CN118000786A - Ultrasonic tomography method and system based on anatomical prior - Google Patents
Ultrasonic tomography method and system based on anatomical prior Download PDFInfo
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Abstract
An anatomical prior-based ultrasound tomography method and system, the method comprising: acquiring original ultrasonic parameters, reflected waves and transmitted wave signal data and storing the data; carrying out ultrasonic reflection imaging by utilizing the reflected wave data; processing the reflected image to obtain a tissue structure image; constructing an acoustic parameter image by utilizing the tissue structure image and the anatomical prior information; and taking the anatomical prior acoustic parameter image as an initial image, and carrying out ultrasonic tomography image reconstruction by using the transmission wave data. The invention extracts the tissue structure outline through the ultrasonic reflection image and constructs the initial acoustic parameter image, thereby greatly improving the accuracy and efficiency of ultrasonic tomography image reconstruction.
Description
Technical Field
The invention belongs to the ultrasonic imaging technology, and particularly relates to an ultrasonic tomography method and system.
Background
The ultrasonic tomography is a noninvasive, ionizing radiation-free and high-imaging-resolution medical imaging technology, and can reconstruct high-precision images of internal tissues of a human body, such as breasts and the like. The ultrasonic tomography system is a set of software and hardware system capable of realizing ultrasonic tomography technology, and the system is partially applied to scientific research and clinical medical treatment at present.
Such systems typically include an ultrasound front end that is excited and receives by the ultrasound signals and a host computer that runs the ultrasound tomography algorithm program. In the aspect of signal acquisition, an ultrasonic transducer array is excited by an electric signal to emit ultrasonic waves to tissues through a piezoelectric effect, and the ultrasonic transducer array receives the ultrasonic signals after being switched to a receiving mode. In the aspect of computed tomography, the method can be divided into two imaging modes, wherein one mode is to carry out reflection imaging by utilizing ultrasonic reflection signals, and the mode can reconstruct the acoustic reflection coefficient of tissues, also called echo intensity image reconstruction, has higher reconstruction resolution ratio on various tissue interfaces, is more sensitive to the focus with larger change of acoustic impedance parameters and obvious boundary, and is insensitive to the change of tissue function parameters in early stage of the lesion; the other mode mainly utilizes ultrasonic transmission signals, including a direct ray method based on a ray theory, a bending ray method and a full waveform inversion (Full waveform inversion, FWI) method based on a wave theory, and can reconstruct a distribution image of acoustic parameters such as sound velocity, attenuation coefficient and the like of tissues, so that the mode has important clinical significance for early cancer screening, but the iterative optimization reconstruction method has the problems of local extremum, large calculated amount, long calculation time and the like. The related technical scheme (publication number: CN 110179495B) can solve the problems of long data quantity and reconstruction time to a certain extent by utilizing the distributed cluster system scheme to process the ultrasonic signals, but cannot fundamentally reduce the complexity of the algorithm. When the iterative reconstruction method is used for reconstructing and calculating, an initial acoustic model is firstly required to be determined, the difference between a simulated received signal and an actual received signal of the model is calculated from the initial model, the parameter update gradient of the model is solved to update the model parameter, the steps are iterated for a plurality of times until the signal difference reaches or is smaller than a set threshold value, namely, the current numerical value calculation result image is considered to better approximate to a real tissue image, and the image of the current acoustic model is output as an ultrasonic tomography result.
In summary, there are two key problems in the present technology that need to be solved:
1) The initial iterative acoustic image calculation method is not general, and the dilemma that the iterative optimization algorithm falls into a local extremum can be effectively solved:
The prior art generally uses a uniform background model (water: 1540 m/s) as an initial model of iterative computation, and according to the prior knowledge of acoustic parameters of various tissues of a human body, the sound speeds of bones and skins are about 3200m/s and 1670m/s respectively. Therefore, the difference between the uniform background initial model and the real tissue is larger, on one hand, the initial model is easy to fall into a local extremum in the iterative optimization calculation process, so that the calculation result deviates from a global minimum value, and a desired high-quality image is difficult to obtain; on the other hand, this will result in a large number of iterations of the calculation, and excessively long imaging times, limiting the practical application of the technique.
2) The computational grid intensive of the existing iterative optimization algorithm results in an increase in computational load:
In order to ensure imaging quality and convergence, the existing acoustic parameter iterative optimization reconstruction algorithm is generally dense in computational grid, and cannot effectively acquire an accurate tissue outline structure, so that a computational domain of the existing acoustic parameter iterative optimization reconstruction algorithm can only take a larger range to ensure that the whole tissue is contained, and the existing acoustic parameter iterative optimization reconstruction algorithm clearly puts higher requirements on computational power and resources, and limits application and popularization of the technology.
Disclosure of Invention
The present invention overcomes the above-identified shortcomings of the prior art by providing an anatomical prior-based ultrasound tomography method and system.
