4D ICE ultrasound imaging transmit control method based on latency data multiplexing

Abstract

This invention relates to the field of imaging launch control technology, specifically including a 4DICE ultrasound imaging launch control method based on time-delay data multiplexing. The method includes: establishing a fractal prediction model and N predicted motion trajectory branches; constructing a directional gain tensor, establishing a gain distribution in the direction of interest, performing low-rank sparse decomposition, establishing low-rank sparse decoupling results, mapping them to array element activation modes, and performing ultrasound imaging launch control management. This invention addresses the technical problem of 4DICE ultrasound imaging launch control using a fixed time-delay scheme, which cannot accurately capture the multi-scale characteristics and dynamic trends of catheter motion, and suffers from motion artifact superposition during the imaging process. It achieves accurate mapping of gain and trajectory through low-rank sparse decomposition, enhances signal gain in key areas, and simultaneously decouples the basic structure from local focusing features, improving the imaging signal-to-noise ratio and the clarity of local details. This provides efficient and accurate launch control management for 4DICE ultrasound imaging.

Description

Technical Field

[0001] This invention relates to the field of imaging emission control technology, specifically to a 4DICE ultrasonic imaging emission control method based on time-delay data multiplexing. Background Technology

[0002] 4DICE (Intracardiac Echocardiography) ultrasound imaging technology is a core imaging tool in minimally invasive interventional cardiology. Its imaging quality directly determines the accuracy of clinical diagnosis. For 4DICE ultrasound imaging, on the one hand, it is necessary to ensure high spatiotemporal resolution of the images to clearly present small lesions and dynamic anatomical relationships. On the other hand, it is necessary to optimize the transmission control efficiency to reduce equipment energy consumption, avoid signal attenuation, and ensure imaging stability.

[0003] However, conventional launch control often uses fixed time delay schemes and simple linear prediction models, which cannot accurately capture the multi-scale characteristics and dynamic changes of catheter motion, and are prone to motion artifacts. In addition, the use of a uniform array element activation mode for the entire area results in blurred display of key lesion areas, affecting the accuracy of lesion assessment.

[0004] In summary, existing technologies suffer from the problem of using a fixed time delay scheme for 4DICE ultrasound imaging transmission control, which fails to accurately capture the multi-scale characteristics and dynamic changes of catheter motion, and results in motion artifact superposition during the imaging process. Summary of the Invention

[0005] This application provides a 4DICE ultrasound imaging transmission control method based on time-delay data reuse, aiming to solve the technical problem that the existing 4DICE ultrasound imaging transmission control uses a fixed time-delay scheme, which cannot accurately capture the multi-scale characteristics and dynamic changes of catheter motion, and there is motion artifact superposition during the imaging process.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows: This application provides a 4DICE ultrasound imaging transmission control method based on time-delay data multiplexing. The method includes: reading the position dataset of a catheter position sensor; establishing a fractal prediction model of catheter motion based on the position dataset and phase difference data of adjacent frame echoes; establishing N predicted motion trajectory branches using the fractal prediction model, wherein each predicted motion trajectory branch is configured with an initial time-delay multiplexing scheme; performing frame echo coherence analysis within a preset frame interval at the receiving end to construct a direction gain tensor containing time and spatial dimensions; performing region-of-interest weighting processing using the direction gain tensor and the target region of interest to establish a direction-of-interest gain distribution; mapping the direction-of-interest gain distribution to the N predicted motion trajectory branches, performing low-rank sparse decomposition to establish a low-rank sparse decoupling result; mapping the low-rank sparse decoupling result to the element activation mode of a MOS transistor switching matrix; configuring a dynamic switching adjustment strategy based on the mapping result; and then performing ultrasound imaging transmission control management.

[0007] Preferably, the following steps are performed: Phase consistency calculation of the echo signal for each frame echo is performed to establish a signal directional stability index; Echo signal amplitude change rate and local peak value analysis are performed for each frame echo to establish signal-to-noise ratio change rate characteristics and echo amplitude gradient characteristics; Multidimensional coherence feature vectors for each array element and each direction in each frame echo are established based on the signal directional stability index, signal-to-noise ratio change rate characteristics, and echo amplitude gradient characteristics; Initial directional gain tensors are established using the multidimensional coherence feature vectors based on the time dimension, array element spatial dimension, and transmission direction dimension; Temporal smoothing is performed on each initial directional gain tensor according to adjacent frames to establish a first update result; Continuous frame gain identification of the first update result is performed to establish a second update result, where the continuous frame gain identification is temporal continuous gain compensation, and the second update result is output as a directional gain tensor.

[0008] Preferably, each directional element of the directional gain tensor is assigned a weighting coefficient according to the spatial distribution of the target region of interest. The weighting coefficients are adaptively configured based on the clinical priority, local tissue structure characteristics, and signal reliability indicators of the target region of interest. The weighting coefficients are used to perform weighted calculation of the directional gain tensor to establish the direction of interest gain distribution.

[0009] Preferably, after mapping the interest direction gain distribution and the N predicted motion trajectory branches to the same coordinate system, local interest direction gain data of the N predicted trajectory branches are established according to the corresponding mapping relationship between position and direction; a multi-branch tensor is constructed based on the N predicted trajectory branches with local interest direction gain data, and low-rank sparse decomposition is performed, including: extracting the gain components that satisfy the preset correlation direction as a low-rank basic structure, establishing sparse substructures from the gain features of the local focus region, and making the sparse substructures explicit; and establishing a low-rank sparse decoupling result based on the low-rank basic structure and the explicit sparse substructures.

