Real-time imaging method and system for three-dimensional ultrasonic imaging catheter

Abstract

The invention discloses a real-time imaging method and system for a three-dimensional ultrasonic imaging catheter, and relates to the technical field of three-dimensional imaging, and the method comprises the steps: obtaining a preset imaging target and tissue type data, and building a three-dimensional imaging target model; performing high-frame-frequency electronic scanning on the target area, and performing imaging quality analysis on returned scanning data to obtain a real-time imaging quality parameter set; performing deviation calculation to obtain a quality parameter deviation set; and performing probe control and scanning path optimization analysis based on the quality parameter deviation set, and outputting an optimal probe control scheme and an optimal scanning path to perform subsequent three-dimensional imaging operation. The technical problems of poor imaging quality and low scanning efficiency caused by lack of effective optimization means for the probe control and scanning path of the three-dimensional imaging equipment in the prior art are solved, and the technical effects of realizing optimization of the probe control and scanning path of the three-dimensional imaging equipment and improving the imaging quality and the scanning efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional imaging, and in particular to a real-time imaging method and system for a three-dimensional ultrasonic imaging catheter. Background Art

[0002] Traditional three-dimensional imaging technology faces many challenges. In terms of probe control, existing methods are difficult to accurately adjust in real time according to different imaging targets and tissue types, resulting in deviations in the data collected by the probe. At the same time, scanning path planning lacks flexibility and pertinence, often using fixed patterns that fail to fully consider the complex characteristics of the target area. This seriously affects the imaging quality, and the image may appear blurred or details may be lost, which not only increases the difficulty of diagnosis, but may also lead to misdiagnosis. In addition, the inefficient scanning process consumes a lot of time and reduces inspection efficiency, which is extremely unfavorable for time-sensitive scenarios such as emergency departments.

[0003] The existing technology has the technical problem that the three-dimensional imaging equipment lacks effective optimization means for probe control and scanning path, resulting in poor imaging quality and low scanning efficiency. Summary of the invention

[0004] The present application provides a real-time imaging method and system for a three-dimensional ultrasound imaging catheter, which is used to solve the technical problem that the three-dimensional imaging equipment in the prior art lacks effective optimization means for probe control and scanning path, resulting in poor imaging quality and low scanning efficiency.

[0005] In view of the above problems, the present application provides a real-time imaging method and system for a three-dimensional ultrasound imaging catheter.

[0006] In a first aspect of the present application, a real-time imaging method for a three-dimensional ultrasound imaging catheter is provided, the method comprising: Acquire pre-set imaging target and tissue type data, and establish a three-dimensional imaging target model, wherein the three-dimensional imaging target model is embedded with a standard imaging quality parameter set; perform high frame rate electronic scanning of the target area according to an initial probe control scheme and an initial scanning path through a predetermined three-dimensional imaging device, and perform imaging quality analysis on the returned scanning data to obtain a real-time imaging quality parameter set; perform deviation calculation based on the standard imaging quality parameter set and the real-time imaging quality parameter set to obtain a quality parameter deviation set; perform probe control and scanning path optimization analysis based on the quality parameter deviation set, and output an optimal probe control scheme and an optimal scanning path for subsequent three-dimensional imaging operations.

[0007] In a second aspect of the present application, a real-time imaging system for a three-dimensional ultrasound imaging catheter is provided, the system comprising: A three-dimensional imaging target model building module is configured to obtain a preset imaging target and tissue type data, and build a three-dimensional imaging target model, wherein a standard imaging quality parameter set is embedded in the three-dimensional imaging target model; a real-time imaging quality parameter set acquisition module is configured to perform high-frame-rate electronic scanning on a target area through a predetermined three-dimensional imaging device according to an initial probe control scheme and an initial scanning path, and perform imaging quality analysis on the transmitted scanning data to obtain a real-time imaging quality parameter set; a quality parameter deviation set acquisition module is configured to obtain a quality parameter deviation set after performing deviation calculation based on the standard imaging quality parameter set and the real-time imaging quality parameter set; an optimal probe control scheme output unit is configured to perform probe control and scanning path optimization analysis based on the quality parameter deviation set, and output an optimal probe control scheme and an optimal scanning path for subsequent three-dimensional imaging operations.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: Obtain a preset imaging target and tissue type data, and build a three-dimensional imaging target model; perform high-frame-rate electronic scanning on a target area through a predetermined three-dimensional imaging device according to an initial probe control scheme and an initial scanning path, and perform imaging quality analysis on the transmitted scanning data to obtain a real-time imaging quality parameter set; obtain a quality parameter deviation set after performing deviation calculation; perform probe control and scanning path optimization analysis based on the quality parameter deviation set, and output an optimal probe control scheme and an optimal scanning path for subsequent three-dimensional imaging operations. It achieves the technical effect of optimizing the probe control and scanning path of the three-dimensional imaging device, thereby improving the imaging quality and scanning efficiency. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained without creative efforts based on these drawings.

[0010] Figure 1 It is a schematic flowchart of a real-time imaging method for a three-dimensional ultrasonic imaging catheter provided by an embodiment of the present application; Figure 2 It is a schematic structural diagram of a real-time imaging system for a three-dimensional ultrasonic imaging catheter provided by an embodiment of the present application.

[0011] Description of the reference numerals: the three-dimensional imaging target model building module 10, the real-time imaging quality parameter set acquisition module 20, the quality parameter deviation set acquisition module 30, the optimal probe control scheme output module 40. Detailed Embodiments

[0012] The present application provides a real-time imaging method and system for a three-dimensional ultrasonic imaging catheter, aiming to solve the technical problems in the prior art that there are lack of effective optimization means for probe control and scanning path in three-dimensional imaging devices, resulting in poor imaging quality and low scanning efficiency.

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0014] Embodiment 1, as Figure 1 shown, the present application provides a real-time imaging method for a three-dimensional ultrasonic imaging catheter, and the method includes: Step S100: Obtain pre-set imaging target and tissue type data, and establish a three-dimensional imaging target model, wherein the three-dimensional imaging target model is embedded with a standard imaging quality parameter set.