Aiming at the initial model, the invention acquires the anatomical morphology information of the tissue by carrying out ultrasonic reflection imaging on the tissue and substitutes the anatomical priori acoustic parameters of the tissue, so that a more accurate initial model can be obtained, and the algorithm is accelerated to converge so as to reduce the calculation iteration times; aiming at the selection of the calculation domain, the accurate tissue outline is obtained through the reflection image, so that the calculation region can be limited in the tissue to the maximum extent, and the calculation cost is saved.
The specification provides an anatomical prior-based ultrasound tomography method, comprising the steps of:
step 1: acquiring ultrasonic array parameters, ultrasonic reflected wave signal data and ultrasonic transmitted wave signal data, and storing;
step 2: carrying out ultrasonic reflection imaging by utilizing ultrasonic reflection wave data to obtain an ultrasonic reflection image;
step 3: processing the ultrasonic reflection image to obtain a structural image of the target tissue;
step 4: constructing a priori initial acoustic parameter image by utilizing the structural image of the target tissue and anatomical priori acoustic information;
step 5: and carrying out ultrasonic tomography image reconstruction by using the prior initial acoustic parameter image and ultrasonic transmission wave signal data.
Preferably, in step 1, parameters of an ultrasonic array, ultrasonic reflected wave signal data, and ultrasonic transmitted wave signal data are obtained and stored, and the method specifically includes: the method comprises the steps of obtaining the position, the information source waveform and the sampling parameters of array elements of an ultrasonic array, and obtaining actual RF data or simulated RF data of ultrasonic reflected waves and actual RF data or simulated RF data of ultrasonic transmitted waves.
Further, the actual RF data or simulated RF data of the ultrasonic reflected wave are: the real RF data or the simulation RF data of the ultrasonic signals received by the array elements at the same side of the position of the ultrasonic excitation array elements relative to the imaging target;
the actual RF data or simulated RF data of the ultrasonic transmission wave are: the actual RF data or simulated RF data of the ultrasound signal received by the array element at the other side of the position of the ultrasound excited array element relative to the imaging target.
Further, acquiring actual RF data of the ultrasonic reflected wave and the ultrasonic transmitted wave specifically includes:
aiming at ultrasonic equipment, determining ultrasonic transducer array arrangement and ultrasonic excitation modes;
an ultrasonic array signal acquisition system built by an ultrasonic imaging platform acquires ultrasonic RF data;
the method for acquiring the simulation RF data of the ultrasonic reflected wave and the ultrasonic transmitted wave specifically comprises the following steps:
Setting a simulation sample model;
Constructing a simulation environment through a k-Wave tool box, and determining required configuration parameters, wherein the configuration parameters comprise: ultrasonic transducer parameters, simulation total time, ke Lang numbers, simulation boundary conditions, mesh subdivision parameters, spatial positions of each array element contained in the ultrasonic transducer parameters, excitation modes of the ultrasonic transducer, array element signals of the array elements and time sampling sequences of the array elements;
The simulated sample model is input into the constructed simulation environment, and ultrasonic RF data is calculated.
Preferably, the ultrasonic reflection imaging using ultrasonic reflection wave signal data in the step 2 specifically includes: and inputting the ultrasonic reflected wave signal data into an ultrasonic reflected imaging algorithm to reconstruct an ultrasonic reflected image, so as to obtain an ultrasonic reflected image.
Further, the ultrasonic reflection imaging algorithm is a time domain delay superposition algorithm or a frequency domain rapid reconstruction algorithm.
Preferably, the processing the ultrasound reflection image in step 3, to obtain a structural image of the target tissue specifically includes: firstly, carrying out normalization and noise reduction on an ultrasonic reflection image, inputting the processed ultrasonic reflection image into a structural image extraction algorithm, and calculating the outline of each part of the tissue by using a connected domain calculation method and a pixel intensity method.
Preferably, constructing the prior initial acoustic parameter image by using the structural image of the target tissue and the anatomical prior acoustic information in step 4 specifically includes: filling corresponding reference acoustic parameters into the region included by each part of the tissue outline according to the acoustic parameter type requirement of the tomographic image reconstruction, and obtaining an priori initial acoustic parameter image.
Further, the parameter types include sound velocity, sound attenuation and acoustic impedance, and each part of the tissue comprises skin, fat, muscle, blood vessel, gland and bone.
Preferably, the reconstructing an ultrasonic tomographic image by using the a priori initial acoustic parameter image and the ultrasonic transmission wave signal data in the step 5 specifically includes: and taking the priori initial acoustic parameter image as an initial model, and inputting the priori initial acoustic parameter image and ultrasonic transmission wave signal data into a selected ultrasonic transmission tomographic reconstruction algorithm for tomographic reconstruction.
Further, the transmission tomography algorithm is a ray method or a full waveform inversion algorithm.