[0010] Preferably, an index mapping relationship is established between each tensor element in the low-rank sparse decoupling result and the corresponding MOS transistor switch; the multi-level energy mode of the array element is configured according to the index mapping relationship, and the closing and opening selection of the MOS transistor switch is determined by the gain threshold; the adaptation analysis of the dynamic switching adjustment strategy is performed using the selection determination result and the multi-level energy mode, the array element emission parameters controlled by the MOS transistor switch are established, and the emission control management is performed.

[0011] Preferably, the timing activation state of the array elements is configured according to the selection and discrimination results, and the multi-level energy mode is used as the response energy mapping of the array elements; the timing activation state and response energy mapping are used to perform smooth transition processing of array element energy; and the adaptation analysis of dynamic switching adjustment strategy is performed according to the smooth transition processing results.

[0012] Preferably, the emission state energy curve in the smooth transition processing result is converted into a multi-level switching adjustment signal target sequence; the multi-level switching adjustment signal target sequence is used as the control target, the multi-level conduction mode of the MOS transistor switch is configured, and the duty cycle, pulse envelope shape and driving voltage amplitude of the MOS transistor switch are set under the constraints of driving delay and switching loss, so as to complete the adaptation analysis of the dynamic switching adjustment strategy.

[0013] Preferably, the displacement sequence of key points of the catheter in the location dataset is obtained, and a multi-scale temporal feature vector of the catheter motion is established based on the displacement sequence of key points of the catheter and the phase difference data; the fractal dimension of the multi-scale temporal feature vector is estimated, and a fractal prediction model is constructed. The fractal prediction model is used to characterize the self-similarity and irregularity of the catheter motion at different scales; the fractal prediction model is used to perform prediction at different scales to establish N predicted motion trajectory branches.

[0014] Preferably, a self-test of the MOSFET switch is performed to establish a switch state dataset; based on the dynamic switch adjustment strategy and the switch state dataset, MOSFET switch anomaly identification is performed to establish an abnormal switch identifier; and equivalent MOSFET switch switching control is performed according to the abnormal switch identifier.

[0015] Preferably, the actual control parameters of the array elements are recorded, control feedback is established, and the control feedback is used for iterative update management of MOS transistor switching control.

[0016] In summary, one or more technical solutions provided in this application achieve the following: capturing the irregular features of duct motion, constructing a directional gain tensor and combining it with the target region of interest to form a gain distribution of the direction of interest, achieving accurate mapping between gain and trajectory through low-rank sparse decomposition, enhancing signal gain in key regions, decoupling the basic structure from local focusing features, improving the imaging signal-to-noise ratio and the clarity of local details, and providing efficient and accurate emission control management for 4DICE ultrasound imaging. Attached Figure Description

[0017] Figure 1 This application provides a flowchart illustrating the 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing. Detailed Implementation

[0018] The embodiments are described in detail below with reference to the accompanying drawings, such as... Figure 1 As shown, this application provides a 4DICE ultrasound imaging transmission control method based on time-delay data multiplexing, wherein the method includes: S1: Read the position dataset of the catheter position sensor, establish a fractal prediction model of catheter motion based on the position dataset and the phase difference data of adjacent frame echoes, and establish N predicted motion trajectory branches using the fractal prediction model, wherein each predicted motion trajectory branch is configured with an initial time delay multiplexing scheme.

[0019] Specifically, the catheter position sensor is used to monitor the catheter's position changes within the body in real time, and its output is a position dataset, including the spatial coordinates of the catheter at different time points; the phase difference data of adjacent frame echoes refers to the phase difference between two adjacent echo signals during ultrasound imaging. By determining this phase difference, relevant information about the catheter's motion can be obtained, including its direction and velocity; the fractal prediction model is a prediction model built based on fractal theory. Fractal theory emphasizes the self-similarity of things at different scales and can capture the complex and irregular characteristics of catheter motion, including its variation patterns at different time and spatial scales; the predicted motion trajectory branches refer to the possible motion paths of the catheter obtained according to the fractal prediction model. N branches mean that multiple possible motion scenarios are considered to cope with the uncertainty of catheter motion; the initial time delay multiplexing scheme is a time delay allocation scheme pre-set for each predicted motion trajectory branch. Time delay multiplexing refers to processing the signal at different time points to improve signal transmission efficiency and imaging quality.

[0020] Execution steps: A position dataset is acquired from the catheter position sensor, and combined with the phase difference data of adjacent frame echoes, providing a foundation for establishing a fractal prediction model of catheter motion. Through the fractal prediction model, N predicted motion trajectory branches are generated, each corresponding to a possible catheter motion scenario. An initial time delay multiplexing scheme is configured for each branch, allowing for pre-planning of signal processing methods based on different predicted motion scenarios during subsequent imaging, thereby improving imaging accuracy and efficiency. These steps accurately capture the irregular features of catheter motion, reduce motion artifacts, and significantly improve imaging clarity and accuracy.

[0021] S2: Perform frame echo coherence analysis within a preset frame interval at the receiving end, construct a directional gain tensor containing time and spatial dimensions, and perform interest region weighting processing using the directional gain tensor and the target interest region to establish an interest direction gain distribution; S3: After mapping the interest direction gain distribution to N predicted motion trajectory branches, perform low-rank sparse decomposition to establish a low-rank sparse decoupling result.