[0015] Specifically, the acquisition and processing of imaging target and tissue type data are crucial. Imaging technology is applied in the medical field, such as cardiac examinations. The operator first needs to determine a specific imaging target, for example, imaging different parts of the heart (atria, ventricles, etc.), and at the same time clarify relevant tissue type data, including the acoustic characteristics of myocardial, valve tissue, etc. These data are the basis for establishing the three-dimensional imaging target model. The model construction process uses professional software and algorithms (finite element analysis software and U-Net algorithm) to simulate the three-dimensional structure of the target area. And a standard imaging quality parameter set is embedded inside the model. This parameter set is set according to medical imaging standards and clinical experience, covering key indicators such as resolution, contrast, and signal-to-noise ratio. For example, 4D ICE technology aims to generate high-quality 3D ultrasonic images in real time. These standard parameters set an ideal benchmark for imaging quality, which is used for subsequent comparison and optimization of the actual imaging effect, ensuring that the finally presented ultrasonic image can meet the needs of clinical diagnosis and treatment, and helping doctors accurately observe the morphology and structure of tissues and organs.

[0016] Step S200: Perform high-frame-rate electronic scanning on the target area according to the initial probe control scheme and the initial scanning path through a predetermined three-dimensional imaging device, and perform imaging quality analysis on the back-transmitted scanning data to obtain a real-time imaging quality parameter set.

[0017] Specifically, the predetermined three-dimensional imaging device is built based on a predetermined two-dimensional probe array and an ASIC architecture. In the two-dimensional probe array, numerous ultrasonic probe elements are arranged in an orderly manner according to a predetermined geometric structure. At the beginning of imaging, the device precisely adjusts the signal emission parameters of each ultrasonic probe element according to the initial probe control scheme, such as signal delay, amplitude, excitation sequence, beam width, and frequency. At the same time, according to the initial scanning path, it conducts an electronic scan of the target area at a high frame rate. During the scanning process, the ultrasonic probe elements emit ultrasonic waves towards the target area. When encountering different tissue interfaces, echo signals are generated. These echo signals are received by the probe and transmitted back, forming the transmitted back scan data. The transmitted back scan data is processed using an image quality analysis algorithm based on deep learning to obtain a set of real-time imaging quality parameters. Taking an algorithm based on a convolutional neural network (CNN) as an example, first, the transmitted back scan data is preprocessed, adjusted to a format suitable for CNN input, such as operations like adjusting the data dimension and normalization, making the data features more easily learned by the network. Then, the preprocessed transmitted back scan data is input into a pre-trained CNN model. This model includes multiple convolutional layers, pooling layers, and fully connected layers. The convolutional layer slides a convolutional kernel over the data for convolution to extract local features in the data, such as features like the boundaries and textures of different tissues. The pooling layer downsamples the feature map output by the convolutional layer, reducing the data volume while retaining the main features, improving the computational efficiency and generalization ability of the model. The fully connected layer integrates the features after multiple convolutions and poolings and outputs the final prediction result. During this process, the model is trained based on a large amount of ultrasonic image data with labeled imaging quality parameters to learn the complex mapping relationship between the transmitted back scan data and the imaging quality parameters. After training, when the current transmitted back scan data is input, the model can output the corresponding set of real-time imaging quality parameters, covering key parameters such as resolution, contrast, and signal-to-noise ratio, providing a data basis for subsequent imaging quality optimization.

[0018] Step S300: Calculate the deviation after calculating the deviation between the standard imaging quality parameter set and the real-time imaging quality parameter set to obtain a set of quality parameter deviations.

[0019] Specifically, first, the standard imaging quality parameter set embedded in the three-dimensional imaging target model and the real-time imaging quality parameter set obtained after imaging quality analysis of the back-transmitted scan data are extracted separately. Both of these parameter sets cover key imaging quality indicators such as resolution, contrast, and signal-to-noise ratio. Then, deviation calculations are performed for each quality indicator separately. Taking resolution as an example, the resolution standard value in the standard imaging quality parameter set is subtracted from the actual resolution value in the real-time imaging quality parameter set to obtain the resolution deviation; similarly, for other parameters such as contrast and signal-to-noise ratio, the same method is used, that is, the standard value is subtracted from the real-time value to calculate their respective deviations. The deviations calculated for different quality indicators are aggregated to form a quality parameter deviation set. This deviation set can clearly reflect the gap between the current real-time imaging effect and the ideal standard in each quality dimension, providing a clear direction and quantitative basis for subsequent optimization of probe control and scanning path based on these deviations.

[0020] Step S400: Based on the quality parameter deviation set, perform optimization analysis of probe control and scanning path, and output the optimal probe control scheme and the optimal scanning path for subsequent three-dimensional imaging operations.

[0021] Specifically, a comprehensive and crucial optimization analysis is carried out based on the quality parameter deviation set to improve the imaging quality, and the probe control and scanning path are optimized respectively. In terms of probe control optimization, guided by the resolution deviation, contrast deviation, and signal-to-noise ratio deviation in the quality parameter deviation set, the probe control parameter threshold is used as the optimization space. For example, the probe control parameters include signal delay, amplitude, excitation order, beam width, and frequency, etc. An initial probe control scheme is randomly selected within the probe control parameter threshold, and with the help of an imaging quality compensation plugin constructed based on machine learning, the imaging quality compensation parameters under this scheme are predicted, such as resolution compensation data, contrast compensation data, and signal-to-noise ratio compensation data, etc. Then, a scheme fitness evaluation function is constructed based on the resolution deviation, contrast deviation, and signal-to-noise ratio deviation (where is the scheme fitness, , , are the resolution weight, contrast weight, and signal-to-noise ratio weight respectively, K is the resolution deviation, k is the resolution compensation data, P is the contrast deviation, p is the contrast compensation data, Y is the signal-to-noise ratio deviation, and y is the signal-to-noise ratio compensation data.). The fitness of the imaging quality compensation parameters is evaluated, and the scheme fitness is output. By continuously randomly selecting probe control schemes within the probe control parameter threshold for iterative compensation prediction and evaluation until the predetermined number of iterations is reached, the probe control scheme corresponding to the maximum scheme fitness is finally determined as the optimal probe control scheme.