The second aspect of the invention relates to an ultrasonic tomography system based on anatomy priori, which comprises a front end acquisition and an upper computer, wherein the front end acquisition is used for acquiring experimental data, and a program is stored in the upper computer, so that the ultrasonic tomography method based on anatomy priori of the invention is realized, and the method specifically comprises the following steps:
The data acquisition module is used for acquiring and storing ultrasonic array parameters, ultrasonic reflected wave signal data and ultrasonic transmitted wave signal data, and specifically comprises the following steps: acquiring the position, the information source waveform and the sampling parameters of array elements of an ultrasonic array, and acquiring actual RF data or simulated RF data of ultrasonic reflected waves and actual RF data or simulated RF data of ultrasonic transmitted waves;
The reflected image reconstruction module is used for selecting an ultrasonic reflected imaging algorithm and completing ultrasonic reflected image reconstruction by utilizing ultrasonic reflected wave signal data;
A structural image acquisition module for processing the ultrasonic reflection image to acquire structural image of the target tissue,
The priori initial acoustic parameter image construction module is used for constructing a priori initial acoustic parameter image by utilizing the structural image of the tissue and anatomical priori acoustic information;
and the tomographic image reconstruction module is used for selecting an ultrasonic tomographic imaging algorithm and completing ultrasonic tomographic image reconstruction by utilizing the priori initial acoustic parameter image and ultrasonic transmission wave signal data.
A third aspect of the invention relates to an anatomical a priori based ultrasound tomography apparatus comprising a memory and one or more processors, the memory having executable code stored therein, which when executed by the one or more processors, is adapted to carry out the anatomical a priori based ultrasound tomography method of the invention.
A fourth aspect of the invention relates to a computer readable storage medium having stored thereon a program which, when executed by a processor, implements the anatomical a priori based ultrasound tomography method of the invention.
In the ultrasonic tomography method based on anatomy priori, firstly, ultrasonic array parameters, original ultrasonic reflected wave signal data and ultrasonic transmitted wave signal data are acquired and stored, then ultrasonic reflected imaging is carried out by utilizing the reflected wave data, the reflected image is processed to acquire structural information of target tissues, then an priori initial acoustic parameter image is constructed by utilizing the structural information, and finally tomographic image reconstruction is carried out by utilizing the priori initial acoustic parameter image and the ultrasonic transmitted wave data. The invention extracts the tissue structure through the ultrasonic reflection image and constructs the initial acoustic parameter image, thereby greatly improving the accuracy and efficiency of ultrasonic tomography image reconstruction.
The invention has the advantages that: by means of the characteristic that the ultrasonic reflection imaging mode can be calculated efficiently, the invention provides high-resolution anatomy priori initial solution for ultrasonic transmission imaging, and creatively fuses two methods of ultrasonic reflection imaging and ultrasonic transmission imaging. Firstly, obtaining an accurate tissue contour structure through a reflection imaging mode result, and reducing a calculation area to the greatest extent, thereby saving calculation cost; and then, combining anatomical priori information, constructing a more accurate initial model by further utilizing a reflection imaging mode result, and reducing the iteration times of transmission imaging reconstruction, thereby shortening the imaging time. The combined advantages of the two can finally meet the practical application of the ultrasonic tomography technology.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification, illustrate and explain the exemplary embodiments of the present specification and their description, are not intended to limit the specification unduly. In the drawings:
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic illustration of the inventive method;
FIG. 3 is a schematic diagram of the system of the present invention;
Fig. 4a to 4d are schematic diagrams of imaging results in breast simulation according to the present invention, wherein fig. 4a is a schematic diagram of a digital model of breast simulation, fig. 4b is a schematic diagram of a reflection image reconstruction result, fig. 4c is a schematic diagram of a result of constructing a priori initial acoustic parameter image, and fig. 4d is a schematic diagram of an ultrasonic tomographic image reconstruction result;
Fig. 5 a-5 b are graphs comparing the performance of the method of the present invention with that of the conventional method, wherein fig. 5a is a graph comparing the mean error MSE, and fig. 5b is a graph comparing the structural similarity SSIM.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present specification more apparent, the technical solutions of the present specification will be clearly and completely described below with reference to specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present specification with reference to the accompanying drawings.
Example 1
As shown in fig. 1, the ultrasound tomography method based on anatomical prior provided in this embodiment includes the following steps:
S101: acquiring and storing original ultrasonic reflected wave and transmitted wave signal data; the original ultrasonic reflected wave and transmitted wave signal data can be experimental data acquired actually or simulation data obtained by simulation synthesis, and the stored original ultrasonic data comprises array distribution, rotation angle, excitation mode and RF data of ultrasonic reflected waves and transmitted waves acquired actually or obtained by simulation.
In this embodiment, an execution body for implementing a rapid ultrasound tomography method based on anatomical priors may be a device such as an upper computer, and for convenience of description, the description uses only the upper computer as an execution body for example, and describes a rapid ultrasound tomography method based on anatomical priors.
Wherein, the upper computer is internally provided with a Matlab application program, and related software packages and application programs of the ultrasonic imaging platform system.
