阅读说明：本技术 一种超声跨尺度和多参量检测的成像平台及方法 (Imaging platform and method for ultrasonic cross-scale and multi-parameter detection ) 是由 万明习 杨雅博 于海洋 张博 邹琴 温瑜 郭昊 赵岩 宗瑜瑾 王弟亚 于 2021-06-21 设计创作，主要内容包括：本发明公开了一种超声跨尺度和多参量检测的成像平台及方法,属于超声成像技术领域。包括：获取没有经过波束合成的通道数据,超声图像数据、视频数据；将通道数据进行波束合成得到射频数据；利用通道数据根据帧数选择,实现被动声学成像；利用射频数据,获得跨尺度超声结构图像：超分辨成像、多力学参量成像和微泡次谐波血压成像；针对超声图像、视频数据,被动声学成像,以及跨尺度超声结构图像,基于三维重建及可视化技术进行处理,获得超声三维图像。所述平台包括超声信号采集控制子平台、跨尺度多模成像子平台和三维重建子平台。本发明实现了基于超声跨尺度和多参量检测的多模成像。(The invention discloses an imaging platform and method for ultrasonic cross-scale and multi-parameter detection, and belongs to the technical field of ultrasonic imaging. The method comprises the following steps: acquiring channel data, ultrasonic image data and video data which are not subjected to beam forming; carrying out beam forming on the channel data to obtain radio frequency data; selecting according to the frame number by utilizing channel data to realize passive acoustic imaging; obtaining a cross-scale ultrasound structure image using the radio frequency data: super-resolution imaging, multi-mechanical parameter imaging and microbubble subharmonic blood pressure imaging; the method comprises the steps of processing ultrasonic images, video data, passive acoustic imaging and cross-scale ultrasonic structural images based on a three-dimensional reconstruction and visualization technology to obtain ultrasonic three-dimensional images. The platform comprises an ultrasonic signal acquisition control sub-platform, a cross-scale multi-mode imaging sub-platform and a three-dimensional reconstruction sub-platform. The invention realizes multimode imaging based on ultrasonic cross-scale and multi-parameter detection.)

1. An imaging method for ultrasonic cross-scale and multi-parameter detection is characterized by comprising the following steps:

step 1, based on cross-scale imaging or parameter detection, completing an acquisition task, and obtaining channel data which is not subjected to beam forming, ultrasonic image data and video data;

step 2, performing beam forming on the channel data which is obtained in the step 1 and is not subjected to beam forming to obtain radio frequency data;

step 3, determining an imaging area, an imaging mode and corresponding imaging parameters according to frame number selection by using the channel data which is not subjected to beam synthesis and is obtained in the step 1, and realizing passive acoustic imaging;

obtaining a cross-scale ultrasound structure image by using the radio frequency data obtained in the step 2, wherein the cross-scale ultrasound structure image comprises: super-resolution imaging, multi-mechanical parameter imaging and microbubble subharmonic blood pressure imaging;

step 4, aiming at the ultrasonic images and video data obtained in the step 1 and the passive acoustic imaging or cross-scale ultrasonic structure images obtained in the step 3, processing the images based on three-dimensional reconstruction and visualization technology to obtain ultrasonic three-dimensional images; therefore, the imaging method of ultrasonic cross-scale and multi-parameter detection is realized.

2. The imaging method of ultrasonic cross-scale and multi-parameter detection according to claim 1, wherein in step 4, the processing is performed based on a three-dimensional reconstruction and visualization technology, and specifically comprises: the method comprises the following steps of robot space correction, planning path data acquisition, three-dimensional interpolation reconstruction and voxel data three-dimensional visualization.

3. The imaging method for ultrasound cross-scale and multi-parameter detection according to claim 1, wherein in step 3, obtaining a cross-scale ultrasound structure image specifically comprises: extracting signals of ultrasonic contrast agent micro-bubbles or nano-droplets and connecting the signals into a track through space-time filtering, micro-bubble positioning, micro-bubble tracking and multi-frame compounding to realize super-resolution imaging and hemodynamic parameter estimation; multi-mechanical parameter imaging is realized through micro displacement estimation, shear wave velocity estimation and viscoelastic fitting; and the subharmonic blood pressure imaging is realized by sensing and selecting an area, extracting subharmonic amplitude and subnorwave estimating pressure.

4. The imaging method of claim 1, wherein in step 1, the task of acquisition specifically comprises waveform editing, setting an emission sequence, adjusting emission acquisition parameters, selecting an imaging mode, and setting a data type, which are performed in sequence.

5. The imaging method of ultrasonic cross-scale and multi-parameter detection according to claim 1, wherein before the cross-scale ultrasonic structural image is obtained by using the radio frequency data obtained in step 2, the artificial intelligence plane wave imaging with high frame rate and high signal-to-noise ratio is realized by using the deep learning method of the convolutional neural network for the radio frequency data obtained in step 2; and then, obtaining a cross-scale ultrasonic structural image by utilizing the obtained artificial intelligent plane wave imaging with high frame rate and high signal to noise ratio.