[0022] In terms of optimizing the scanning path, first obtain the first set of quality parameter deviations of the initial scanning path, where the initial scanning path includes the initial scanning angle. Determine the first comprehensive quality coefficient based on the evaluation of the first set of quality parameter deviations. If the coefficient is greater than or equal to the predetermined quality threshold, set the initial scanning angle as the secondary scanning angle; if it is less than the predetermined quality threshold, randomly select any angle within the scanning angle threshold other than the initial scanning angle and set it as the secondary scanning angle. In addition, the historical scanning angle sequence and the historical comprehensive quality coefficient sequence will also be recorded. Cluster the historical scanning angle sequence according to the predetermined angle interval to determine multiple historical scanning angle intervals, and calculate the average comprehensive quality coefficient of each interval based on the historical comprehensive quality coefficient sequence. If the average comprehensive quality coefficient greater than or equal to the predetermined quality threshold is not zero, set the historical scanning angle interval corresponding to the maximum average comprehensive quality coefficient as the optimal historical scanning angle interval, and randomly select any angle within this interval as the optimal scanning path; if such an average value is zero, randomly select an angle within the scanning angle threshold other than the multiple historical scanning angle intervals as the optimal scanning path. After the above optimization analysis of the probe control and the scanning path, finally output the optimal probe control scheme and the optimal scanning path, and apply them to the subsequent three-dimensional imaging operation to improve the imaging quality.

[0023] In a possible implementation manner, step S200 further includes: Step S210: The predetermined three-dimensional imaging device is built based on a predetermined two-dimensional probe array and an ASIC architecture. The predetermined two-dimensional probe array includes a plurality of ultrasonic probe elements, where the plurality of ultrasonic probe elements are arranged according to a predetermined geometric structure, and the predetermined geometric structure includes a predetermined shape, a predetermined size, and a predetermined arrangement manner.

[0024] Specifically, when constructing a predetermined three-dimensional imaging device for three-dimensional ultrasonic imaging, its core architecture is built based on a predetermined two-dimensional probe array and an ASIC (Application Specific Integrated Circuit). The predetermined two-dimensional probe array is a key component of the device and is composed of a large number of ultrasonic probe elements. These ultrasonic probe elements are not randomly distributed but are strictly arranged according to a predetermined geometric structure, which covers aspects such as a predetermined shape, a predetermined size, and a predetermined arrangement. From the perspective of the predetermined shape, the two-dimensional probe array can be designed into a rectangular shape, a circular shape, or other specific shapes. Different shapes will affect the emission and reception ranges of ultrasonic signals. For example, the rectangular probe array has a relatively regular signal coverage in certain directions and is suitable for large-area scanning of specific regions; the circular probe array may have certain advantages in omnidirectional signal acquisition. The predetermined size determines the physical size of each ultrasonic probe element, which is directly related to its ability to emit and receive ultrasonic signals. Larger probe elements may have an advantage in signal intensity, while smaller probe elements may perform better in terms of resolution. In terms of the predetermined arrangement, the ultrasonic probe elements are arranged in an equally spaced linear arrangement, a matrix arrangement, etc. The equally spaced linear arrangement is conducive to dense scanning in a certain direction to obtain detailed information; the matrix arrangement can achieve a more flexible scanning mode and improve the imaging ability for target regions with complex shapes. Through such a carefully designed predetermined two-dimensional probe array, combined with the powerful signal processing ability of the ASIC architecture, the predetermined three-dimensional imaging device can operate efficiently, laying a solid foundation for subsequent three-dimensional ultrasonic imaging work.

[0025] In a possible implementation manner, step S200 further includes: Step S220: Obtain the backscattered scan data, where the backscattered scan data includes a plurality of echo signals backscattered by a plurality of ultrasonic probe elements.

[0026] Step S230: Perform denoising processing and time-domain alignment on the plurality of echo signals to obtain standard backscattered scan data.

[0027] Step S240: Analyze the imaging quality of the standard backscattered scan data according to a predetermined evaluation index to obtain a set of real-time imaging quality parameters, where the imaging quality parameters at least include resolution, contrast, and signal-to-noise ratio.

[0028] Specifically, the process of processing the backscattered data begins. After the ultrasonic probe elements in the two-dimensional probe array of the predetermined three-dimensional imaging device complete the scanning of the target region, backscattered scan data will be generated, which contains a plurality of echo signals backscattered by a plurality of ultrasonic probe elements respectively. Each echo signal carries acoustic information of different positions in the target region and is the original data basis for subsequent imaging analysis.

[0029] Preprocess these echo signals to improve data quality. Denoising is one of the key steps. Since various noises, such as electronic noise and environmental noise, will inevitably be mixed in during signal transmission, these noises will interfere with the extraction of the true information of the target area. By adopting appropriate filtering algorithms, such as Gaussian filtering, wavelet filtering, etc., remove the noise components in the echo signals and retain the useful signal features. At the same time, perform time-domain alignment operations because there may be slight differences in the time when different ultrasonic probe elements receive echo signals, which will affect the subsequent accurate judgment of the structure of the target area. By calibrating and adjusting the time information of the echo signals, make each echo signal have consistency in the time dimension, and finally obtain the standard back-transmitted scan data, providing reliable data support for accurate imaging quality analysis.

[0030] After obtaining the standard back-transmitted scan data, conduct a comprehensive imaging quality analysis on it according to the pre-set evaluation indicators to obtain a set of real-time imaging quality parameters. These evaluation indicators focus on the key aspects of imaging quality, among which resolution, contrast, and signal-to-noise ratio are important measurement parameters. For resolution, it is evaluated by analyzing the smallest details that can be distinguished in the standard back-transmitted scan data. For example, when imaging the heart, whether the fine structure of the myocardium can be clearly distinguished; contrast focuses on the gray-scale differences between different tissues and is determined by calculating the gray-scale changes in different regions. If the gray-scale differences between different tissues are obvious, the contrast is high, and the imaging effect is more conducive to distinguishing different tissues; the signal-to-noise ratio is used to measure the ratio of the signal intensity to the noise intensity and is obtained by calculating the ratio of the signal power to the noise power. A higher signal-to-noise ratio means that the signal is clearer and less affected by noise. By comprehensively analyzing and calculating these parameters, a set of real-time imaging quality parameters that can accurately reflect the actual quality of the current imaging is finally obtained, providing a quantitative basis for subsequent evaluation of the imaging effect and optimization of the imaging process.