In the actual acquisition process, parameters such as excitation frequency, excitation time sequence and the like are set to control an ultrasonic array signal acquisition system to acquire actual ultrasonic reflected wave and transmitted wave data by configuring related software packages and application programs of an ultrasonic imaging platform system in a Matlab environment. Or configuring a k-Wave tool box in a Matlab environment, and determining required configuration parameters to simulate and acquire ultrasonic reflected Wave signals and transmitted Wave signals, wherein the configuration parameters comprise: the method comprises the steps of ultrasonic transducer parameters, simulation total time, ke Lang numbers, simulation boundary conditions, mesh subdivision parameters, spatial positions of array elements contained in the ultrasonic transducer parameters, an excitation mode of the ultrasonic transducer, array element signals of the array elements and a time sampling sequence of the array elements.
In the signal acquisition process, all array elements receive signals simultaneously for each ultrasonic excitation. At the moment, relative to an imaging target, reflected wave signals are received by the array elements at the same side as the positions of the ultrasonic excitation array elements; the array element at the other side of the position of the ultrasonic excitation array element receives the transmitted wave signal. And then transmitting the reflected wave and the transmitted wave signals to an upper computer, and storing the ultrasonic reflected wave, the transmitted wave signals and the acquisition parameters in the upper computer, wherein for actual acquisition, the acquisition parameters are parameters such as array position information, excitation modes and the like, and for simulation, the acquisition parameters are simulation configuration parameters.
S102: inputting the ultrasonic reflected wave signal data into a selected ultrasonic reflected imaging algorithm for image reconstruction to obtain an ultrasonic reflected image; the input of the ultrasonic reflection image reconstruction is ultrasonic reflection wave signal data, and the output is an ultrasonic reflection image.
The reflected wave data is, unlike the transmitted wave data of S101: aiming at each ultrasonic excitation, relative to an imaging target, the data received by the array elements at the same side of the position of the ultrasonic excitation array element; the selected ultrasonic reflection imaging algorithm can be a reflection mode imaging algorithm such as a time domain delay superposition algorithm, a frequency domain rapid reconstruction algorithm and the like; ultrasound reflectance images are very effective for characterizing the boundaries of tissue.
S103: normalizing and denoising the ultrasonic reflection image, inputting the processed ultrasonic reflection image into a structure extraction algorithm, and calculating the outline of each part of the tissue; the purpose of normalization and noise reduction is to eliminate noise in the ultrasonic reflection image, and highlight the tissue main body to facilitate structure extraction; the structure extraction algorithm can use a connected domain algorithm to firstly acquire the outline of the tissue, separate the background from the tissue in the image, and then use methods such as a pixel intensity method to acquire the outline of the internal tissue.
S104: filling corresponding reference acoustic parameters into the region included by each part of the tissue outline according to the parameter type requirement of the tomography reconstruction, so as to obtain an priori initial acoustic parameter image; according to the requirements of the type of parameters of the tomographic reconstruction, the requirements refer to the acoustic parameters to be finally reconstructed in the ultrasonic tomographic reconstruction scheme, such as sound velocity, sound attenuation, density and the like;
Filling corresponding reference acoustic parameters into the regions included in the outline of each part of the tissue, namely, distinguishing the regions of different tissues such as skin layers, fat, muscles, glands and the like in the image according to the tissue structure and anatomical knowledge of human tissues obtained in the step S103, filling the reference acoustic parameters of each tissue into the different regions, and finally obtaining the priori initial acoustic parameter image.
S105: taking the prior initial sound velocity model as an initial model, and inputting the prior initial sound velocity model and ultrasonic transmission wave data into a selected ultrasonic transmission tomography algorithm for tomography; wherein, the initial model refers to an initial model used by the calculation of a tomography algorithm;
The transmitted wave data is different from the reflected wave data in S101, and is data received by an array element at the other side of the position of the transmitted wave data and the position of the ultrasonic excitation array element relative to the imaging target for each ultrasonic excitation;
The selected ultrasound transmission tomography algorithm can be a bending ray method, full waveform inversion algorithm, and the like. In particular, in performing tomographic calculation, the calculation region of the reconstruction algorithm should be limited to the inside of the tissue using the tissue outline information obtained in S103, and thus the calculation amount can be reduced while increasing the calculation accuracy.
According to the method, in the ultrasonic tomography process, the accurate tissue outline structure is obtained by or obtained ultrasonic reflected wave signals and reflected wave imaging, and by combining anatomical knowledge, a more accurate initial acoustic parameter image can be constructed as a priori, so that the iteration times are reduced, and the imaging time is shortened; meanwhile, the accurate tissue outline is obtained, so that the calculation area can be reduced to the greatest extent, and the calculation cost is saved.
Example 2
This embodiment provides an anatomical prior-based ultrasound tomography system for implementing the anatomical prior-based ultrasound tomography method of embodiment 1.