[0022] Specifically, in an ultrasound imaging system, the receiver refers to the part responsible for receiving ultrasound signals reflected back from human tissue, typically composed of multiple receiver array elements. Frame echo coherence analysis involves analyzing frame echo signals within a preset frame interval to evaluate the signal coherence between different frames, extracting directional stability, signal-to-noise ratio (SNR) change rate, and echo amplitude gradient. Signal coherence between different frames refers to the phase and amplitude consistency of the signal. The directional gain tensor is a multidimensional array containing time and spatial dimensions, used to describe the signal gain in different directions, integrating directional stability, SNR change rate, and echo amplitude gradient. Region of interest (ROI) weighted processing involves weighting the ROI tensor, assigning different weights to gains in different directions based on the clinical priority, local tissue structure characteristics, and signal reliability indicators of the target ROI. Commonly, the target ROI is the lesion region. Low-rank sparse decomposition involves decomposing a matrix or tensor into a low-rank part and a sparse part. The low-rank part represents the basic structure of the signal, while the sparse part represents local focusing features.

[0023] Execution steps: At the receiving end, coherence analysis is performed on the frame echo signal within a preset frame interval to extract the signal's directional stability, signal-to-noise ratio (SNR) change rate, and echo amplitude gradient, constructing a directional gain tensor. Based on the characteristics of the target region of interest, the directional gain tensor is weighted to form a gain distribution in the direction of interest, enhancing the signal gain in key regions while suppressing signal interference in non-key regions, thus improving the imaging SNR and local detail clarity. After mapping the gain distribution in the direction of interest to N predicted motion trajectory branches, low-rank sparse decomposition is performed. Through low-rank sparse decomposition, the gain distribution is decomposed into a basic gain structure and local focusing features, further improving the imaging SNR and local detail clarity, significantly improving the imaging effect of the lesion area.

[0024] S4: Map the low-rank sparse decoupling results to the element activation modes of the MOS transistor switching matrix, configure the dynamic switching adjustment strategy according to the mapping results, and then execute the transmission control management of ultrasound imaging.

[0025] Specifically, the low-rank sparse decoupling result refers to the result obtained through low-rank sparse decomposition, which includes a low-rank basic structure and a sparse substructure. The low-rank basic structure represents the basic characteristics of the signal, while the sparse substructure represents the local focusing characteristics. The MOS transistor switching matrix is ​​a matrix composed of multiple MOS transistors used to control the activation state of array elements in the ultrasound imaging system. The switching state of each MOS transistor determines whether the corresponding array element participates in signal transmission. The array element activation mode describes the switching state of each MOS transistor in the MOS transistor switching matrix, determining which array elements are activated for signal transmission. The dynamic switching adjustment strategy refers to dynamically adjusting the switching state of the MOS transistors based on the low-rank sparse decoupling result to optimize the array element activation mode and improve imaging efficiency and quality. Transmission control management refers to managing the transmission process of the ultrasound imaging system based on the dynamic switching adjustment strategy, including signal transmission time and energy distribution.

[0026] Execution steps: The low-rank sparse decoupling results are mapped to the element activation modes of the MOS transistor switching matrix. Specifically, the low-rank basic structure and sparse substructure are converted into MOS transistor index mapping relationships to configure the multi-level energy modes of the array elements. The closing and opening selection of the MOS transistor switches is determined by a gain threshold, realizing the adaptation analysis of the dynamic switching adjustment strategy. The activation state of the array elements is dynamically adjusted according to imaging requirements to optimize signal transmission parameters, reduce energy loss in non-critical areas, and improve signal strength in critical areas. This dynamic switching adjustment strategy reduces transmission energy while improving imaging accuracy, providing efficient and precise transmission control management for 4DICE ultrasound imaging.

[0027] Furthermore, at the receiving end, frame echo coherence analysis is performed within a preset frame interval to construct a directional gain tensor containing both temporal and spatial dimensions. The method of this application includes: The process involves: calculating the phase consistency of the echo signal for each frame echo to establish a signal directional stability index; analyzing the rate of change of echo signal amplitude and local peak values ​​for each frame echo to establish signal-to-noise ratio (SNR) rate of change characteristics and echo amplitude gradient characteristics; establishing multidimensional coherence feature vectors for each array element and each direction in each frame echo based on the signal directional stability index, SNR rate of change characteristics, and echo amplitude gradient characteristics; using these multidimensional coherence feature vectors to establish an initial directional gain tensor based on the time dimension, array element spatial dimension, and transmission direction dimension; performing time smoothing on each initial directional gain tensor according to adjacent frames to establish a first update result; performing continuous frame gain identification based on the first update result to establish a second update result, where the continuous frame gain identification is time-domain continuous gain compensation; and outputting the second update result as a directional gain tensor.

[0028] Specifically, echo signal phase consistency calculation assesses the phase similarity of echo signals from adjacent frames to determine the signal's directional stability; high phase consistency indicates greater signal stability in that direction. Signal directional stability indexes, obtained through phase consistency calculations, quantify signal stability in a specific direction. Echo signal amplitude change rate measures the rate of change of echo signal amplitude over time, evaluating the signal's dynamic characteristics. Local peak analysis analyzes local peaks in the echo signal to identify key feature points. Signal-to-noise ratio (SNR) change rate characteristics, calculated from the echo signal amplitude change rate, quantify how the SNR changes over time. Echo amplitude gradient features refer to features obtained through local peak analysis, used to describe the spatial rate of change of signal amplitude; multidimensional coherence feature vector refers to a vector that combines signal directional stability index, signal-to-noise ratio change rate features, and echo amplitude gradient features to describe the multidimensional characteristics of the signal; initial directional gain tensor refers to a multidimensional array constructed based on the time dimension, array element spatial dimension, and transmission direction dimension, used to describe the gain of the signal in different directions; temporal smoothing refers to smoothing the initial directional gain tensor in the time dimension to reduce noise and instability; continuous frame gain identification refers to identifying gain changes between consecutive frames and performing temporal continuity gain compensation to optimize the gain result.