[0031] In a possible implementation manner, step S400 further includes: Step S410: Obtain the set of quality parameter deviations, where the set of quality parameter deviations includes resolution deviation, contrast deviation, and signal-to-noise ratio deviation.

[0032] Step S420: For the purpose of satisfying the resolution deviation, contrast deviation, and signal-to-noise ratio deviation, conduct an optimization analysis of probe control with the probe control parameter threshold as the optimization space, and output the optimal probe control scheme. The probe control scheme includes the probe control parameters of several probes, and the probe control parameters include signal delay, amplitude, excitation sequence, beam width, and frequency.

[0033] Specifically, first, a set of quality parameter deviations is extracted, which is the key basis for judging the gap between the current imaging quality and the ideal standard. Among them, the resolution deviation reflects the difference between the ability of actual imaging to resolve fine structures and the standard requirements. For example, when imaging a human organ, if the actual imaging cannot clearly show the branching of tiny blood vessels in the organ while the standard imaging can clearly distinguish them, the gap between them is the resolution deviation; the contrast deviation reflects the degree to which the gray-scale difference between different tissues in the image deviates from the ideal state. Normally, different tissues should have obvious gray-scale distinctions in an ultrasound image for easy observation. If the gray-scales between tissues in the actual imaging are similar and difficult to distinguish, a contrast deviation occurs; the signal-to-noise ratio deviation measures the extent to which the ratio of signal intensity to noise intensity deviates from the standard. If excessive noise is mixed in the imaging process, resulting in a decrease in signal clarity and an imbalance in the signal-to-noise ratio, a signal-to-noise ratio deviation will occur. By obtaining this series of deviation data, it is possible to comprehensively understand the deficiencies of the current imaging quality in various key indicators, provide accurate direction guidance for subsequent targeted optimization and adjustment, and thus achieve the improvement of imaging quality.

[0034] With the goal of meeting the resolution deviation, contrast deviation, and signal-to-noise ratio deviation, the probe control parameter threshold is used as the optimization space. The probe control scheme covers the probe control parameters of multiple probes, and these parameters include signal delay, amplitude, excitation order, beam width, and frequency, which have a direct impact on the ultrasonic imaging quality. For example, the signal delay determines the time difference between the transmission and reception of ultrasonic signals. Reasonably adjusting the delay can optimize the signal focusing effect and thus improve the resolution; the amplitude size affects the intensity of ultrasonic signals. Appropriately changing the amplitude can enhance the distinction between signals and noise and improve the signal-to-noise ratio; adjusting the excitation order can change the working mode of the probe array, affect the formation and scanning range of the beam, and act on the contrast; the beam width determines the coverage area of the ultrasonic beam. An appropriate beam width helps to improve the resolution and contrast; the selection of frequency is closely related to the penetration depth and resolution of imaging. A higher frequency is suitable for high-resolution imaging of superficial structures, while a lower frequency can penetrate deeper tissues but has relatively lower resolution. During the optimization process, within the range defined by the probe control parameter threshold, different parameter combinations are continuously tried. Through simulation calculations or actual tests, the improvement effect of each combination on the imaging quality deviation is evaluated, and finally, the optimal probe control scheme that can minimize these deviations to the greatest extent is output, providing strong support for improving the three-dimensional ultrasonic imaging quality.

[0035] In a possible implementation manner, step S420 further includes: Step S421: Randomly select a first probe control scheme within the probe control parameter threshold, and use the imaging quality compensation plug-in to perform imaging quality compensation prediction on the first probe control scheme, and output the first imaging quality compensation parameter. The imaging quality compensation plug-in is constructed based on machine learning, and the imaging quality compensation parameter includes resolution compensation data, contrast compensation data, and signal-to-noise ratio compensation data.

[0036] Step S422: Based on the resolution deviation, contrast deviation, and signal-to-noise ratio deviation, construct a scheme fitness evaluation function, perform fitness evaluation on the first imaging quality compensation parameter, and output the first scheme fitness.

[0037] Step S423: Continue to randomly select a probe control scheme within the probe control parameter threshold for iterative compensation prediction and evaluation until a predetermined number of iterations is reached. The probe control scheme with the maximum scheme fitness output is set as the optimal probe control scheme.

[0038] Step S424: The expression of the scheme fitness evaluation function is: ; where is the scheme fitness, , , are the resolution weight, contrast weight, and signal-to-noise ratio weight respectively, K is the resolution deviation, k is the resolution compensation data, P is the contrast deviation, p is the contrast compensation data, Y is the signal-to-noise ratio deviation, and y is the signal-to-noise ratio compensation data.

[0039] Specifically, within the probe control parameter threshold, parameters such as signal delay, amplitude, excitation order, beam width, and frequency are randomly combined to generate the first probe control scheme. The imaging quality compensation plug-in is constructed based on a neural network. During the training phase of this network, a large amount of historical data is used, which includes different combinations of probe control parameters and the corresponding changes in imaging quality indicators. During training, the probe control parameters are used as the node data of the input layer, and the resolution compensation data, contrast compensation data, and signal-to-noise ratio compensation data are used as the target values of the output layer. Through the backpropagation algorithm, the weights and biases between neurons in the network are continuously adjusted, enabling the network to learn the complex mapping relationship between probe control parameters and imaging quality compensation. When the first probe control scheme is obtained, it is input into the trained neural network. The network calculates and reasons based on the learned patterns, predicts the imaging quality compensation situation under this scheme, and finally outputs the first imaging quality compensation parameter including resolution compensation data, contrast compensation data, and signal-to-noise ratio compensation data, providing a quantitative basis for evaluating the improvement effect of this scheme on imaging quality.