FIG. 2 is a schematic diagram of an anatomical prior-based ultrasound tomography system provided herein;
The ultrasound acquisition front end used in the ultrasound tomography system can use different transducer types, such as a ring array, a linear array, an area array, etc., and the figure only takes an ultrasound ring array as an example.
In the figure, the original ultrasonic reflected wave and transmitted wave data acquisition process is shown in the case of a ring array, each array element of the ring array is sequentially excited in the whole acquisition process, and when a certain array element in the ring array is excited, the reflected wave and the transmitted wave are respectively received and stored.
In the figure, reflected wave data will be used for reflected wave imaging, resulting in an ultrasound reflected image. On one hand, the reflected image can obtain a relatively accurate initial model through structure extraction and model construction; on the other hand, the outer contour may be extracted from the reflected image to limit the computational domain of the image.
In the figure, ultrasonic transmission wave data are used for transmission wave tomography, and a high-precision reconstruction result is finally obtained through calculation of a domain-limited and accurate initial model.
Fig. 3 is a block diagram of an anatomical prior-based ultrasound tomography system according to the present embodiment, where the system includes a front-end acquisition and an upper computer, the front-end acquisition is used for acquiring experimental data, and a program is stored in the upper computer, so as to implement the anatomical prior-based ultrasound tomography method. Comprising the following steps:
The data acquisition module is used for acquiring and storing ultrasonic array parameters, ultrasonic reflected wave signal data and ultrasonic transmitted wave signal data, and specifically comprises the following steps: acquiring the position, the information source waveform and the sampling parameters of array elements of an ultrasonic array, and acquiring actual RF data or simulated RF data of ultrasonic reflected waves and actual RF data or simulated RF data of ultrasonic transmitted waves;
The reflected image reconstruction module is used for selecting an ultrasonic reflected imaging algorithm and completing ultrasonic reflected image reconstruction by utilizing ultrasonic reflected wave signal data;
A structural image acquisition module for processing the ultrasonic reflection image to acquire structural image of the target tissue,
The priori initial acoustic parameter image construction module is used for constructing a priori initial acoustic parameter image by utilizing the structural image of the tissue and anatomical priori acoustic information;
and the tomographic image reconstruction module is used for selecting an ultrasonic tomographic imaging algorithm and completing ultrasonic tomographic image reconstruction by utilizing the priori initial acoustic parameter image and ultrasonic transmission wave signal data.
FIGS. 4 a-4 d are diagrams of imaging examples of breast simulations of the present embodiment, the digital model (FIG. 4 a) used in the simulation examples being a 100mm diameter breast acoustic model, wherein the background sound velocity (water), skin layer sound velocity, fat sound velocity, and gland sound velocity are about 1540m/s, 1650m/s, 1470m/s, and 1515m/s, respectively; obtaining an ultrasonic reflection image through reflection imaging (fig. 4 b); obtaining tissue structures such as tissue outline and skin layer by processing the reflection image, and filling anatomical priori acoustic information to construct a priori initial acoustic parameter image (figure 4 c); finally, the final ultrasonic tomography image reconstruction result is obtained by carrying out a tomography algorithm (fig. 4 d).
Fig. 5 a-5 b show performance comparison of the tomographic reconstruction results of the method of the present invention with the conventional method using a uniform background model (water: 1540 m/s) as an initial model for iterative computation in the simulation imaging example, and using a priori initial acoustic parameter images as shown in fig. 4 a. Fig. 5a is a graph showing the average error of the true value of the digital model used in the chromatographic reconstruction result and simulation of the method according to the present invention and the conventional method along with the iteration number, and it can be seen that the error of the chromatographic reconstruction result of the method according to the present invention is significantly lower than that of the conventional method, and the error convergence speed is faster along with the increase of the iteration number. Fig. 5b is a graph showing the structural similarity with the number of iterations, and it can be seen that the structural similarity of the reconstruction result of the method of the present invention is significantly higher than that of the conventional method, and the structural similarity increases faster with the number of iterations.
Example 3
This embodiment relates to a computer readable storage medium having stored thereon a program which, when executed by a processor, implements the anatomical a priori based ultrasound tomography method of embodiment 1.
Example 4
The present embodiment relates to an anatomical prior based ultrasound tomography apparatus comprising a memory and one or more processors, the memory having executable code stored therein, the one or more processors, when executing the executable code, for implementing the anatomical prior based ultrasound tomography method of embodiment 1.
At the hardware level, the device includes a processor, an internal bus, a network interface, a memory, and a non-volatile storage, although other hardware required by the service is possible. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to implement the method shown in fig. 1 described above. Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present invention, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.
Improvements to one technology can clearly distinguish between improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) and software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATE ARRAY, FPGA)) is an integrated circuit whose logic functions are determined by user programming of the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of kinds, such as ABEL（Advanced Boolean Expression Language）、AHDL（Altera Hardware Description Language）、Confluence、CUPL（Cornell University Programming Language）、HDCal、JHDL（Java Hardware Description Language）、Lava、Lola、MyHDL、PALASM、RHDL（Ruby Hardware Description Language）, and VHDL (Very-High-SPEED INTEGRATED Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.