[0029] Execution steps: Phase consistency calculation is performed on each frame of echo signal to establish a signal directional stability index; amplitude change rate and local peak analysis are performed on each frame of echo signal to establish signal-to-noise ratio (SNR) change rate characteristics and echo amplitude gradient characteristics. These characteristics are then combined to form a multidimensional coherence feature vector for each element and each direction in each frame of echo signal. Based on this multidimensional coherence feature vector, an initial directional gain tensor is constructed, describing the signal gain in different directions. The gain results are further optimized; specifically, the initial directional gain tensor is time-smoothed to reduce noise and instability, yielding a first update result. Continuous frame gain identification and temporal continuous gain compensation are performed on the first update result to obtain a second update result. The second update result is then output as the directional gain tensor. Through multidimensional signal analysis and optimization, the directional stability and gain accuracy of the signal are improved, significantly enhancing the imaging effect of the lesion area.

[0030] Furthermore, by utilizing the aforementioned directional gain tensor and the target region of interest to perform region-of-interest weighting processing, an interest direction gain distribution is established. The method of this application includes: Each directional element of the directional gain tensor is assigned a weighting coefficient according to the spatial distribution of the target region of interest. The weighting coefficients are adaptively configured based on the clinical priority, local tissue structure characteristics, and signal reliability indicators of the target region of interest. The weighted calculation of the directional gain tensor is performed using the weighting coefficients to establish the gain distribution of the direction of interest.

[0031] Specifically, the directional gain tensor is a multidimensional array containing time, space, and directional dimensions, used to describe the signal gain in different directions. In medical imaging, the target region of interest (ROI) refers to an important region under diagnostic markings, such as lesion areas or specific anatomical structures. Weighting coefficients are used to adjust the weight of each directional element in the directional gain tensor to reflect the clinical importance of the ROI. Clinical priority refers to ranking the importance of different regions according to clinical diagnostic needs, with higher-priority regions requiring more precise imaging. Local tissue structure features refer to the anatomical features of the ROI, including tissue density and boundary clarity, which affect signal propagation and reception. Signal reliability indicators are metrics for evaluating signal quality, including signal-to-noise ratio (SNR) and signal strength, used to determine signal reliability. The directional gain distribution refers to the weighted directional gain distribution, which focuses more on the signal gain of the ROI.

[0032] Execution steps: Each directional element of the directional gain tensor is assigned a weighting coefficient according to the spatial distribution of the target region of interest. The weighting coefficients are adaptively configured based on the clinical priority, local tissue structure characteristics, and signal reliability indicators of the target region of interest. This means that regions with high clinical priority, complex tissue structure, and reliable signals will be assigned higher weighting coefficients to ensure that the signals in these regions are enhanced in imaging. These weighting coefficients are used to perform weighted calculations on the directional gain tensor to establish the gain distribution of the direction of interest, highlighting the signal gain of the target region of interest while suppressing signal interference in non-critical regions, thereby improving the signal-to-noise ratio and local detail clarity of the imaging, and displaying the details of the lesion area more clearly.

[0033] Furthermore, after mapping the interest direction gain distribution to N predicted motion trajectory branches, low-rank sparse decomposition is performed to establish a low-rank sparse decoupling result. The method of this application includes: After mapping the interest direction gain distribution and N predicted motion trajectory branches to the same coordinate system, local interest direction gain data of the N predicted trajectory branches are established according to the corresponding mapping relationship between position and direction. A multi-branch tensor is constructed based on the N predicted trajectory branches with local interest direction gain data, and low-rank sparse decomposition is performed, including: extracting the gain components that satisfy the preset correlation direction as a low-rank basic structure, establishing sparse substructures from the gain features of the local focus region, and making the sparse substructures explicit; and establishing a low-rank sparse decoupling result based on the low-rank basic structure and the explicit sparse substructures.

[0034] Specifically, the interest direction gain distribution refers to the weighted directional gain distribution, which focuses more on the signal gain of the target interest region; the predicted motion trajectory branches refer to the possible motion paths of the duct obtained according to the fractal prediction model, with N branches indicating that multiple possible motion scenarios are considered; the same coordinate system, i.e., a unified reference frame, is used to spatially align and map the interest direction gain distribution and the predicted motion trajectory branches; local interest direction gain data refers to the data obtained after mapping the interest direction gain distribution onto each predicted motion trajectory branch, reflecting the gain of each branch at different positions and directions; the multi-branch tensor contains a multidimensional array of multiple branches, used to describe the local interest direction gain data of each branch; low-rank sparse decomposition refers to decomposing a matrix or tensor into a low-rank part and a sparse part. The low-rank part represents the basic structure of the signal, and the sparse part represents local focusing features; the low-rank basic structure represents the basic features of the signal, usually corresponding to the global or background part of the signal; the sparse substructure represents local focusing features, usually corresponding to the local or specific regions of the signal; explicit processing refers to highlighting the important features in the sparse substructure so that they are easier to identify and utilize in subsequent analysis.

[0035] Execution steps: Map the gain distribution of the direction of interest and N predicted motion trajectory branches to the same coordinate system. Through the corresponding mapping relationship between position and direction, establish local gain data of the N predicted trajectory branches in the direction of interest, thereby combining the signal gain with the possible motion path of the catheter and providing a more accurate data foundation for subsequent signal processing. Construct a multi-branch tensor based on the N predicted trajectory branches with local gain data in the direction of interest, and perform low-rank sparse decomposition. Further, during the decomposition process, extract the gain components that meet the preset correlation direction as low-rank basic structures, establish sparse substructures for the gain features of the local focusing region, and perform explicit processing of the sparse substructures, thereby separating the basic structure and local features of the signal for better analysis and utilization of these features. Establish low-rank sparse decoupling results based on the low-rank basic structure and the explicit sparse substructures. The low-rank sparse decoupling results are used to further optimize signal processing and imaging control, improve the signal-to-noise ratio and local detail clarity of imaging, and significantly improve the imaging effect of the lesion area.