[0040] After obtaining the resolution deviation, contrast deviation, signal-to-noise ratio deviation, and the first imaging quality compensation parameters (including resolution compensation data, contrast compensation data, and signal-to-noise ratio compensation data), start constructing a scheme fitness evaluation function based on these data. This function comprehensively considers the importance of different imaging quality indicators and reflects the proportion of each indicator in the overall evaluation through the resolution weight, contrast weight, and signal-to-noise ratio weight. The function expression , clearly shows the relationship between the parameters. Substitute the first imaging quality compensation parameters into the evaluation function for calculation, and finally output the first scheme fitness. This value can intuitively and quantitatively reflect the comprehensive effect of the first probe control scheme in improving the imaging quality deviation. The higher the value, the more effectively the scheme can narrow the gap between the current imaging quality and the ideal standard, providing an important reference basis for subsequent screening of the optimal probe control scheme.

[0041] After completing the imaging quality compensation prediction and fitness evaluation of the first probe control scheme, start continuously exploring better schemes. Within the threshold range of the probe control parameters, randomly select new probe control schemes continuously. For each newly selected scheme, use the imaging quality compensation plugin constructed based on machine learning to perform imaging quality compensation prediction on it to obtain the corresponding imaging quality compensation parameters. Then, based on the scheme fitness evaluation function constructed based on the resolution deviation, contrast deviation, and signal-to-noise ratio deviation, perform fitness evaluation on the new imaging quality compensation parameters, so as to output the fitness of the scheme. This series of operations is repeated continuously. Each iteration may generate a new scheme fitness value. Continuously perform such iterative compensation prediction and evaluation until the preset number of iterations is reached. During the entire iterative process, record the scheme fitness generated in each iteration, and select the largest one from all the recorded fitness values. Finally, determine the probe control scheme corresponding to the maximum scheme fitness as the optimal probe control scheme. This optimal scheme can theoretically minimize the imaging quality deviation to the greatest extent, provide the most ideal probe control parameter settings for subsequent three-dimensional imaging, and thus effectively improve the quality of ultrasonic imaging.

[0042] Function expression Comprehensively considers the key indicators of imaging quality and provides a quantitative basis for evaluating the pros and cons of the probe control scheme. Is an important indicator to measure the comprehensive effect of a probe control scheme in improving imaging quality. In the formula , , They represent the resolution weight, contrast weight, and signal-to-noise ratio weight respectively. These weights reflect the relative importance of different imaging quality metrics in the overall evaluation. In practical applications, these weight values can be flexibly adjusted according to specific imaging requirements and scenarios. For example, in cases where high requirements for imaging fine structures exist, the resolution weight can be appropriately increased to highlight the role of the resolution metric in the evaluation.

[0043] K, P, and Y represent the resolution deviation, contrast deviation, and signal-to-noise ratio deviation respectively, which reflect the gaps between the current imaging quality and the ideal imaging quality in each metric. k, p, and y are the corresponding resolution compensation data, contrast compensation data, and signal-to-noise ratio compensation data, representing the possible improvement degrees of a certain probe control scheme in the corresponding metrics. By calculating the reciprocal of the absolute value of the difference between the deviation of each metric and the compensation data, multiplying by the corresponding weight, and then summing the three terms, the obtained scheme fitness can comprehensively reflect the ability of this probe control scheme to narrow the imaging quality deviation. The larger the scheme fitness value, the more effectively the scheme can reduce the imaging quality deviation, which means the scheme performs better in improving the imaging quality. In summary, this scheme fitness evaluation function provides a scientific and accurate evaluation criterion for screening the optimal probe control scheme through comprehensive consideration of the three key imaging quality metrics of resolution, contrast, and signal-to-noise ratio, which helps to improve the quality and effect of three-dimensional ultrasound imaging.

[0044] In a possible implementation manner, step S400 further includes: Step S430: Obtain the first set of quality parameter deviations of the initial scan path, where the initial scan path includes the initial scan angle.

[0045] Step S440: Evaluate and determine the first comprehensive quality coefficient according to the first set of quality parameter deviations. If the first comprehensive quality coefficient is greater than or equal to a predetermined quality threshold, set the initial scan angle as the secondary scan angle.

[0046] Step S450: If the first comprehensive quality coefficient is less than the predetermined quality threshold, randomly select any angle within the scan angle threshold except the initial scan angle as the secondary scan angle, and set the secondary scan angle as the optimal scan path.

[0047] Specifically, first focus on the initial scanning path, which includes the initial scanning angle. Based on the previously obtained set of standard imaging quality parameters and the real-time imaging quality parameter set, conduct in-depth analysis on the imaging data collected under the initial scanning path. By carefully comparing the key quality parameters such as resolution, contrast, and signal-to-noise ratio in the imaging data with the standard values, calculate the deviation values of each parameter. These deviation values are aggregated to form the first set of quality parameter deviations. For example, when calculating the resolution deviation, compare the minimum distinguishable detail specified in the standard imaging quality parameter set with the minimum distinguishable detail that can be actually imaged under the current initial scanning path to obtain the resolution deviation. The first set of quality parameter deviations comprehensively reflects the gap between the current imaging quality and the ideal imaging quality in each key quality dimension under the initial scanning path, providing crucial data support for subsequent evaluation of the imaging effect of the initial scanning path, determination of whether to adjust the scanning angle, and how to adjust it.

[0048] Use the resolution deviation, contrast deviation, and signal-to-noise ratio deviation in the first set of quality parameter deviations as input data to construct a neural network model. This neural network can include an input layer, several hidden layers, and an output layer. The neurons in the hidden layers are connected through weights and biases. During the training phase, use a large number of existing sets of quality parameter deviations and the corresponding known comprehensive quality coefficient data as training samples, and continuously adjust the weights and biases in the network through the backpropagation algorithm, enabling the network to learn the mapping relationship between the quality parameter deviations and the comprehensive quality coefficient. After the model is trained, input the current first set of quality parameter deviations into the trained neural network, and the network will output the predicted first comprehensive quality coefficient. Then, compare this coefficient with a predetermined quality threshold. If it is greater than or equal to the predetermined quality threshold, it indicates that the imaging quality under the current initial scanning path meets the requirements. Set the initial scanning angle as the secondary scanning angle so that subsequent imaging work can continue at this angle, thereby ensuring the stability and reliability of the imaging quality.