Claims (14)
1. An anatomical prior-based ultrasound tomography method comprising the steps of:
step 1: acquiring ultrasonic array parameters, ultrasonic reflected wave signal data and ultrasonic transmitted wave signal data, and storing;
step 2: carrying out ultrasonic reflection imaging by utilizing ultrasonic reflection wave data to obtain an ultrasonic reflection image;
step 3: processing the ultrasonic reflection image to obtain a structural image of the target tissue;
step 4: constructing a priori initial acoustic parameter image by utilizing the structural image of the target tissue and anatomical priori acoustic information;
step 5: and carrying out ultrasonic tomography image reconstruction by using the prior initial acoustic parameter image and ultrasonic transmission wave signal data.
2. The anatomical prior-based ultrasound tomography method as recited in claim 1, wherein in step 1, ultrasound array parameters, ultrasound reflected wave signal data, ultrasound transmitted wave signal data are acquired and stored, and specifically comprising: the method comprises the steps of obtaining the position, the information source waveform and the sampling parameters of array elements of an ultrasonic array, and obtaining actual RF data or simulated RF data of ultrasonic reflected waves and actual RF data or simulated RF data of ultrasonic transmitted waves.
3. An anatomical a priori based ultrasound tomography method as defined in claim 2,
The actual RF data or simulated RF data of the ultrasonic reflected wave are: the real RF data or the simulation RF data of the ultrasonic signals received by the array elements at the same side of the position of the ultrasonic excitation array elements relative to the imaging target;
the actual RF data or simulated RF data of the ultrasonic transmission wave are: the actual RF data or simulated RF data of the ultrasound signal received by the array element at the other side of the position of the ultrasound excited array element relative to the imaging target.
4. An anatomical a priori based ultrasound tomography method as defined in claim 2,
The method for acquiring the actual RF data of the ultrasonic reflected wave and the ultrasonic transmitted wave specifically comprises the following steps:
aiming at ultrasonic equipment, determining ultrasonic transducer array arrangement and ultrasonic excitation modes;
an ultrasonic array signal acquisition system built by an ultrasonic imaging platform acquires ultrasonic RF data;
the method for acquiring the simulation RF data of the ultrasonic reflected wave and the ultrasonic transmitted wave specifically comprises the following steps:
Setting a simulation sample model;
Constructing a simulation environment through a k-Wave tool box, and determining required configuration parameters, wherein the configuration parameters comprise: ultrasonic transducer parameters, simulation total time, ke Lang numbers, simulation boundary conditions, mesh subdivision parameters, spatial positions of each array element contained in the ultrasonic transducer parameters, excitation modes of the ultrasonic transducer, array element signals of the array elements and time sampling sequences of the array elements;
The simulated sample model is input into the constructed simulation environment, and ultrasonic RF data is calculated.
5. The anatomical prior-based ultrasound tomography method as recited in claim 1, wherein the performing ultrasound reflection imaging using ultrasound reflection wave data in step 2, obtaining an ultrasound reflection image, comprises: and inputting the ultrasonic reflected wave signal data into an ultrasonic reflected imaging algorithm to reconstruct an ultrasonic reflected image, so as to obtain an ultrasonic reflected image.
6. An anatomical a priori based ultrasound tomography method as defined in claim 5,
The ultrasonic reflection imaging algorithm is a time domain delay superposition algorithm or a frequency domain rapid reconstruction algorithm.
7. The anatomical prior-based ultrasound tomography method as recited in claim 1, wherein the processing of the ultrasound reflectance image in step 3, obtaining a structural image of the target tissue, comprises: firstly, carrying out normalization and noise reduction on an ultrasonic reflection image, inputting the processed ultrasonic reflection image into a structural image extraction algorithm, and calculating the outline of each part of the tissue by using a connected domain calculation method and a pixel intensity method.
8. The method of claim 1, wherein the constructing the a priori initial acoustic parameter image using the structural image of the target tissue and the anatomical a priori acoustic information in step 4 specifically comprises: and according to the acoustic parameter type requirement of the tomographic image reconstruction, automatically filling corresponding reference acoustic parameters in the region included by each part of the tissue outline, and obtaining an priori initial acoustic parameter image.
9. An anatomical a priori based ultrasound tomography method as defined in claim 8, wherein said parameter types include sound velocity, sound attenuation, acoustic impedance, and tissue portions including skin, fat, muscle, blood vessels, glands, bones.
10. An anatomical prior based ultrasound tomography method as defined in claim 1, wherein the ultrasound tomography image reconstruction using prior initial acoustic parameter images and ultrasound transmission wave signal data comprises: and taking the priori initial acoustic parameter image as an initial image, and inputting the initial acoustic parameter image and ultrasonic transmission wave signal data into a selected ultrasonic transmission tomographic reconstruction algorithm for tomographic reconstruction.