[0036] Furthermore, the method of this application maps the low-rank sparse decoupling results to the element activation modes of the MOS transistor switching matrix, configures a dynamic switching adjustment strategy based on the mapping results, and then performs emission control management for ultrasound imaging. Establish an index mapping relationship between each tensor element in the low-rank sparse decoupling result and the corresponding MOS transistor switch; configure the multi-level energy mode of the array element according to the index mapping relationship, and select the closing and opening of the MOS transistor switch by using the gain threshold; perform adaptation analysis of dynamic switch adjustment strategy using the selection discrimination result and the multi-level energy mode, establish the array element emission parameters controlled by the MOS transistor switch, and perform emission control management.

[0037] Specifically, the low-rank sparse decoupling result refers to the result obtained through low-rank sparse decomposition, which includes the low-rank basic structure and sparse substructure, used to describe the basic characteristics and local focusing features of the signal; tensor elements refer to each data point in the low-rank sparse decoupling result, corresponding to a specific signal feature; index mapping relationship refers to the correspondence between each tensor element in the low-rank sparse decoupling result and the MOS transistor switch, used to control the switching state of the MOS transistor; array element refers to the transmitting and receiving units in the ultrasound imaging system, used to transmit and receive ultrasound signals; multi-level energy mode refers to different energy levels configured according to signal characteristics, used to optimize the transmission energy of the array element; gain threshold is the threshold used to determine the switching state of the MOS transistor, determining whether the MOS transistor is closed or open based on the signal gain; dynamic switch adjustment strategy refers to the strategy of dynamically adjusting the switching state of the MOS transistor according to signal characteristics and gain threshold to optimize the transmission parameters of the array element; transmission control management refers to managing the transmission process of the ultrasound imaging system according to the dynamic switch adjustment strategy, including signal transmission time and energy distribution.

[0038] Execution steps: First, establish an index mapping relationship between each tensor element in the low-rank sparse decoupling result and its corresponding MOS transistor switch, linking signal characteristics with specific hardware control to provide a foundation for subsequent dynamic adjustment. Second, configure multi-level energy modes for array elements based on the index mapping relationship, and select and discriminate the closing and opening of MOS transistor switches using gain thresholds. Third, dynamically adjust the switching state of MOS transistors based on signal strength and characteristics to optimize the emission energy of array elements. Specifically, if the signal gain in a certain direction is lower than the threshold, the corresponding MOS transistor is turned off to reduce energy loss; if the signal gain is higher than the threshold, the MOS transistor remains on to ensure signal strength. Fourth, perform adaptation analysis of the dynamic switching adjustment strategy using the selection discrimination results and multi-level energy modes, establish array element emission parameters controlled by MOS transistor switches, and execute emission control management. Fifth, optimize the emission parameters of array elements based on the dynamic adjustment results, improving the signal-to-noise ratio and local detail clarity of imaging while reducing energy loss, providing efficient and precise emission control management for 4DICE ultrasound imaging.

[0039] Furthermore, by utilizing the selection and discrimination results and the adaptation analysis of the multi-level energy mode to execute the dynamic switching adjustment strategy, the array element emission parameters controlled by the MOS transistor switch are established. The method of this application includes: Configure the timing activation state of the array elements according to the selection and discrimination results, and use the multi-level energy mode as the response energy mapping of the array elements; use the timing activation state and response energy mapping to perform smooth transition processing of array element energy; perform adaptation analysis of dynamic switching adjustment strategy according to the smooth transition processing results.

[0040] Specifically, the selection and discrimination result refers to the result of judging the switching state of the MOSFET based on the gain threshold, which determines whether the MOSFET should be closed or open; the timing activation state describes the activation state of the array element at different time points, that is, when the array element is activated for signal transmission; response energy mapping refers to mapping the multi-level energy mode to the response energy of the array element, that is, the energy output of each array element at different time points; smooth transition processing refers to processing the array element energy to ensure the stability of energy output and avoid signal distortion or equipment damage caused by sudden changes in energy; the adaptation analysis of dynamic switching adjustment strategy refers to adjusting and optimizing the dynamic switching adjustment strategy based on the results of smooth transition processing to ensure the best imaging effect and equipment operating efficiency.

[0041] Execution steps: Configure the timing activation state of array elements based on the selection and discrimination results, and use multi-level energy modes as the response energy mapping of array elements. Dynamically adjust the activation time and energy output of array elements according to signal characteristics and gain thresholds to ensure that each array element transmits signals with appropriate energy at the appropriate time point. Perform smooth transition processing of array element energy using timing activation states and response energy mapping to ensure the stability of array element energy output and avoid signal distortion or equipment damage caused by sudden energy changes. Furthermore, through smooth transition processing, control the rate of change of array element energy within a certain range to reduce the impact of energy fluctuations on imaging quality. Perform adaptation analysis of the dynamic switching adjustment strategy based on the smooth transition processing results. Adjust and optimize the dynamic switching adjustment strategy based on the results of the smooth transition processing to ensure optimal imaging effect and equipment operating efficiency.