[0049] If the first comprehensive quality coefficient obtained in the previous step is less than the predetermined quality threshold, it means that the imaging quality under the initial scanning path does not meet the expected standard. At this time, it is necessary to adjust the scanning angle to improve the imaging effect. Operations are carried out within the range of the scanning angle threshold except for the initial scanning angle. This scanning angle threshold is preset according to factors such as the performance of the imaging device and the characteristics of the imaging target, and it defines the feasible variation range of the scanning angle. Randomly select an angle within this range and determine it as the secondary scanning angle. Since there is currently insufficient information to judge which angle can most effectively improve the imaging quality, the random selection method can widely explore other possible angles. Once the secondary scanning angle is determined, it is directly set as the optimal scanning path, and subsequent imaging operations will be carried out based on this new scanning path, hoping to improve quality parameters such as the resolution, contrast, and signal-to-noise ratio of the imaging by changing the scanning angle, thereby improving the overall imaging quality to meet or exceed the requirements of the predetermined quality threshold.

[0050] In a possible implementation manner, step S450 further includes: Step S451: Record the obtained historical scanning angle sequence and historical comprehensive quality coefficient sequence.

[0051] Step S452: Cluster multiple historical scanning angles in the historical scanning angle sequence according to the predetermined angle interval, determine multiple historical scanning angle intervals, and calculate the average comprehensive quality coefficients of the multiple historical scanning angle intervals according to the historical comprehensive quality coefficient sequence.

[0052] Step S453: If the average comprehensive quality coefficient greater than or equal to the predetermined quality threshold is not 0, set the historical scanning angle interval corresponding to the maximum average comprehensive quality coefficient as the optimal historical scanning angle interval, and randomly select any angle within the optimal historical scanning angle interval as the optimal scanning path.

[0053] Step S454: If the average comprehensive quality coefficient greater than or equal to the predetermined quality threshold is 0, randomly select any angle within the scanning angle threshold except for the multiple historical scanning angle intervals as the optimal scanning path.

[0054] Specifically, after each ultrasound imaging operation is completed, accurately record the scanning angle used in this imaging, and sequentially add this angle information to the historical scanning angle sequence according to the imaging order. At the same time, based on the set of quality parameter deviations obtained during the imaging process, by means of weighted summation, first identify the key parameters affecting the imaging quality, such as resolution deviation, contrast deviation, signal-to-noise ratio deviation, etc. Assign corresponding weights to each parameter, and the sum of these weights is 1. Then perform a function transformation on each parameter value (for example, take the reciprocal when the deviation value is small), and then multiply the transformed value by the weight and sum to obtain the comprehensive quality coefficient. Calculate the corresponding comprehensive quality coefficient and record these coefficients in the historical comprehensive quality coefficient sequence according to the imaging order. In this way, the historical scanning angle sequence and the historical comprehensive quality coefficient sequence completely record the scanning angles and corresponding quality performances of each imaging, providing rich data support for subsequent analysis.

[0055] Perform a clustering operation on each historical scanning angle in the historical scanning angle sequence according to a pre-set angle interval. For example, if the predetermined angle interval is 10 degrees per interval, the scanning angles will be divided into multiple intervals such as 0 - 10 degrees, 10 - 20 degrees, etc., thus determining multiple historical scanning angle intervals. Then, in combination with the historical comprehensive quality coefficient sequence, summarize and calculate the mean value of the comprehensive quality coefficients corresponding to each historical scanning angle interval. This mean value can reflect the average level of imaging quality within this angle interval, helping to screen out more optimal angle intervals in the subsequent process.

[0056] When the clustering of the historical scanning angles is completed and the mean values of the comprehensive quality coefficients of each historical scanning angle interval are calculated accordingly, these mean values will be compared with a predetermined quality threshold. If the mean value of the comprehensive quality coefficient greater than or equal to the predetermined quality threshold is not 0, that is, there is at least one historical scanning angle interval whose mean value of the comprehensive quality coefficient reaches or exceeds the predetermined quality threshold, this indicates that during past scanning attempts, the imaging quality presented in some angle intervals has reached or even exceeded the expected standard. At this time, from these qualified intervals, select the historical scanning angle interval with the largest mean value of the comprehensive quality coefficient. This is because the largest mean value of the comprehensive quality coefficient means that this interval shows the most excellent performance when comprehensively considering various quality indicators of imaging (such as resolution, contrast, signal-to-noise ratio, etc.), and has higher imaging quality potential. Therefore, it is set as the optimal historical scanning angle interval. In order to further explore the appropriate scanning angle within this high-quality angle interval and avoid falling into a local optimal solution, randomly select an angle within this optimal historical scanning angle interval and determine it as the optimal scanning path for subsequent ultrasound imaging operations, hoping to obtain high-quality imaging results through this.

[0057] If the average value of the comprehensive quality coefficient greater than or equal to the predetermined quality threshold is 0, this means that within all the statistically analyzed historical scanning angle intervals, the comprehensive quality of the imaging has not met the expected standard, and it is impossible to find a path that meets the quality requirements relying on the existing historical scanning angle data. At this time, in order to obtain an imaging result that meets the quality requirements, exploration is carried out in the area that is outside the existing multiple historical scanning angle intervals but still within the scanning angle threshold. This scanning angle threshold is a reasonable angle range preset according to various factors such as device performance and imaging requirements. A random angle is selected within this remaining feasible angle range and determined as the optimal scanning path. In this way, an attempt is made to break through the limitations of the past scanning angles, and imaging is performed at the randomly selected new angle, hoping to find a path that can bring high-quality imaging effects, so that the imaging quality reaches the predetermined standard.

