oscillation during the adjustment process.

[0051] The parameter optimization module supports adaptive tuning. When the system detects a change in the load on the linkage mechanism (through a motor current fluctuation greater than 100%), it will automatically adjust the tuning. When making a judgment, the system automatically initiates the recursive least squares method to identify model parameters and updates the PID gain coefficients. The user interface provides a "coarse-fine" mode switch: in fine-tuning scenarios such as vascular scanning, the system automatically reduces the integral gain by 40% and increases the proportional gain by 15%, while simultaneously increasing the stepper motor's microsteps to 25,600 pulses / revolution. After each adjustment, a motion analysis report is generated, recording the maximum acceleration, arrival time, and energy consumption data, providing an optimization basis for probe path planning.

[0052] The process is based on ultrasound image feedback optimization, which includes real-time analysis of image quality indicators, detection of edge contrast using an image sharpness assessment module, and iterative adjustment of the target posture model based on the assessment results to achieve adaptive posture calibration.

[0053] In this embodiment, the image feedback optimization mechanism captures the B-mode image stream output by the ultrasound device in real time at a frequency of 10 frames per second. The image quality assessment module first locates the region of interest, which is set by default to a 100×100 pixel rectangular window at the center of the image. The Sobel edge detection operator is then used to calculate the gradient magnitude of all pixels within the region of interest. Specifically, the system calculates... and gradient components of direction , The gradient magnitude is defined as Final sharpness benchmark Take the arithmetic mean of the gradient magnitudes of all edge pixels (pixels with a gradient magnitude greater than a preset threshold of 15). Below the dynamic threshold (In this embodiment, it is 60, corresponding to a medium organizational texture complexity scenario.) When the target pose model iterative optimization process is automatically triggered, the current control signal output is frozen to prevent mechanical oscillations during the optimization process.

[0054] The iterative adjustment process employs an adaptive calibration algorithm based on gradient descent. The system constructs image sharpness... Euler angle attitude parameters of the probe The response surface model probes local gradients by applying small perturbations to three degrees of freedom. Each iteration executes the following steps: first, save the current pose. and corresponding clarity ; then in Apply on each of the three axes The disturbance (i.e.) Acquire disturbed ultrasound images and calculate sharpness. and Attitude gradient Approximate calculation using the central difference method:

[0055] in For image clarity Regarding attitude parameters gradient Euler angles, For small perturbation vectors, Corresponding to , , , , , These are pitch angle, yaw angle, and roll angle, respectively. This is for the transpose operation.

[0056] The target pose update formula is: ,in For the updated target pose, For the current target posture, For image clarity exist gradient at point, learning rate The initial value is set to 0.3. When the gradient norm... (convergence threshold) Optimization will stop when the maximum number of iterations (6) is reached. To improve clinical safety, the system enforces a limit on the magnitude of a single pose adjustment: the absolute value of any Euler angle change must not exceed [a certain value]. And weighted deviation norm The increase will not exceed 20%. After optimization, the updated target pose... Load the deviation calculation module and drive the adjustment mechanism to perform fine-tuning operations.

[0057] Example 2: Figure 2 As shown, the present invention provides a medical ultrasound probe posture adjustment system, comprising: Attitude acquisition module 1 uses an attitude sensor to acquire real-time attitude data of the probe. The attitude sensor includes an inertial measurement unit and an optical sensor. The angular velocity data of the inertial measurement unit and the position data of the optical sensor are combined through a sensor fusion algorithm to generate fused attitude information. Deviation calculation module 2, based on the preset target attitude model, processes the fused attitude information to calculate attitude deviation, including coordinate system transformation and deviation quantization, wherein the coordinate system transformation maps the probe coordinate system to the global coordinate system; Signal generation module 3 generates control signals, which drive the adjustment mechanism through a proportional-integral-derivative control algorithm. The adjustment mechanism includes a stepper motor and a linkage mechanism. The attitude adjustment module 4 automatically adjusts the attitude of the probe through the adjustment mechanism to reduce attitude deviation; Feedback optimization module 5 is based on the ultrasound image feedback optimization and adjustment process. The optimization includes real-time analysis of image quality indicators, detection of edge contrast using the image sharpness evaluation module, and iterative adjustment of the target posture model based on the evaluation results to achieve adaptive posture calibration.

[0058] 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.

Claims

1. A method for adjusting the posture of a medical ultrasound probe, characterized in that, The method includes: The real-time attitude data of the probe is obtained using an attitude sensor, which includes an inertial measurement unit and an optical sensor. The angular velocity data of the inertial measurement unit and the position data of the optical sensor are combined through a sensor fusion algorithm to generate fused attitude information. Based on a preset target posture model, the fused posture information is processed to calculate the posture deviation, including coordinate system transformation and deviation quantization, wherein the coordinate system transformation maps the probe coordinate system to the global coordinate system. A control signal is generated, which drives an adjustment mechanism through a proportional-integral-derivative (PI-DE) control algorithm. The adjustment mechanism includes a stepper motor and a linkage mechanism. The adjustment mechanism automatically adjusts the probe's posture, reducing posture deviation. The process is based on ultrasound image feedback optimization, which includes real-time analysis of image quality indicators, detection of edge contrast using an image sharpness assessment module, and iterative adjustment of the target posture model based on the assessment results to achieve adaptive posture calibration.