[0042] Furthermore, based on the smooth transition processing results, the method of this application performs an adaptation analysis of the dynamic switching adjustment strategy, including: The emission state energy curve in the smooth transition processing result is converted into a multi-level switching adjustment signal target sequence; the multi-level switching adjustment signal target sequence is used as the control target, and the multi-level conduction mode of the MOS transistor switch is configured. Under the constraints of drive delay and switching loss, the duty cycle, pulse envelope shape and drive voltage amplitude of the MOS transistor switch are set to complete the adaptation analysis of the dynamic switching adjustment strategy.

[0043] Specifically, the smooth transition processing result refers to the processed energy output result of the array element, ensuring the stability of the energy output; the emission state energy curve is used to describe the energy output state of the array element at different time points; the multi-level switching adjustment signal target sequence refers to converting the emission state energy curve into a series of specific switching adjustment signals to control the switching state of the MOSFET; the multi-level conduction mode refers to the multiple conduction states of the MOSFET switch, used to realize the transmission of signals at different energy levels; the drive delay refers to the time delay from receiving the control signal to the actual start of conduction of the MOSFET; the switching loss refers to the energy loss generated by the MOSFET during the switching process; the duty cycle refers to the ratio of the conduction time of the MOSFET to the total time in one cycle; the pulse envelope shape refers to the shape of the pulse signal, including the rise time, fall time, and duration; and the drive voltage amplitude is used to control the magnitude of the voltage at which the MOSFET is turned on.

[0044] Execution steps: The emission state energy curve in the smooth transition processing result is converted into a multi-level switching adjustment signal target sequence. The continuous energy output curve is converted into a series of discrete switching adjustment signals to facilitate actual hardware control. Furthermore, if the emission state energy curve indicates that a higher energy output is required within a certain time period, the corresponding multi-level switching adjustment signal target sequence will instruct the MOSFET to conduct at a higher energy level within that time period. Using the multi-level switching adjustment signal target sequence as the control target, the multi-level conduction mode of the MOSFET switch is configured. Under the constraints of drive delay and switching losses, the duty cycle, pulse envelope shape, and drive voltage amplitude of the MOSFET switch are set to ensure that the MOSFET can perform switching operations according to the requirements of the target sequence while meeting the hardware constraints, thereby achieving precise energy control. Through the above steps, the switching losses of the MOSFET are reduced, while the signal-to-noise ratio of the imaging is improved. By precisely controlling the duty cycle and pulse envelope shape of the MOSFET, the energy output is further optimized, reducing the impact of energy fluctuations on imaging quality and significantly improving the stability and accuracy of imaging.

[0045] Furthermore, based on the location dataset and the phase difference data of adjacent frame echoes, a fractal prediction model for duct motion is established. This fractal prediction model is then used to establish N branches for predicting the motion trajectory. The method of this application includes: Obtain the displacement sequence of key points of the catheter in the location dataset, and establish a multi-scale temporal feature vector of catheter motion based on the displacement sequence of key points of the catheter and the phase difference data; perform fractal dimension estimation on the multi-scale temporal feature vector to construct a fractal prediction model, which is used to characterize the self-similarity and irregularity of catheter motion at different scales; use the fractal prediction model to perform prediction at different scales and establish N predicted motion trajectory branches.

[0046] Specifically, the location dataset refers to the data set acquired by the catheter position sensor, recording the catheter's position information at different time points; the catheter key point displacement sequence is the displacement information sequence of key points of the catheter extracted from the location dataset, reflecting the catheter's motion at different time points; the phase difference data refers to the phase difference between adjacent frame echo signals, used to analyze the dynamic characteristics of catheter motion; the multi-scale temporal feature vector contains vectors of catheter motion characteristics at different time scales, used to describe the complexity of catheter motion; fractal dimension estimation refers to estimating the fractal dimension of the multi-scale temporal feature vector using mathematical methods, used to quantify the self-similarity and irregularity of catheter motion; the fractal prediction model refers to a prediction model built based on fractal theory, used to predict the future trajectory of catheter motion; the predicted motion trajectory branch refers to the possible motion paths of the catheter generated by the fractal prediction model, with each branch representing a possible motion situation.

[0047] Execution steps: First, displacement sequences of key catheter points are obtained from the location dataset. Then, a multi-scale temporal feature vector of catheter motion is established by combining this vector with phase difference data. Further, key features of catheter motion are extracted, including displacement and phase changes at different time scales, providing foundational data for subsequent predictions. Second, fractal dimension estimation is performed on the multi-scale temporal feature vector to construct a fractal prediction model. Fractal dimension estimation quantifies the self-similarity and irregularity of catheter motion. The fractal prediction model uses these features to predict the catheter's trajectory. Further analysis using fractal dimension estimation reveals that catheter motion exhibits strong self-similarity at certain time scales, while showing high irregularity at others. Third, predictions are performed at different scales using the fractal prediction model, establishing N predicted trajectory branches and generating multiple possible catheter motion paths. This provides various contingency plans for imaging control, reduces motion artifacts in ultrasound imaging, and improves the spatiotemporal coherence of imaging.

[0048] Furthermore, after configuring a dynamic switch adjustment strategy based on the mapping results, the emission control management of ultrasound imaging is executed. The method of this application includes: Perform a self-test of the MOSFET switch and establish a switch state dataset; identify MOSFET switch anomalies based on the dynamic switch adjustment strategy and the switch state dataset and establish an abnormal switch identifier; perform equivalent MOSFET switch switching control based on the abnormal switch identifier.