[0058] Embodiment 2. Based on the same inventive concept as the real-time imaging method for the three-dimensional ultrasonic imaging catheter in the foregoing embodiment, as Figure 2 shown, the present application provides a real-time imaging system for a three-dimensional ultrasonic imaging catheter. The system in the embodiment of the present application and the method embodiment are based on the same inventive concept. Among them, the system includes: A three-dimensional imaging target model establishment module 10, configured to obtain preset imaging target and tissue type data, and establish a three-dimensional imaging target model, wherein the three-dimensional imaging target model is embedded with a standard imaging quality parameter set.

[0059] A real-time imaging quality parameter set acquisition module 20, configured to perform high-frame-rate electronic scanning on a target area through a predetermined three-dimensional imaging device according to an initial probe control scheme and an initial scanning path, and perform imaging quality analysis on the transmitted-back scanning data to obtain a real-time imaging quality parameter set.

[0060] A quality parameter deviation set acquisition module 30, configured to obtain a quality parameter deviation set after performing deviation calculation according to the standard imaging quality parameter set and the real-time imaging quality parameter set.

[0061] An optimal probe control scheme output module 40, configured to perform probe control and scanning path optimization analysis based on the quality parameter deviation set, and output an optimal probe control scheme and an optimal scanning path for subsequent three-dimensional imaging operations.

[0062] Furthermore, the system is also used to implement the following functions: The predetermined three-dimensional imaging device is built based on a predetermined two-dimensional probe array and an ASIC architecture. The predetermined two-dimensional probe array includes a plurality of ultrasonic probe elements, wherein the plurality of ultrasonic probe elements are arranged according to a predetermined geometric structure, and the predetermined geometric structure includes a predetermined shape, a predetermined size, and a predetermined arrangement manner.

[0063] Furthermore, the system is also used to implement the following functions: Obtain the backhaul scan data, where the backhaul scan data includes a plurality of echo signals returned by a plurality of ultrasonic probe elements; perform denoising processing and time-domain alignment on the plurality of echo signals to obtain standard backhaul scan data; perform imaging quality analysis on the standard backhaul scan data according to a predetermined evaluation index to obtain a set of real-time imaging quality parameters, where the imaging quality parameters at least include resolution, contrast, and signal-to-noise ratio.

[0064] Furthermore, the system is also used to implement the following functions: Obtain the set of quality parameter deviations, where the set of quality parameter deviations includes resolution deviation, contrast deviation, and signal-to-noise ratio deviation; aim to satisfy the resolution deviation, contrast deviation, and signal-to-noise ratio deviation, and perform probe control optimization analysis with the probe control parameter threshold as the optimization space to output an optimal probe control scheme, where the probe control scheme includes probe control parameters of a plurality of probes, and the probe control parameters include signal delay, amplitude, excitation order, beam width, and frequency.

[0065] Furthermore, the system is also used to implement the following functions: Randomly select a first probe control scheme within the probe control parameter threshold, use the imaging quality compensation plug-in to perform imaging quality compensation prediction on the first probe control scheme, and output the first imaging quality compensation parameter, where the imaging quality compensation plug-in is constructed based on machine learning, and the imaging quality compensation parameters include resolution compensation data, contrast compensation data, and signal-to-noise ratio compensation data; construct a scheme fitness evaluation function based on the resolution deviation, contrast deviation, and signal-to-noise ratio deviation, perform fitness evaluation on the first imaging quality compensation parameter, and output the first scheme fitness; continue to randomly select probe control schemes within the probe control parameter threshold for iterative compensation prediction and evaluation until a predetermined number of iterations is reached, and set the probe control scheme with the maximum scheme fitness as the optimal probe control scheme; the expression of the scheme fitness evaluation function is: ; where is the scheme fitness, , , are the resolution weight, contrast weight, and signal-to-noise ratio weight respectively, K is the resolution deviation, k is the resolution compensation data, P is the contrast deviation, p is the contrast compensation data, Y is the signal-to-noise ratio deviation, and y is the signal-to-noise ratio compensation data.

[0066] Furthermore, the system is also used to implement the following functions: Obtain a first set of quality parameter deviations of the initial scanning path, where the initial scanning path includes an initial scanning angle; evaluate and determine a first comprehensive quality coefficient according to the first set of quality parameter deviations. If the first comprehensive quality coefficient is greater than or equal to a predetermined quality threshold, set the initial scanning angle as the secondary scanning angle; if the first comprehensive quality coefficient is less than the predetermined quality threshold, randomly select any angle within the scanning angle threshold other than the initial scanning angle as the secondary scanning angle, and set the secondary scanning angle as the optimal scanning path.

[0067] Further, the system is also used to implement the following functions: Record the obtained historical scanning angle sequence and historical comprehensive quality coefficient sequence; cluster multiple historical scanning angles in the historical scanning angle sequence according to a predetermined angle interval to determine multiple historical scanning angle intervals, and calculate the average comprehensive quality coefficients of the multiple historical scanning angle intervals according to the historical comprehensive quality coefficient sequence; if the average comprehensive quality coefficient greater than or equal to the predetermined quality threshold is not 0, set the historical scanning angle interval corresponding to the maximum average comprehensive quality coefficient as the optimal historical scanning angle interval, and randomly select any angle within the optimal historical scanning angle interval as the optimal scanning path; if the average comprehensive quality coefficient greater than or equal to the predetermined quality threshold is 0, randomly select any angle within the scanning angle threshold other than the multiple historical scanning angle intervals as the optimal scanning path.

[0068] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the advantages or disadvantages of the embodiments. And the above describes specific embodiments of this specification. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0070] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A real-time imaging method for a three-dimensional ultrasound imaging catheter, characterized in that: Methods include: Acquiring pre-set imaging target and tissue type data, and establishing a three-dimensional imaging target model, wherein the three-dimensional imaging target model has a standard imaging quality parameter set embedded therein; By using a predetermined three-dimensional imaging device, a high frame rate electronic scan is performed on the target area according to an initial probe control scheme and an initial scanning path, and imaging quality analysis is performed on the returned scanning data to obtain a real-time imaging quality parameter set; A quality parameter deviation set is obtained by performing deviation calculation based on the standard imaging quality parameter set and the real-time imaging quality parameter set; Based on the quality parameter deviation set, probe control and scanning path optimization analysis are performed, and an optimal probe control solution and an optimal scanning path are output for subsequent three-dimensional imaging operations.