6. An imaging platform for ultrasonic cross-scale and multi-parameter detection is characterized by comprising an ultrasonic signal acquisition control sub-platform, a cross-scale multi-mode imaging sub-platform and a three-dimensional reconstruction sub-platform;

the ultrasonic signal acquisition control sub-platform is used for programming and transmitting ultrasonic signal waveforms, realizing acquisition and monitoring of ultrasonic signals and further obtaining channel data, radio frequency data, ultrasonic image data and video data which are not subjected to beam forming;

the cross-scale multimode imaging sub-platform is built on the ultrasonic signal acquisition control sub-platform and is used for acquiring ultrasonic plane wave imaging with a high frame rate and a high signal-to-noise ratio through an artificial intelligence plane wave imaging technology; aiming at the obtained ultrasonic plane wave imaging with high frame rate and high signal-to-noise ratio, the cross-scale multimode imaging sub-platform is used for obtaining a cross-scale ultrasonic structural image;

the three-dimensional reconstruction sub-platform is used for performing three-dimensional reconstruction and visualization processing on the obtained ultrasonic image data or the obtained cross-scale ultrasonic structural image to obtain an ultrasonic three-dimensional image.

7. The imaging platform for ultrasound cross-scale and multi-parameter detection according to claim 6, wherein for the obtained ultrasound plane wave imaging with high frame rate and high signal-to-noise ratio, the cross-scale multi-mode imaging sub-platform is configured to obtain a cross-scale ultrasound structural image, and specifically includes the following operations:

the cross-scale multimode imaging sub-platform is used for performing passive acoustic imaging to realize monitoring of the ultrasonic treatment process; the cross-scale multimode imaging sub-platform is used for performing super-resolution imaging and hemodynamic parameter estimation to obtain a tissue or transcranial tiny blood vessel network; the cross-scale multimode imaging sub-platform is used for imaging multi-mechanical parameters and detecting the mechanical characteristics of tissues; the cross-scale multimode imaging sub-platform is used for subharmonic blood pressure imaging and realizes noninvasive pressure detection.

8. The imaging platform for ultrasound cross-scale and multi-parametric detection according to claim 6, wherein the three-dimensional reconstruction sub-platform specifically comprises: the system comprises a robot, a level conversion module, a direct-current stabilized voltage supply, an arbitrary waveform generator, a pressure monitoring module and an industrial control computer;

the method comprises the steps of acquiring all-directional information through the movement of a planned path of a robot, and generating regular voxel data to perform three-dimensional reconstruction.

9. The imaging platform for ultrasound cross-scale and multi-parameter detection according to claim 6, further comprising a display and storage sub-platform for displaying the obtained image, or storing the displayed image picture, or directly storing the image data. The display parameters are adjusted to obtain the image with the best quality, and the image picture can be stored, or the image data can be directly stored for further imaging analysis and imaging method research.

10. The imaging platform for ultrasound cross-scale and multi-parametric detection of claim 9, wherein for the image to be displayed, the best quality image is obtained by adjusting the display parameters.

Technical Field

The invention belongs to the technical field of ultrasonic imaging, and relates to an imaging platform and method for ultrasonic cross-scale and multi-parameter detection.

Background

With the continuous research and development of ultrasonic imaging at home and abroad, a plurality of novel imaging methods are emerging continuously. Advances in imaging technology have enabled ultrasound imaging to achieve scale-spanning, with imaging resolution increasing from the millimeter scale to the micrometer scale. Meanwhile, the imaging technology not only can improve the image quality of imaging of human tissues, organs and blood vessels by utilizing an artificial intelligence technology and improve the signal to noise ratio of images, but also can measure parameters such as pressure, tissue mechanical characteristics and the like, and can realize monitoring of an ultrasonic treatment process, high-precision three-dimensional imaging and the like by detecting acoustic parameters. However, the research aiming at the leading edge imaging methods often has the problems of poor robustness of the imaging methods, single detection parameters, difficult realization of transcranial imaging and the like. Therefore, there is a need to develop a stable and effective method for multi-mode imaging, simultaneously achieve cross-scale imaging and multi-parameter detection, and obtain high-quality imaging results in both tissue imaging and transcranial imaging. Meanwhile, the research on the imaging method is mostly focused on the method due to the limitation of instruments at present, and the distance of the method relative to clinical application is long, so that the research on the method needs to be in track with the clinical application.

At the present stage, most of innovative researches on ultrasonic imaging are based on an open device, and various parameters of ultrasonic signals can be adjusted or the emission waveforms of the ultrasonic signals can be directly programmed, imaging data of all links in the ultrasonic imaging process can be selectively stored, and imaging researches can be performed according to the data of different links. For example, the digital programmable ultrasonic imaging experiment platform of Verasonics Inc. in the United states. The equipment has the functions of emission and acquisition, and is of great significance for developing a new imaging method. However, the equipment does not have a new imaging mode, and is difficult to adapt to the requirement of rapid industrial transformation, and meanwhile, the equipment is mainly used for research of science and technology, and due to the complexity of operation, the requirement of clinical ultrasonic equipment cannot be met, and the equipment is difficult to use by medical experts, the transformation from a front-edge imaging method to clinic is difficult to realize.

Therefore, in light of the clinical research of medical ultrasound imaging and the need for the conversion of imaging technology into clinics, there is a great need to develop imaging platforms with novel multi-modality imaging methods.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide an imaging platform and an imaging method for ultrasonic cross-scale and multi-parameter detection, and a multi-mode imaging method based on ultrasonic cross-scale and multi-parameter detection is realized.