[0049] Specifically, MOSFET switch self-test refers to performing a self-test on the switching state of MOSFETs to determine whether they are working properly; the switch state dataset is a collection of data recording the switching states of MOSFETs, including whether each MOSFET is currently on or off; the dynamic switch adjustment strategy refers to a strategy that dynamically adjusts the switching state of MOSFETs based on signal characteristics and gain thresholds; MOSFET switch anomaly identification refers to identifying whether there are any anomalies in MOSFETs based on the dynamic switch adjustment strategy and the switch state dataset; abnormal switch identification refers to marking those MOSFET switches identified as abnormal; and equivalent MOSFET switch switching control refers to switching abnormal MOSFET switches to equivalent normal switches based on the abnormal switch identification to ensure the normal operation of the system.

[0050] Execution steps: Perform a self-test on the MOSFET switches to establish a switch status dataset and monitor the switching status of the MOSFETs in real time to ensure that each MOSFET can work normally. Furthermore, the self-test can detect abnormalities such as short circuits or open circuits in a particular MOSFET. Based on the dynamic switch adjustment strategy and the switch status dataset, perform MOSFET switch anomaly identification and establish abnormal switch identifiers. By comparing the current switch status with the expected switch status, identify those MOSFETs that do not meet expectations. Further, if a MOSFET is on when it should be off, or off when it should be on, then this MOSFET will be marked as abnormal. Perform equivalent MOSFET switch switching control based on the abnormal switch identifiers, switching the abnormal MOSFETs to equivalent normal switches to ensure the normal operation of the system. Furthermore, if a MOSFET is identified as abnormal, it is automatically switched to a backup MOSFET, reducing the MOSFET failure rate and thus preventing the entire system from malfunctioning due to the failure of a single MOSFET, significantly improving the stability and reliability of the system.

[0051] Furthermore, the method of this application includes: Record the actual control parameters of the array elements, establish control feedback, and use the control feedback to perform iterative update management of MOS transistor switching control.

[0052] Specifically, the actual control parameters of the array element are the parameters actually used to control the transmission and reception of signals by the array element, including transmission energy, transmission time, and reception gain; control feedback refers to the feedback mechanism formed by monitoring and recording the actual control parameters of the array element, which is used to evaluate and adjust the control strategy; iterative update management refers to continuously adjusting and optimizing the control strategy of MOS transistor switching based on control feedback in order to achieve better imaging effect and system performance.

[0053] Execution steps: Record the actual control parameters of the array elements, which reflect the actual state of the array elements during operation; establish a control feedback mechanism by recording the actual control parameters to evaluate the effectiveness of the current control strategy; furthermore, if the actual emission energy of a certain array element deviates from the expected value, or the actual switching state of a certain MOSFET does not match the expectation, this information will be recorded; use the control feedback to iteratively update and manage the switching control of the MOSFETs, continuously adjusting and optimizing the switching control strategy of the MOSFETs based on the feedback information; furthermore, if the feedback shows that the switching state of a certain MOSFET is abnormal, automatically adjust the control parameters of that MOSFET, or switch to a backup MOSFET to ensure the normal operation of the system. At the same time, through iterative updates, continuously learn and adapt to different imaging conditions, optimize the control strategy, reduce the system failure rate, and significantly improve imaging quality and system performance.

[0054] In summary, the beneficial effects of the embodiments of this application are: By employing the position dataset from the catheter position sensor, a fractal prediction model for catheter motion is established based on the position dataset and the phase difference data of adjacent frame echoes. This fractal prediction model is used to establish N predicted motion trajectory branches, each configured with an initial time delay multiplexing scheme. At the receiving end, frame echo coherence analysis is performed within a preset frame interval to construct a direction gain tensor containing both time and spatial dimensions. The direction gain tensor and the target region of interest are used to perform region-of-interest weighting processing to establish a direction-of-interest gain distribution. After mapping the direction-of-interest gain distribution to the N predicted motion trajectory branches, low-rank sparse decomposition is performed to establish a low-rank sparse decoupling result. This low-rank sparse decoupling result is then mapped to the element activation mode of the MOS transistor switching matrix. Based on the mapping result, a dynamic switching adjustment strategy is configured, and then the transmission control management for ultrasound imaging is executed. This application provides a 4DICE ultrasound imaging transmission control method based on time delay data multiplexing. This technology captures the irregular features of duct motion, constructs a directional gain tensor and combines it with the target region of interest to form a gain distribution in the direction of interest. Through low-rank sparse decomposition, it achieves accurate mapping between gain and trajectory, enhances signal gain in key regions, and decouples the basic structure from local focusing features, thereby improving the imaging signal-to-noise ratio and the clarity of local details. This provides efficient and precise emission control management for 4DICE ultrasound imaging.

[0055] In summary, any step can be stored as a computer instruction or program in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor; no further restrictions are imposed here.

[0056] Furthermore, the above technical solutions only embody the preferred technical solutions of the embodiments of this application. Any changes that those skilled in the art may make to certain parts of these solutions embody the novel principles of the embodiments of this application. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application.