2. The real-time imaging method for a three-dimensional ultrasound imaging catheter according to claim 1, characterized in that: The predetermined three-dimensional imaging device is built based on a predetermined two-dimensional probe array and an ASIC architecture, wherein the predetermined two-dimensional probe array includes a plurality of ultrasound probe elements, wherein the plurality of ultrasound probe elements are arranged according to a predetermined geometric structure, and the predetermined geometric structure includes a predetermined shape, a predetermined size and a predetermined arrangement.

3. The real-time imaging method for a three-dimensional ultrasound imaging catheter according to claim 2, characterized in that: Perform imaging quality analysis on the returned scan data to obtain a real-time imaging quality parameter set, including: Acquiring return scan data, wherein the return scan data includes a plurality of echo signals returned by a plurality of ultrasound probe elements; Performing denoising and time domain alignment on the plurality of echo signals to obtain standard return scanning data; The imaging quality analysis of the standard returned scanning data is performed according to a predetermined evaluation index to obtain a real-time imaging quality parameter set, wherein the imaging quality parameters at least include resolution, contrast and signal-to-noise ratio.

4. The real-time imaging method for a three-dimensional ultrasound imaging catheter according to claim 2, characterized in that: Probe control optimization analysis is performed based on the quality parameter deviation set, including: Acquire the quality parameter deviation set, wherein the quality parameter deviation set includes a resolution deviation, a contrast deviation, and a signal-to-noise ratio deviation; In order to meet the resolution deviation, contrast deviation and signal-to-noise ratio deviation, probe control optimization analysis is performed with the probe control parameter threshold as the optimization space, and an optimal probe control scheme is output, wherein the probe control scheme includes probe control parameters of several probes, and the probe control parameters include signal delay, amplitude, excitation sequence, beam width and frequency.

5. The real-time imaging method for a three-dimensional ultrasound imaging catheter according to claim 4, characterized in that: In order to meet the resolution deviation, contrast deviation and signal-to-noise ratio deviation, the probe control optimization analysis is performed with the probe control parameter threshold as the optimization space, including: Randomly selecting a first probe control scheme within the probe control parameter threshold, using an imaging quality compensation plug-in to predict imaging quality compensation for the first probe control scheme, and outputting a first imaging quality compensation parameter, wherein the imaging quality compensation plug-in is constructed based on machine learning, and the imaging quality compensation parameter includes resolution compensation data, contrast compensation data, and signal-to-noise ratio compensation data; Constructing a scheme fitness evaluation function based on the resolution deviation, contrast deviation and signal-to-noise ratio deviation, performing fitness evaluation on the first imaging quality compensation parameter, and outputting the fitness of the first scheme; Continue to randomly select probe control schemes within the probe control parameter threshold for iterative compensation prediction and evaluation until a predetermined number of iterations is reached, and output the probe control scheme with the maximum scheme fitness as the optimal probe control scheme; The expression of the fitness evaluation function of the scheme is: ； in, is the solution fitness, , , are resolution weight, contrast weight and signal-to-noise ratio weight respectively, K is resolution deviation, k is resolution compensation data, P is contrast deviation, p is contrast compensation data, Y is signal-to-noise ratio deviation, and y is signal-to-noise ratio compensation data.

6. The real-time imaging method for a three-dimensional ultrasound imaging catheter according to claim 1, characterized in that: Performing a scanning path optimization analysis based on the quality parameter deviation set includes: Acquire a first quality parameter deviation set of an initial scanning path, wherein the initial scanning path includes an initial scanning angle; Determining a first comprehensive quality coefficient according to the first quality parameter deviation set evaluation, and if the first comprehensive quality coefficient is greater than or equal to a predetermined quality threshold, setting the initial scanning angle to a secondary scanning angle; If the first comprehensive quality coefficient is less than a predetermined quality threshold, any angle within the scanning angle threshold except the initial scanning angle is randomly selected as a secondary scanning angle, and the secondary scanning angle is set as an optimal scanning path.

7. The real-time imaging method for a three-dimensional ultrasound imaging catheter according to claim 6, characterized in that: Get the optimal scan path, also includes: Record and obtain historical scanning angle sequence and historical comprehensive quality coefficient sequence; Clustering multiple historical scanning angles in the historical scanning angle sequence according to a predetermined angle interval, determining multiple historical scanning angle intervals, and calculating multiple comprehensive quality coefficient means of the multiple historical scanning angle intervals according to the historical comprehensive quality coefficient sequence; If the mean value of the comprehensive quality coefficients greater than or equal to the predetermined quality threshold is not 0, the historical scanning angle interval corresponding to the maximum comprehensive quality coefficient mean value is set as the optimal historical scanning angle interval, and any angle is randomly selected within the optimal historical scanning angle interval as the optimal scanning path; If the average value of the comprehensive quality coefficients greater than or equal to the predetermined quality threshold is 0, any angle is randomly selected within the scanning angle threshold except the multiple historical scanning angle intervals to be set as the optimal scanning path.

8. A real-time imaging system for a three-dimensional ultrasound imaging catheter, characterized in that: The system is used to implement the real-time imaging method for a three-dimensional ultrasound imaging catheter according to any one of claims 1 to 7, and the system comprises: A three-dimensional imaging target model building module, used to obtain preset imaging target and tissue type data and build a three-dimensional imaging target model, wherein the three-dimensional imaging target model is embedded with a standard imaging quality parameter set; A real-time imaging quality parameter set acquisition module is used to perform high frame rate electronic scanning of the target area according to the initial probe control scheme and the initial scanning path through a predetermined three-dimensional imaging device, and to perform imaging quality analysis on the returned scanning data to acquire a real-time imaging quality parameter set; A quality parameter deviation set acquisition module, used to obtain a quality parameter deviation set after performing deviation calculation based on the standard imaging quality parameter set and the real-time imaging quality parameter set; The optimal probe control solution output module is used to perform probe control and scanning path optimization analysis based on the quality parameter deviation set, and output the optimal probe control solution and the optimal scanning path for subsequent three-dimensional imaging operations.

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