In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:

the invention discloses an imaging method for ultrasonic cross-scale and multi-parameter detection, which comprises the following steps:

step 1, based on cross-scale imaging or parameter detection, completing an acquisition task, and obtaining channel data which is not subjected to beam forming, ultrasonic image data and video data;

step 2, performing beam forming on the channel data which is obtained in the step 1 and is not subjected to beam forming to obtain radio frequency data;

step 3, determining an imaging area, an imaging mode and corresponding imaging parameters according to frame number selection by using the channel data which is not subjected to beam synthesis and is obtained in the step 1, and realizing passive acoustic imaging;

obtaining a cross-scale ultrasound structure image by using the radio frequency data obtained in the step 2, wherein the cross-scale ultrasound structure image comprises: super-resolution imaging, multi-mechanical parameter imaging and microbubble subharmonic blood pressure imaging;

step 4, aiming at the ultrasonic images and video data obtained in the step 1 and the passive acoustic imaging or cross-scale ultrasonic structure images obtained in the step 3, processing the images based on three-dimensional reconstruction and visualization technology to obtain ultrasonic three-dimensional images; therefore, the imaging method of ultrasonic cross-scale and multi-parameter detection is realized.

Preferably, in step 4, the processing is performed based on a three-dimensional reconstruction and visualization technology, and specifically includes: the method comprises the following steps of robot space correction, planning path data acquisition, three-dimensional interpolation reconstruction and voxel data three-dimensional visualization.

Preferably, in step 3, obtaining the cross-scale ultrasound structure image specifically includes: extracting signals of ultrasonic contrast agent micro-bubbles or nano-droplets and connecting the signals into a track through space-time filtering, micro-bubble positioning, micro-bubble tracking and multi-frame compounding to realize super-resolution imaging and hemodynamic parameter estimation; multi-mechanical parameter imaging is realized through micro displacement estimation, shear wave velocity estimation and viscoelastic fitting; and the subharmonic blood pressure imaging is realized by sensing and selecting an area, extracting subharmonic amplitude and subnorwave estimating pressure.

Preferably, the ultrasonic three-dimensional image obtained in step 4 is displayed or stored.

Preferably, the passive acoustic imaging and the cross-scale ultrasonic structure image obtained in the step 3 are displayed or stored by adjusting display parameters, or image data is stored.

Preferably, in step 1, the acquisition task specifically includes waveform editing, setting an emission sequence, adjusting emission acquisition parameters, selecting an imaging mode, and setting a data type, which are performed in sequence.

Further preferably, the waveform editing is divided into plane wave waveform editing and focused wave waveform editing.

Further preferably, the sequence modes in the transmission sequence are set to be a plane wave mode, a focused wave mode and a plane wave alternating transmission mode respectively.

Preferably, before the radio frequency data obtained in the step 2 is used for obtaining the cross-scale ultrasonic structural image, the radio frequency data obtained in the step 2 is subjected to a deep learning method of a convolutional neural network, so that artificial intelligent plane wave imaging with a high frame rate and a high signal-to-noise ratio is realized; and then, obtaining a cross-scale ultrasonic structural image by utilizing the obtained artificial intelligent plane wave imaging with high frame rate and high signal to noise ratio.

The invention discloses an imaging platform for ultrasonic cross-scale and multi-parameter detection, which comprises an ultrasonic signal acquisition control sub-platform, a cross-scale multi-mode imaging sub-platform and a three-dimensional reconstruction sub-platform;

the ultrasonic signal acquisition control sub-platform is used for programming and transmitting ultrasonic signal waveforms, realizing acquisition and monitoring of ultrasonic signals and further obtaining channel data, radio frequency data, ultrasonic image data and video data which are not subjected to beam forming; the cross-scale multimode imaging sub-platform is built on the ultrasonic signal acquisition control sub-platform and is used for acquiring ultrasonic plane wave imaging with a high frame rate and a high signal-to-noise ratio through an artificial intelligence plane wave imaging technology; aiming at the obtained ultrasonic plane wave imaging with high frame rate and high signal-to-noise ratio, the cross-scale multimode imaging sub-platform is used for obtaining a cross-scale ultrasonic structural image; the three-dimensional reconstruction sub-platform is used for performing three-dimensional reconstruction and visualization processing on the obtained ultrasonic image data or the obtained cross-scale ultrasonic structural image to obtain an ultrasonic three-dimensional image.

Preferably, for the obtained ultrasonic plane wave imaging with the high frame rate and the high signal-to-noise ratio, the cross-scale multimode imaging sub-platform is used for obtaining a cross-scale ultrasonic structural image, and specifically includes the following operations:

the cross-scale multimode imaging sub-platform is used for performing passive acoustic imaging to realize monitoring of the ultrasonic treatment process; the cross-scale multimode imaging sub-platform is used for performing super-resolution imaging and hemodynamic parameter estimation to obtain a tissue or transcranial tiny blood vessel network; the cross-scale multimode imaging sub-platform is used for imaging multi-mechanical parameters and detecting the mechanical characteristics of tissues; the cross-scale multimode imaging sub-platform is used for subharmonic blood pressure imaging and realizes noninvasive pressure detection.

Preferably, the three-dimensional reconstruction sub-platform specifically includes: the system comprises a robot, a level conversion module, a direct-current stabilized voltage supply, an arbitrary waveform generator, a pressure monitoring module and an industrial control computer; the method comprises the steps of acquiring all-directional information through the movement of a planned path of a robot, and generating regular voxel data to perform three-dimensional reconstruction.

Preferably, the system further comprises a display and storage sub-platform, wherein the display and storage sub-platform is used for displaying the obtained image, or storing the displayed image picture, or directly storing the image data. The display parameters are adjusted to obtain the image with the best quality, and the image picture can be stored, or the image data can be directly stored for further imaging analysis and imaging method research.

Further preferably, for the image to be displayed, the image with the best quality is obtained by adjusting the display parameters.