2. The method for adjusting the posture of a medical ultrasound probe as described in claim 1, characterized in that, The sensor fusion algorithm uses an extended Kalman filter to convert the time output of the inertial measurement unit. angular velocity data Time with optical sensor output Location data To merge; Establish state vector ,in For quaternion pose, To achieve zero bias in the gyroscope, This is a transpose operation; Through state prediction equations Update the integral of angular velocity, where This is quaternion multiplication. For time angular velocity data at that time The derivative of the quaternion attitude; Constructing observation equations using optical sensor position data ,in, For time The observed value at that time, For rotation matrix, For time Location data at that time To observe noise; By combining the Kalman gain-weighted angular velocity integral with the optical position data, fused attitude information is output. .

3. The method for adjusting the posture of a medical ultrasound probe as described in claim 1, characterized in that, The coordinate system transformation specifically includes: Map the attitude data in the probe coordinate system to a global coordinate system based on the patient's body surface; Deviation quantification is achieved by calculating the Euler angle difference between the probe coordinate system and the global coordinate system; Based on the ideal Euler angle range in the preset target attitude model, determine the direction and magnitude of the pitch, yaw, and roll angle deviations in the real-time attitude.

4. The method for adjusting the posture of a medical ultrasound probe as described in claim 3, characterized in that, The deviation quantization introduces a dynamic weighting function to handle Euler angle differences: Define attitude deviation vector ,in , , These are pitch, yaw, and roll angle deviations, respectively. This is a transpose operation; Using weight matrix Weighted bias, where , , These are the weighting coefficients for pitch, yaw, and roll angle deviations, respectively. The weighting values ​​increase non-linearly with the deviation amplitude. in , The gain coefficients are related to organizational characteristics, and the final output is the weighted bias norm. As a control input.

5. The method for adjusting the posture of a medical ultrasound probe as described in claim 4, characterized in that, The gain coefficients of the weight matrix are adaptively adjusted based on the ultrasound image features: Establish image texture complexity Mapping function with gain coefficient: in Calculated from the entropy value of the gray-level co-occurrence matrix of the image region. , , These are calibration constants; When the blood vessel boundary is detected The value enhances the sensitivity of the angle fine-tuning.

6. The method for adjusting the posture of a medical ultrasound probe as described in claim 1, characterized in that, When the proportional-integral-derivative control algorithm drives the adjustment mechanism: The attitude deviation is decomposed into three-dimensional translation and rotation components; The translation component controls the stepper motor to drive the probe to move linearly, while the rotation component controls the linkage mechanism to change the probe tilt angle. A dual closed-loop control structure is adopted, with the inner loop correcting displacement error in real time based on feedback from the motor encoder.

7. The method for adjusting the posture of a medical ultrasound probe as described in claim 6, characterized in that, The inner loop of the dual closed-loop control is designed to resist saturation integrators. Define the amplitude limit condition for the integral term: in For the output of the anti-saturation integrator, The maximum value of the integral term. For displacement tracking error, It is a time-varying decay factor; When the linkage mechanism reaches the mechanical limit... The value is dynamically reduced by 50% to suppress the accumulation of integrals.

8. The method for adjusting the posture of a medical ultrasound probe as described in claim 1, characterized in that, The real-time image quality indicators include: Extracting organ boundary contours from ultrasound images using edge detection operators; The average gradient magnitude of the boundary pixels is calculated as the sharpness benchmark. When the baseline value is lower than the preset threshold, the target posture model iterative optimization process is triggered.

9. The method for adjusting the posture of a medical ultrasound probe as described in claim 8, characterized in that, The iterative adjustment of the target attitude model employs an adaptive calibration based on gradient descent: Building image sharpness With attitude parameters Response surface model: in For image clarity Regarding attitude parameters gradient, Euler angles, , , These are pitch angle, yaw angle, and roll angle, respectively. This is a transpose operation; by Update target attitude. The learning rate, until Stop iteration, where For the updated target pose, For the current target posture, For image clarity exist gradient at, For gradient norm, This is the convergence threshold.

10. A medical ultrasound probe posture adjustment system, characterized in that, The system includes: The attitude acquisition module uses an attitude sensor to acquire real-time attitude data of the probe. The attitude sensor includes an inertial measurement unit and an optical sensor. The angular velocity data of the inertial measurement unit and the position data of the optical sensor are combined through a sensor fusion algorithm to generate fused attitude information. The deviation calculation module, based on a preset target attitude model, processes the fused attitude information to calculate the attitude deviation, including coordinate system transformation and deviation quantization, wherein the coordinate system transformation maps the probe coordinate system to the global coordinate system. A signal generation module generates a control signal, which drives an adjustment mechanism through a proportional-integral-derivative (PI-DE) control algorithm. The adjustment mechanism includes a stepper motor and a linkage mechanism. The attitude adjustment module automatically adjusts the attitude of the probe through the adjustment mechanism to reduce attitude deviation; The feedback optimization module is based on the ultrasound image feedback optimization and adjustment process. The optimization includes real-time analysis of image quality indicators, detection of edge contrast using the image sharpness assessment module, and iterative adjustment of the target posture model based on the assessment results to achieve adaptive posture calibration.

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