11. An anatomical a priori based ultrasound tomography method as defined in claim 10, wherein said ultrasound transmission tomographic reconstruction algorithm is a ray method or a full waveform inversion algorithm.
12. An anatomical prior-based ultrasound tomography system, comprising a front-end acquisition and an upper computer, wherein the front-end acquisition is used for acquiring experimental data, and a program is stored in the upper computer to realize the anatomical prior-based ultrasound tomography method according to any one of claims 1-11, and the method specifically comprises the following steps:
The data acquisition module is used for acquiring and storing ultrasonic array parameters, ultrasonic reflected wave signal data and ultrasonic transmitted wave signal data, and specifically comprises the following steps: acquiring the position, the information source waveform and the sampling parameters of array elements of an ultrasonic array, and acquiring actual RF data or simulated RF data of ultrasonic reflected waves and actual RF data or simulated RF data of ultrasonic transmitted waves;
The reflected image reconstruction module is used for selecting an ultrasonic reflected imaging algorithm and completing ultrasonic reflected image reconstruction by utilizing ultrasonic reflected wave signal data;
A structural image acquisition module for processing the ultrasonic reflection image to acquire structural image of the target tissue,
The priori initial acoustic parameter image construction module is used for constructing a priori initial acoustic parameter image by utilizing the structural image of the tissue and anatomical priori acoustic information;
and the tomographic image reconstruction module is used for selecting an ultrasonic tomographic imaging algorithm and completing ultrasonic tomographic image reconstruction by utilizing the priori initial acoustic parameter image and ultrasonic transmission wave signal data.
13. An anatomical a priori based ultrasound tomography device comprising a memory and one or more processors, the memory having executable code stored therein, the one or more processors, when executing the executable code, for implementing the anatomical a priori based ultrasound tomography method of any of claims 1-11.
14. A computer readable storage medium, having stored thereon a program which, when executed by a processor, implements the anatomical a priori based ultrasound tomography method of any of claims 1-11.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118285846A (en) * | 2024-06-06 | 2024-07-05 | 之江实验室 | Universal ultrasonic CT virtual twin imaging method and system |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080275344A1 (en) * | 2007-05-04 | 2008-11-06 | Barbara Ann Karmanos Cancer Institute | Method and Apparatus for Categorizing Breast Density and Assessing Cancer Risk Utilizing Acoustic Parameters |
| US8837860B1 (en) * | 2014-03-28 | 2014-09-16 | Heartflow, Inc. | Systems and methods for data and model-driven image reconstruction and enhancement |
| US20160030000A1 (en) * | 2014-08-04 | 2016-02-04 | Delphinus Medical Technologies, Inc. | Ultrasound waveform tomography method and system |
| CN106199064A (en) * | 2016-08-23 | 2016-12-07 | 清华大学 | The iteration ultrasound tomography method of axial flow field imaging in pipeline |
| CN108567445A (en) * | 2017-03-10 | 2018-09-25 | 南京大学 | A kind of multi-modality imaging method based on twin-line array sonic transducer |
| US20180271479A1 (en) * | 2016-06-23 | 2018-09-27 | Hitachi, Ltd. | Ultrasonic Imaging Device and Ultrasonic Imaging Method Using Ultrasonic Imaging Device |
| CN108885786A (en) * | 2016-04-05 | 2018-11-23 | 皇家飞利浦有限公司 | medical image processing |
| CN109875606A (en) * | 2019-01-22 | 2019-06-14 | 北京航空航天大学 | The method of ultrasound computed tomography velocity of sound imaging based on priori catoptric imaging |
| US20200008779A1 (en) * | 2017-02-27 | 2020-01-09 | Eth Zurich | System and method for speed and attenuation reconstruction in ultrasound imaging |
| CN110706298A (en) * | 2019-09-10 | 2020-01-17 | 天津大学 | Regularization weighted least square transmission-reflection dual-mode ultrasonic imaging reconstruction method |
| US20210248791A1 (en) * | 2018-08-31 | 2021-08-12 | QT Imaging, Inc. | Application of machine learning to iterative and multimodality image reconstruction |
| US20210265042A1 (en) * | 2018-07-20 | 2021-08-26 | Koninklijke Philips N.V. | Ultrasound imaging by deep learning and associated devices, systems, and methods |
| CN113947662A (en) * | 2021-10-09 | 2022-01-18 | 山东大学 | Ultrasonic simulation method and system based on medical tomography |