Compared with the prior art, the invention has the following beneficial effects:

the invention is based on ultrasonic imaging, and realizes an imaging method of ultrasonic cross-scale and multi-parameter detection while freely setting to acquire ultrasonic signals. Wherein, the micron-sized tiny blood vessels can be imaged by a super-resolution imaging method; by the microbubble subharmonic blood pressure imaging method, noninvasive detection of intracranial blood pressure can be realized, and the viscosity and elasticity of tissues can be detected by a multi-mechanical parameter detection method. Meanwhile, a multimode imaging method and a three-dimensional reconstruction technology are integrated, and a feasible way is opened for scientific research and imaging methods to clinical transformation. Therefore, the imaging method of ultrasonic cross-scale and multi-parameter detection realizes ultrasonic super-resolution cross-scale imaging and multi-parameter detection of blood pressure, viscoelasticity and the like, and can analyze the detection parameters while performing structural imaging on the tiny blood vessels.

Furthermore, in the specific imaging process of each imaging technology in the cross-scale multimode imaging sub-platform, the related data obtained in each link can be stored, and the data is further researched, so that the research and improvement on the imaging technology are more convenient. Meanwhile, each imaging technology is open, step-by-step imaging can be carried out, each link of the imaging technology is researched, and a one-key imaging mode is further arranged, so that the method is suitable for clinical research which does not care about the imaging process and only focuses on the final imaging result.

Furthermore, various ultrasonic signal transmitting modes such as focusing waves, plane waves, diverging waves and the like are comprehensively realized, the waveform of the ultrasonic signal can be transmitted in a programmable mode, various transmitting parameters can be adjusted, the ultrasonic acquisition imaging can be set freely, and the ultrasonic data of each link can be stored.

The invention also discloses an imaging platform for ultrasonic cross-scale and multi-parameter detection, wherein the ultrasonic signal acquisition control sub-platform not only has the imaging function of the traditional ultrasonic equipment, but also can acquire various types of imaging data. Meanwhile, the imaging method for ultrasonic cross-scale and multi-parameter detection is carried, through the three-dimensional reconstruction sub-platform, the image data obtained by the ultrasonic signal acquisition control sub-platform can be used for carrying out three-dimensional reconstruction, and the result obtained by the cross-scale multi-mode imaging sub-platform can also be subjected to three-dimensional reconstruction, so that the reconstruction has high flexibility. Therefore, the imaging platform for ultrasonic cross-scale and multi-parameter detection effectively solves the problem that the imaging technology is difficult to convert into clinic.

Furthermore, the platform carries a linear frequency modulation signal transmitting function and a low-frequency transducer, and transcranial imaging of large animals or people is realized by the design of transmitting waveforms and the combination of imaging methods of all sub-platforms.

Furthermore, the display and storage sub-platform displays the results obtained by various imaging technologies in a unified manner, so that the integration of imaging in various scales and multi-parameter detection is realized, and image pictures and data results can be stored, thereby facilitating the development of research.

In summary, the imaging platform for ultrasonic cross-scale and multi-parameter detection and the imaging method for ultrasonic cross-scale and multi-parameter detection provided by the invention provide an imaging mode for acquiring ultrasonic signals by programmable emission waveforms and freely setting emission acquisition parameters, integrate a multi-mode imaging method and have an artificial intelligent plane wave imaging technology, thereby realizing cross-scale vascular imaging, vascular dynamics parameter extraction such as vascular diameter, blood flow rate and the like, vascular pressure detection, vascular surrounding tissue mechanics characteristic parameter detection and three-dimensional reconstruction technology, and the passive acoustic imaging technology can monitor the ultrasonic treatment process and realize transcranial imaging of large animals and people. An imaging platform and method for ultrasonic cross-scale and multi-parameter detection are formed. Therefore, the following advantages are provided:

firstly, the method comprises the following steps: the ultrasonic signal acquisition control sub-platform programs waveforms and adjusts emission parameters according to an imaging target and an expected imaging result, so that the ultrasonic imaging is freely set, and the research on the ultrasonic imaging is more flexible;

secondly, the method comprises the following steps: by monitoring the acquisition process, the situation that the acquisition process is failed and is not known by a user is avoided in the long-time acquisition process, and valuable time is saved for scientific research;

thirdly, the method comprises the following steps: the international leading-edge imaging technology is realized on a cross-scale multimode imaging sub-platform, and can selectively carry out imaging or parameter detection of multiple scales;

fourthly: after various imaging is realized, three-dimensional reconstruction can be carried out, imaging targets are reflected from all angles, and information contained in imaging results is richer.

Drawings

FIG. 1 is a flow chart and a platform structure diagram of an ultrasonic cross-scale and multi-parameter detection imaging method of the present invention;

FIG. 2 is a user control interface for ultrasound signal acquisition and cross-scale multi-modal imaging in accordance with the present invention;

FIG. 3 is a user control interface for an artificial intelligence plane wave imaging technique according to the present invention;

FIG. 4 is a super-resolution imaging user control interface in accordance with the present invention;

FIG. 5 is a user control interface for multi-mechanical parameter imaging in accordance with the present invention;

FIG. 6 is a user control interface for microbubble subharmonic blood pressure imaging in accordance with the present invention;

FIG. 7 is a passive acoustic imaging user control interface in accordance with the present invention;

FIG. 8 is a schematic structural diagram of a three-dimensional ultrasound image reconstruction and visualization system according to the present invention;

FIG. 9 is a user control interface of the three-dimensional ultrasound image reconstruction and visualization system of the present invention;

FIG. 10 is a user control interface for the display and storage sub-platform of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The invention is described in further detail below with reference to the accompanying drawings:

an imaging method of ultrasonic cross-scale and multi-parameter detection comprises the following steps,

step 1, freely setting acquisition parameters according to requirements so as to carry out cross-scale ultrasonic imaging. According to the specific requirements of cross-scale imaging or parameter detection, waveform editing, emission sequence setting, emission acquisition parameter adjustment, imaging mode selection and data type setting are carried out for imaging, and channel data which are not subjected to beam forming, ultrasonic images and video data are obtained.