Claims

1. A 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing, characterized in that, The method includes: Read the position dataset of the catheter position sensor, establish a fractal prediction model of catheter motion based on the position dataset and the phase difference data of adjacent frame echoes, and establish N predicted motion trajectory branches using the fractal prediction model, wherein each predicted motion trajectory branch is configured with an initial time delay multiplexing scheme. At the receiving end, frame echo coherence analysis is performed within a preset frame interval to construct a directional gain tensor containing time and spatial dimensions. The directional gain tensor and the target region of interest are used to perform region of interest weighting to establish the direction of interest gain distribution. After mapping the interest direction gain distribution to N predicted motion trajectory branches, low-rank sparse decomposition is performed to establish low-rank sparse decoupling results. The low-rank sparse decoupling results are mapped to the element activation modes of the MOS transistor switching matrix. After configuring a dynamic switching adjustment strategy based on the mapping results, the transmission control management of ultrasound imaging is executed. At the receiving end, perform frame echo coherence analysis within a preset frame interval to construct a directional gain tensor containing both temporal and spatial dimensions, including: Perform echo signal phase consistency calculation for each frame echo and establish signal direction stability index; Perform echo signal amplitude change rate and local peak value analysis for each frame echo to establish signal-to-noise ratio change rate characteristics and echo amplitude gradient characteristics; Based on the signal direction stability index, signal-to-noise ratio change rate characteristics, and echo amplitude gradient characteristics, a multidimensional coherence feature vector is established for each array element and each direction in each frame echo. An initial directional gain tensor is established using the multidimensional coherence feature vector based on the time dimension, array element space dimension, and emission direction dimension; For each initial directional gain tensor, perform temporal smoothing on adjacent frames to establish the first update result; Perform continuous frame gain identification based on the first update result, establish a second update result, wherein the continuous frame gain identification is temporal continuous gain compensation, and output the second update result as a directional gain tensor.

2. The 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing as described in claim 1, characterized in that, Using the aforementioned directional gain tensor and the target region of interest, a region-of-interest weighting process is performed to establish the directional gain distribution, including: Each directional element of the directional gain tensor is assigned a weighting coefficient according to the spatial distribution of the target region of interest, and the weighting coefficient is adaptively configured based on the clinical priority, local tissue structure characteristics and signal reliability index of the target region of interest. The weighted calculation of the directional gain tensor is performed using the weighting coefficients to establish the gain distribution of the direction of interest.

3. The 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing as described in claim 1, characterized in that, After mapping the interest direction gain distribution to N predicted motion trajectory branches, low-rank sparse decomposition is performed to establish low-rank sparse decoupling results, including: After mapping the interest direction gain distribution and N predicted motion trajectory branches to the same coordinate system, local interest direction gain data of the N predicted trajectory branches are established according to the corresponding mapping relationship between position and direction. A multi-branch tensor is constructed based on N predicted trajectory branches with local interest direction gain data, and low-rank sparse decomposition is performed, including: extracting the gain components that satisfy the preset correlation direction as low-rank basic structures, establishing sparse substructures from the gain features of the local focus region, and making the sparse substructures explicit. Based on the low-rank basic structure and the explicit sparse substructure, a low-rank sparse decoupling result is established.

4. The 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing as described in claim 1, characterized in that, The low-rank sparse decoupling results are mapped to the element activation modes of the MOS transistor switching matrix. After configuring a dynamic switching adjustment strategy based on the mapping results, the transmission control management for ultrasound imaging is executed, including: Establish an index mapping relationship between each tensor element in the low-rank sparse decoupling result and the corresponding MOS transistor switch; The multi-level energy mode of the array element is configured according to the index mapping relationship, and the closing and opening selection of the MOS transistor switch is determined by the gain threshold. By utilizing the selection and discrimination results and the adaptation analysis of the multi-level energy mode to execute the dynamic switching adjustment strategy, the array element emission parameters controlled by the MOS transistor switch are established, and emission control management is performed.

5. The 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing as described in claim 4, characterized in that, Based on the selection discrimination results and the adaptation analysis of the multi-level energy mode to execute the dynamic switching regulation strategy, the array element emission parameters controlled by the MOS transistor switch are established, including: Configure the temporal activation state of the array elements according to the selection and discrimination results, and use the multi-level energy mode as the response energy mapping of the array elements; The timing activation state and response energy mapping are used to perform smooth transition processing of array element energy; Based on the smooth transition processing results, an adaptation analysis of the dynamic switching adjustment strategy is performed.

6. The 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing as described in claim 5, characterized in that, Based on the smooth transition processing results, an adaptation analysis of the dynamic switching adjustment strategy is performed, including: The emission state energy curve in the smooth transition processing result is converted into a multi-level switching adjustment signal target sequence; Using the multi-level switching adjustment signal target sequence as the control target, the multi-level conduction mode of the MOS transistor switch is configured. Under the constraints of drive delay and switching loss, the duty cycle, pulse envelope shape and drive voltage amplitude of the MOS transistor switch are set to complete the adaptation analysis of the dynamic switching adjustment strategy.

7. The 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing as described in claim 1, characterized in that, Based on the location dataset and the phase difference data of adjacent frame echoes, a fractal prediction model for duct motion is established. This fractal prediction model is then used to establish N branches for predicting the motion trajectory, including: Obtain the displacement sequence of catheter key points in the location dataset, and establish a multi-scale temporal feature vector of catheter motion based on the displacement sequence of catheter key points and the phase difference data; The fractal dimension of the multi-scale temporal feature vector is estimated to construct a fractal prediction model, which is used to characterize the self-similarity and irregularity of duct motion at different scales. The fractal prediction model is used to perform predictions at different scales, and N branches of the predicted motion trajectory are established.

8. The 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing as described in claim 1, characterized in that, After configuring the dynamic switch adjustment strategy based on the mapping results, the transmission control management of ultrasound imaging is executed, including: Perform a switch self-test on the MOSFET switch and establish a switch status dataset; Based on the dynamic switching adjustment strategy and the switch state dataset, MOSFET switch anomaly identification is performed, and an abnormal switch identifier is established. The equivalent MOS transistor switching control is performed based on the abnormal switch identifier.

9. The 4DICE ultrasonic imaging transmission control method based on time-delay data multiplexing as described in claim 1, characterized in that, Record the actual control parameters of the array elements, establish control feedback, and use the control feedback to perform iterative update management of MOS transistor switching control.

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