| CN114494497A (en) * | 2022-01-28 | 2022-05-13 | 天津大学 | Ultrasonic tomography dynamic image reconstruction method based on Kalman filtering |
| CN115082585A (en) * | 2022-06-21 | 2022-09-20 | 北京银河方圆科技有限公司 | Medical image reconstruction method |
| CN116712038A (en) * | 2023-06-09 | 2023-09-08 | 南方医科大学 | Multispectral photoacoustic tomography system and multispectral photoacoustic tomography method based on spiral staggered sparse sampling |
| CN117237473A (en) * | 2023-10-16 | 2023-12-15 | 南昌大学 | Photoacoustic tomography sparse reconstruction method based on diffusion model |
| CN117481687A (en) * | 2023-11-02 | 2024-02-02 | 东华大学 | Neural network-based ultrasonic tomography system transducer position and delay correction method |
| CN117770870A (en) * | 2024-02-26 | 2024-03-29 | 之江实验室 | Ultrasonic imaging method and device based on double-linear-array ultrasonic field separation |
| CN117828957A (en) * | 2024-03-05 | 2024-04-05 | 之江实验室 | Ultrasonic elastic simulation method and device, storage medium and electronic equipment |
-
2024
- 2024-04-10 CN CN202410427379.8A patent/CN118000786B/en active Active
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080275344A1 (en) * | 2007-05-04 | 2008-11-06 | Barbara Ann Karmanos Cancer Institute | Method and Apparatus for Categorizing Breast Density and Assessing Cancer Risk Utilizing Acoustic Parameters |
| US8837860B1 (en) * | 2014-03-28 | 2014-09-16 | Heartflow, Inc. | Systems and methods for data and model-driven image reconstruction and enhancement |
| US20160030000A1 (en) * | 2014-08-04 | 2016-02-04 | Delphinus Medical Technologies, Inc. | Ultrasound waveform tomography method and system |
| CN108885786A (en) * | 2016-04-05 | 2018-11-23 | 皇家飞利浦有限公司 | medical image processing |
| US20180271479A1 (en) * | 2016-06-23 | 2018-09-27 | Hitachi, Ltd. | Ultrasonic Imaging Device and Ultrasonic Imaging Method Using Ultrasonic Imaging Device |
| CN106199064A (en) * | 2016-08-23 | 2016-12-07 | 清华大学 | The iteration ultrasound tomography method of axial flow field imaging in pipeline |
| US20200008779A1 (en) * | 2017-02-27 | 2020-01-09 | Eth Zurich | System and method for speed and attenuation reconstruction in ultrasound imaging |
| CN108567445A (en) * | 2017-03-10 | 2018-09-25 | 南京大学 | A kind of multi-modality imaging method based on twin-line array sonic transducer |
| US20210265042A1 (en) * | 2018-07-20 | 2021-08-26 | Koninklijke Philips N.V. | Ultrasound imaging by deep learning and associated devices, systems, and methods |
| US20210248791A1 (en) * | 2018-08-31 | 2021-08-12 | QT Imaging, Inc. | Application of machine learning to iterative and multimodality image reconstruction |
| CN109875606A (en) * | 2019-01-22 | 2019-06-14 | 北京航空航天大学 | The method of ultrasound computed tomography velocity of sound imaging based on priori catoptric imaging |
| CN110706298A (en) * | 2019-09-10 | 2020-01-17 | 天津大学 | Regularization weighted least square transmission-reflection dual-mode ultrasonic imaging reconstruction method |
| CN113947662A (en) * | 2021-10-09 | 2022-01-18 | 山东大学 | Ultrasonic simulation method and system based on medical tomography |
| CN114494497A (en) * | 2022-01-28 | 2022-05-13 | 天津大学 | Ultrasonic tomography dynamic image reconstruction method based on Kalman filtering |
| CN115082585A (en) * | 2022-06-21 | 2022-09-20 | 北京银河方圆科技有限公司 | Medical image reconstruction method |
| CN116712038A (en) * | 2023-06-09 | 2023-09-08 | 南方医科大学 | Multispectral photoacoustic tomography system and multispectral photoacoustic tomography method based on spiral staggered sparse sampling |
| CN117237473A (en) * | 2023-10-16 | 2023-12-15 | 南昌大学 | Photoacoustic tomography sparse reconstruction method based on diffusion model |
| CN117481687A (en) * | 2023-11-02 | 2024-02-02 | 东华大学 | Neural network-based ultrasonic tomography system transducer position and delay correction method |
| CN117770870A (en) * | 2024-02-26 | 2024-03-29 | 之江实验室 | Ultrasonic imaging method and device based on double-linear-array ultrasonic field separation |
| CN117828957A (en) * | 2024-03-05 | 2024-04-05 | 之江实验室 | Ultrasonic elastic simulation method and device, storage medium and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| 谢丹艳;井西利;任国朝;: "基于Bayes估计的稀疏数据CT重建算法研究", CT理论与应用研究, no. 04, 15 December 2008 (2008-12-15) * |
| 顾建飞;贾楠;苏明旭;蔡小舒;: "集成超声透射和反射层析成像的理论与实验研究", 高校化学工程学报, no. 05, 15 October 2018 (2018-10-15) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118285846A (en) * | 2024-06-06 | 2024-07-05 | 之江实验室 | Universal ultrasonic CT virtual twin imaging method and system |
| CN118285846B (en) * | 2024-06-06 | 2024-09-24 | 之江实验室 | Universal ultrasonic CT virtual twin imaging method and system |
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