And 2, performing beam synthesis by using a platform hardware beam synthesis method or a user-defined beam synthesis method and using channel data which is obtained by the platform and is not subjected to beam synthesis to obtain radio frequency data.

And 2, based on the channel data, the ultrasonic images, the video data and the radio frequency data which are obtained in the step 1 and are not subjected to beam forming, the compatibility of the platform to clinical ultrasonic equipment is ensured.

Wherein, by comparing the ultrasound image and the video data obtained in the step 1 with the ultrasound image of the radio frequency data obtained by the beam forming in the step 2, the condition of the image quality improvement can be analyzed through the comparison, thereby researching the beam forming method. The beamformed radio frequency data may be used for subsequent processing.

Step 3, specifically comprising the following steps:

passive acoustic imaging: selecting a frame number from a time angle by utilizing channel data obtained by the platform, determining an imaging region and an imaging mode, setting corresponding imaging parameters, realizing passive acoustic imaging and monitoring a high-intensity focused ultrasound thermal damage process;

② preferably, the artificial intelligence plane wave imaging technology is used for high frame rate plane wave imaging: the radio frequency data obtained in the step 2 is learned by a deep learning method (also an artificial intelligent plane wave imaging technology) of a convolutional neural network by taking a high-quality ultrasonic image compounded from multiple angles as a target, so that the image quality is improved, the artificial intelligent plane wave imaging with high frame rate and high signal-to-noise ratio is realized, and the defect that the frame rate is reduced due to excessive compound angles in the plane wave imaging is overcome;

and thirdly, simultaneously, aiming at the artificial intelligent plane wave imaging with high frame rate and high signal to noise ratio obtained in the second step or directly aiming at the radio frequency data obtained in the step 2, obtaining the cross-scale ultrasonic structural image, comprising the following steps: extracting signals of ultrasonic contrast agent micro-bubbles or nano-droplets and connecting the signals into a track through a series of steps of space-time filtering, micro-bubble positioning, micro-bubble tracking and multi-frame compounding to realize super-resolution imaging and hemodynamic parameter estimation; multi-mechanical parameter imaging of detecting the tissue mechanical properties such as viscoelasticity and fluidity is realized through micro displacement estimation, shear wave velocity estimation and viscoelasticity fitting; the subharmonic blood pressure imaging is realized by sensing and selecting the region, extracting the subharmonic amplitude and estimating the pressure by subharmonic, and further the pressure detection and imaging of the blood vessel or the cavity are realized.

And 4, acquiring ultrasonic data by a robot space calibration and planned path data acquisition technology, realizing three-dimensional reconstruction and visualization by applying a three-dimensional difference value reconstruction and voxel data three-dimensional visualization technology, further acquiring an ultrasonic three-dimensional image based on the ultrasonic image and the video data acquired in the step 1, the passive acoustic imaging acquired in the step I or the cross-scale ultrasonic structure image acquired in the step III, and displaying or storing the three-dimensional image.

And 5, for the image obtained by each imaging method, the passive acoustic imaging obtained in the step 3, the super-resolution imaging and the hemodynamic parameter estimation obtained in the step three, the super-multi-mechanical parameter imaging obtained in the step three and the subharmonic blood pressure imaging (namely, a cross-scale ultrasonic structural image) obtained in the step three can be used for obtaining the optimal image by adjusting the display parameters including the dynamic range and the size of the imaging area, and further performing image display or image picture storage on the obtained optimal image. Or directly store the image data for further study.

An imaging platform for ultrasonic cross-scale and multi-parameter detection comprises an ultrasonic signal acquisition control sub-platform, a cross-scale multi-mode imaging sub-platform, a three-dimensional reconstruction sub-platform and a display and storage sub-platform as shown in figure 1, wherein each part is as follows:

1. ultrasonic signal acquisition control sub-platform

The ultrasonic signal acquisition control sub-platform controls the acquisition of ultrasonic signals in a parameter transmission mode, sets an emission sequence, adjusts emission acquisition parameters, selects an imaging mode and sets a data type to complete a set acquisition task in five steps.

The waveform editing is divided into plane wave waveform editing and focused wave waveform editing. And editing the plane wave and the focusing wave respectively, and determining the editing of the plane wave or the focusing wave by selecting a waveform mode option in the waveform panel. Through the waveform option, different waveforms are selected and set, such as: square waves, sine waves, chirps, and the like. Further, parameters required for the transmission waveform such as transmission frequency, the number of cycles, and the like are set respectively according to the selected waveform.

And setting sequence modes in the transmitting sequence as a plane wave mode, a focused wave and a plane wave alternate transmitting mode respectively. And in the plane wave mode, the emission acquisition parameter part only has adjustable plane wave emission acquisition parameters. In the focused wave mode, only the emission and acquisition parameters of the focused wave are adjustable. In the alternating transmitting mode of the plane wave and the condensed wave, the parameters of the plane wave and the focused wave are adjustable.

Different emission acquisition parameters are set for different sequence modes. The emission and collection parameters are divided into two modes of plane waves and focused waves. The plane wave acquisition controllable parameters comprise angle number, maximum angle, acquisition frame number, frame rate and voltage; the focus range, focus depth, collection frame number, pulse repetition frequency and voltage are parameters which can be controlled by the focusing wave.

The imaging mode can be selected from two modes of transmitting and receiving only, and in the case of ultrasonic waves generated from the outside, the receiving only mode is selected for imaging.

The stored ultrasonic data format is selected by setting the data type, and whether the channel data, the radio frequency data, the ultrasonic image and the audio and video data are subjected to beam forming or not is selectable.

The ultrasonic image is displayed in the image area in real time and is used for monitoring whether the ultrasonic signal acquisition process is terminated accidentally. The status bar is used to record the operation performed and error information. Meanwhile, entrances to various imaging methods are provided.

2. Cross-scale multimode imaging sub-platform

And constructing a cross-scale multi-mode imaging sub-platform on the basis of the ultrasonic signal acquisition control sub-platform, and performing imaging of various scales and modes. The artificial intelligence plane wave imaging technology is used for obtaining ultrasonic plane wave imaging with high frame rate and high signal-to-noise ratio as the basis of further imaging. Aiming at the obtained ultrasonic plane wave imaging with high frame rate and high signal-to-noise ratio, the spatial-temporal filtering processing, the microbubble positioning, the microbubble tracking and the multi-frame compounding are completed, the signals of ultrasonic contrast agent microbubbles or nanometer droplets are extracted, the operations of nanometer droplet or microbubble signal positioning and tracking, tracking track drawing, multi-frame compounding and the like are realized, the operations are connected into tracks, the super-resolution imaging and the hemodynamic parameter estimation are realized, and the tissue or transcranial micro vascular network is obtained; the multi-mechanical parameter imaging is realized by detecting shear waves, performing micro displacement estimation, shear wave speed estimation and viscoelastic fitting, and detecting the mechanical characteristics of tissues; the microbubble subharmonic blood pressure imaging is realized by detecting the nonlinear vibration characteristic of microbubbles, extracting generated subharmonic signals (by sensing and taking region selection, extracting subharmonic amplitude and subnoro wave to estimate pressure), and realizing the subharmonic blood pressure imaging and the noninvasive pressure detection. And (3) the passive acoustic imaging utilizes the channel data which is not subjected to beam synthesis and is obtained in the step (1), beam synthesis is carried out on the channel data according to the selection of the frame number, and related signal components are selectively amplified, so that the monitoring of the ultrasonic treatment process is realized. As shown in fig. 2, buttons for various imaging methods are provided on a user control interface for ultrasound signal acquisition, and a study mode or an application mode can be selected. The research mode cares about the imaging details, and each link in the imaging is analyzed; the application mode directly results in the final image.

The platform structure and the method flow of various imaging technologies are shown in fig. 1, and the trans-scale multimode imaging sub-platform utilizes the data generated by the ultrasonic signal acquisition control sub-platform to perform imaging of various scales, so as to realize the corresponding imaging technology.

The platform has an artificial intelligence plane wave imaging technology, a user control interface is shown in figure 3, a radio frequency data result is processed, an acquired image of ultrasonic plane wave imaging with a high frame rate and a high signal-to-noise ratio is guaranteed, and meanwhile, an image with parameters such as the signal-to-noise ratio and the contrast ratio close to multiple compound angles is obtained. Black and white images obtained by the radio frequency data are used as a data set for training a deep learning algorithm to obtain a deep learning model, and finally, any radio frequency data are input into the deep learning model to obtain corresponding high-quality images. And entering an IQ demodulation link after the radio frequency data under different emission angles are obtained. This link uses Hilbert transform to perform IQ demodulation to remove signal carriers, and extracts the organization structure information contained in the signal, thereby obtaining a baseband signal. Then, the intensity of the baseband signal is obtained, and the gray level of the baseband signal is logarithmically compressed to a range which can be adapted by human eyes, so that plane wave ultrasonic images of different angles are obtained. And processing all the data to obtain a data set, and dividing the obtained data into three different data sets, namely a training set, a verification set and a test set. In the training process, the radio frequency data is used as the training input of the deep learning model, and the multi-composite angle plane wave image is used as the learning target of the deep learning model. Therefore, a deep learning model is obtained through training, the model can realize the input of plane wave images with a small number of angles, the output result image can achieve the image quality which can be obtained only by compounding a plurality of angle plane waves, and the high space-time resolution of plane wave imaging is realized. In addition, a feedback system is added in the iterative process of network training to solve the over-fitting problem and help model convergence. The steps of the method are open and updatable, and different models can be selected by the deep learning algorithm, so that the expandability of the future image processing algorithm is facilitated. And the depth learning model has universality, the input image can be an image from other scales, such as the image of blood flow. In clinical application, the technology can meet the requirements of many ultrahigh-speed imaging applications with higher time-space resolution requirements.

The images obtained by the ultrasonic signal acquisition control sub-platform and the artificial intelligent plane wave imaging technology can be used for super-resolution imaging, multi-mechanical parameter imaging and microbubble subharmonic blood pressure imaging.

The tissue or transcranial super-resolution imaging is realized by positioning and tracking the ultrasonic contrast agent micro-bubble or nano-droplet. The user control interface is as shown in fig. 4, a low frequency transducer is used for emitting a chirp signal to perform transcranial imaging or a high frequency probe is used for receiving a signal of liquid drop phase change so as to perform monitoring imaging. The method can also use the high-quality image obtained by the artificial intelligent plane wave imaging technology to process the data so as to obtain the super-resolution image, and the data processing process of the imaging method is divided into 4 links, namely space-time filtering, microbubble positioning, microbubble tracking and multi-frame compounding. After the operation of some links is finished, corresponding data can be stored for independent analysis, and the data processing of some links is improved in a targeted manner. The space-time filtering link adopts a space-time filtering method based on singular value decomposition, tissue signals with different proportions are filtered out by modifying a threshold value, blood flow signals are reserved, a common black-and-white B-mode ultrasonic image with the tissue signals removed is drawn and filtered, and then image data after the space-time filtering can be stored. The microbubble or nano-droplet positioning adopts a peak positioning method, and the image is preprocessed through a positioning threshold value to filter noise signals with lower intensity. And then, a local peak value is found by traversing the image, so that the microbubble or nano-droplet signal is converted into a value of one pixel point, a positioning result is stored, and the positioning of each point comprises coordinate information and intensity information of the point. And tracking the microbubbles or the nano-droplets obtained by positioning, realizing tracking through Kalman filtering and probability calculation by adopting a Markov data related method, drawing a tracking track, and selecting a certain number of frames for multi-frame compounding after tracking for multiple times. Meanwhile, the obtained images comprise a common black-and-white B ultrasonic image, a common black-and-white B ultrasonic image with tissue signals filtered, a blood vessel track image obtained by one-time tracking and a blood vessel track obtained by compounding multiple frames. The designer can update the functions, open the settings of all imaging links and update the algorithm according to the requirements. During later use, the previous method or the new method after updating can be selectively used. For example, for transcranial situations, a phase correction element is added. If pulse inversion or other imaging techniques are used, a method of selecting adjacent frame images for addition to filter out tissue signals may be performed. The microbubble location can adopt centroid location or Gaussian fitting location according to a calculated point spread function. Microbubble tracking can update the Markov data related algorithm, add different limiting conditions, or directly adopt other tracking algorithms.

And (3) imaging and detecting the tissue mechanical characteristics by using the multi-mechanical parameters. As shown in fig. 5, the data processing process of the imaging technique is divided into 3 links, namely micro displacement estimation, shear wave velocity estimation and viscoelastic fitting. In the micro displacement estimation step, a method based on cross-correlation operation is adopted, the tissue displacement information is obtained by performing the cross-correlation operation on different frames, and the estimated displacement data can be stored. The velocity estimation adopts a phase time delay method, and the phase change trend solving velocity of the shear wave on a certain scanning line is obtained by carrying out phase preprocessing on the signal of the scanning line. The viscoelastic fitting is to use the shear wave velocity estimation result and the corresponding frequency as a data source, and to use a mechanical model to perform least square fitting to obtain the viscoelasticity. The imaging method can perform transcranial brain tissue elasticity imaging to obtain a transcranial brain tissue elasticity image of a large animal or a human.

The microbubble subharmonic blood pressure image is used for processing the data so as to obtain a pressure distribution map of the region of interest and the average pressure of the region of interest. The user control interface is shown in fig. 6, and the data processing process is divided into 3 links, namely region-of-interest selection, subharmonic amplitude extraction and subharmonic pressure estimation. The region of interest is selected by utilizing a capture function on the B-ultrasonic image, and the position is marked, so that the B-ultrasonic image marked with the region of interest and the B-ultrasonic image only with the region of interest can be saved. And carrying out Fourier transform on the region of interest by selecting a segmentation method or a step-by-step method, and extracting the sub-harmonic amplitude. And then, extracting and averaging the subharmonic amplitudes of all the frames to obtain a subharmonic distribution map of the region of interest, wherein the image can be stored at the moment. Inputting parameters in the linear relation between the subharmonic and the pressure, estimating the pressure to obtain a pressure distribution diagram of the region of interest and the average pressure of the region, and storing the pressure distribution diagram, the parameters in the linear relation between the subharmonic pressure and the average pressure. The platform can measure intracranial vascular pressure to obtain intracranial vascular pressure of animals or human, and the intracranial vascular pressure directly reflects the state of cerebral vessels in real time, so that a new method is provided for detecting cerebrovascular diseases.

Passive acoustic imaging may enable monitoring of the ultrasound treatment process. The sub-platform user control interface is shown in fig. 7, the tissue treated by high-intensity focused ultrasound irradiation is used as an imaging target, the linear array transducer of the sub-platform is controlled by using ultrasound signal acquisition to passively receive ultrasound radio-frequency signals in the ultrasound irradiation process, and after data acquisition is completed, beam forming is performed on the radio-frequency data to obtain a passive acoustic imaging result of an imaging area. And then, selecting an imaging mode, imaging parameters and an imaging area to obtain a passive acoustic image. After the frame number of single-frame passive acoustic imaging and the range of the multi-frame superimposed passive acoustic imaging frame number are selected, firstly, an imaging algorithm is selected according to the actual ultrasonic irradiation pulse length and the imaging requirement, a beam synthesis algorithm under a non-time sequence synchronous passive acoustic imaging technology is selected for a long pulse, and a beam synthesis algorithm under a time sequence synchronous passive cavitation imaging technology is selected for a short pulse; setting corresponding parameters according to the actual signal transmitting and collecting process; and setting an imaging area and a pixel size so as to perform passive acoustic imaging. And in the passive acoustic imaging process, a time delay superposition beam synthesis algorithm based on time sequence synchronization or a time delay superposition integral beam synthesis algorithm based on non-time sequence synchronization is used, and a passive acoustic imaging result is drawn. Different algorithms can be developed by designers in the research process, so that different beam forming algorithms can be added in the beam forming algorithm selection step to meet different imaging requirements.

3. Three-dimensional reconstruction sub-platform

As shown in fig. 8, the free-scanning three-dimensional reconstruction and visualization sub-platform includes a six-degree-of-freedom robot module, a level conversion module, a dc regulated power supply module, an arbitrary waveform generation module, a pressure monitoring module, and an industrial control computer module. The six-degree-of-freedom robot is connected with the industrial control computer through an RJ45 network cable, and is connected with the input end of the level conversion module through a BNC cable; the output end of the level conversion module is connected with a direct current stabilized voltage power supply, and the output end of the level conversion module is connected with an external trigger interface of the arbitrary waveform generator; the output port of the arbitrary waveform generator is connected with the external trigger port of the ultrasonic signal transmitting and receiving control sub-platform; the ultrasonic transmitting and receiving platform is connected with an industrial control computer through Bluetooth. The main operation flow is as follows:

1) and (3) calibration operation: the calibration operation is a necessary link of the free scanning ultrasonic three-dimensional reconstruction, and comprises time calibration and space calibration. The purpose of time calibration is to ensure the simultaneity between the position information acquired by the robot and the image information acquired by the ultrasonic diagnostic apparatus. The purpose of spatial calibration is to obtain the image coordinates in the base coordinate system. And selecting a two-dimensional imaging probe of the ultrasonic transmitting and receiving control sub-platform to scan an imaging target in space, and simultaneously recording the space position of a scanned image and then performing three-dimensional reconstruction.

2) Data acquisition: the data acquisition includes position acquisition and image acquisition. The specific operation is as follows: firstly, a B-ultrasonic probe is additionally held at a flange disc end of a robot, the robot is controlled to move to an initial position of sample scanning in a manual mode, the posture of the robot is adjusted according to imaging quality, the posture of the robot with the best scanning effect is obtained, current posture data of the flange disc of the robot is sent to a PC end through a RAPID sending program of the robot end, then a proper path is selected to move the robot in the manual mode, the sample can be scanned all the time in the path, a pressure monitoring device monitors the pressure between the robot and the sample within a certain range, and otherwise the posture of the robot is adjusted. And then the robot moves to a teaching position at a specified speed, after the robot reaches a path point, the IO port of the robot controller sends a trigger signal do1, the ultrasonic diagnostic apparatus is driven to acquire an ultrasonic image of the path point after voltage conversion, and pose information and corresponding image data are continuously acquired in the scanning process.

3) Three-dimensional interpolation and reconstruction: after obtaining the flange plate attitude data of the path point and the ultrasonic image of the corresponding position, calculating a transfer matrix (a B) M _ r of a flange plate T coordinate system of the point to a robot base coordinate system B according to the flange plate attitude data, simultaneously multiplying (a T) M _ S rightwards to obtain a coordinate transfer matrix (a B) M (S _ i) of each image coordinate system to the robot base coordinate system B, then selecting a proper three-dimensional reconstruction algorithm, and selecting reconstruction precision and reconstruction step length, thereby determining the voxel value of each point in a regular voxel sequence and completing three-dimensional reconstruction.

4) Three-dimensional visualization: after reconstruction is completed, regular sequence images obtained after reconstruction are stored, the sequence images are used for opening three-dimensional visualization software developed by people, and three-dimensional visualization is carried out after image segmentation, filtering and other processing. There are three basic methods of visualization: slice projection, surface rendering, and volume rendering. And obtaining a visualized image by selecting different methods, carrying out operations such as region selection, measurement and the like on the visualized image, and measuring the size and the volume of the target. Meanwhile, the images obtained by other imaging modes such as MRI or CT and the like are compared and fused. And verifying the accuracy of the ultrasonic reconstruction result.

The three-dimensional reconstruction result is shown in fig. 9, the three-dimensional reconstruction and visualization system integrates the sequence data of the three-dimensional scanning image and the corresponding pose coordinate data, the software obtains the voxel after three-dimensional reconstruction, selects an image sequence storage path, can lead the voxel data out of the sequence image along the Z-axis direction, and then utilizes the visualization software to perform visualization display, so that the multi-plane cross projection result, the volume rendering and surface rendering result can be obtained, and the interaction functions such as rotation, magnification and reduction can be performed.

4. Display and storage sub-platform

The display and storage sub-platform can display the original B-mode ultrasonic image, the cross-scale and the final result of multi-parameter imaging. The method specifically comprises the following steps: the artificial intelligent plane wave imaging can display a high-space-time resolution plane wave image processed by a network model; the super-resolution micro blood vessel imaging can display high and low frequency super-resolution micro blood vessel images; the passive acoustic imaging can be used for setting image linear display or logarithmic display, selecting a logarithmic dynamic range, and selecting image pseudo color or gray scale to display a passive acoustic image. The multi-mechanical parameter imaging can dynamically display tissue elasticity images, the subharmonic blood pressure imaging can display the pressure distribution map of the region of interest, and the three-dimensional reconstruction visualization technology can dynamically display three-dimensional structural images. After the images are adjusted on the user control interfaces of all the sub-platforms, the images are uniformly adjusted and saved on the display and storage sub-platform, as shown in fig. 10.

All images are of the same size and can be analyzed for structure and various parameters.

The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.