WO2020140917A1 - 用于检测组织硬度的方法、设备及系统 - Google Patents

用于检测组织硬度的方法、设备及系统 Download PDF

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WO2020140917A1
WO2020140917A1 PCT/CN2019/130934 CN2019130934W WO2020140917A1 WO 2020140917 A1 WO2020140917 A1 WO 2020140917A1 CN 2019130934 W CN2019130934 W CN 2019130934W WO 2020140917 A1 WO2020140917 A1 WO 2020140917A1
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measured
depth
shear wave
hardness
tissue
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PCT/CN2019/130934
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English (en)
French (fr)
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许晓臣
邵金华
孙锦
段后利
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无锡海斯凯尔医学技术有限公司
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0833—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving detecting or locating foreign bodies or organic structures
    • A61B8/085—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving detecting or locating foreign bodies or organic structures for locating body or organic structures, e.g. tumours, calculi, blood vessels, nodules
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A61B8/4483—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device characterised by features of the ultrasound transducer
    • A61B8/4488—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device characterised by features of the ultrasound transducer the transducer being a phased array
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5207—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of raw data to produce diagnostic data, e.g. for generating an image
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5269—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving detection or reduction of artifacts

Definitions

  • the invention relates to the field of ultrasonic medical imaging, and in particular to a method, equipment and system for detecting tissue hardness.
  • Tissue elasticity is the biological tissue mechanical parameter most affected by physiological and pathological factors.
  • a large number of physiological and pathological changes in the human body are accompanied by changes in tissue elasticity.
  • the elasticity of tissue can be used as an important parameter to reflect the characteristics of biological tissue.
  • Japanese scholar Y. Yamakoshi and American scholar J. Ophir first proposed ultrasonic elastography technology, which takes elastic parameters such as tissue shear modulus, Young's modulus, stress and strain as imaging objects.
  • two-dimensional ultrasound elastography first generates a shear wave propagating in the tissue by means of acoustic radiation, and then collects echo data including the propagation information of the shear wave in the tissue through the ultra-high-speed ultrasound data acquisition system.
  • the deformation information of the tissue can be obtained in the following two ways to obtain the tissue deformation estimation data: one is based on the Doppler ultrasound image or ultrasound sequence image, and uses some image data-based displacement estimation
  • the algorithm obtains relevant tissue deformation information; the other is when the tissue is deformed, the radio frequency signals received by the ultrasonic transducer before and after the tissue deformation are obtained, and the radio frequency signals are directly processed to estimate the tissue deformation information.
  • a series of algorithms are used to obtain the velocity value of the shear wave when it propagates through different parts of the tissue, and then the hardness value of the different parts of the tissue is obtained according to the velocity value, so as to finally obtain the tissue Two-dimensional elastic hardness diagram of cut plane.
  • the invention provides a method, equipment and system for detecting tissue hardness, which are used to achieve accurate and efficient tissue hardness.
  • the first aspect of the present invention is to provide a method for detecting tissue hardness, comprising: acquiring motion parameters of shear waves propagating in a depth direction with time at a plurality of positions to be measured; according to the motion parameters, calculating Describe the hardness information at each depth of multiple locations to be measured.
  • Another aspect of the present invention is to provide an apparatus for detecting tissue hardness, including: an acquisition module for acquiring motion parameters of shear waves propagating in a depth direction with time at a plurality of positions to be measured; a calculation module for Based on the motion parameters, the hardness information at each depth of the plurality of positions to be measured is calculated.
  • Another aspect of the present invention is to provide a system for detecting tissue hardness, including: a transducer array, and the device as described above, the transducer array is connected to the device, the transducer The array corresponds to multiple locations to be measured.
  • the method, equipment and system for detecting the hardness of a tissue taken the tissue cut plane of each depth of the tissue to be measured as a unit, and the movement parameters of the shear wave propagating along the depth direction at multiple locations to be measured with time, Calculate the hardness information at each depth of multiple locations to be measured to obtain the tissue hardness.
  • the above scheme has a small amount of calculation, the results are accurate and real-time, and the tissue hardness can be obtained accurately and efficiently.
  • FIG. 1A is a schematic flowchart of a method for detecting tissue hardness according to Embodiment 1 of the present invention
  • FIG. 1B is a schematic flowchart of another method for detecting tissue hardness according to Embodiment 1 of the present invention.
  • FIG. 1C is a schematic flowchart of another method for detecting tissue hardness according to Embodiment 1 of the present invention.
  • FIG. 2 is a schematic flowchart of a method for detecting tissue hardness according to Embodiment 2 of the present invention
  • FIG. 3A is a schematic structural diagram of an apparatus for detecting tissue hardness according to Embodiment 3 of the present invention.
  • FIG. 3B is a schematic structural diagram of another apparatus for detecting tissue hardness according to Embodiment 3 of the present invention.
  • FIG. 3C is a schematic structural diagram of another apparatus for detecting tissue hardness according to Embodiment 3 of the present invention.
  • FIG. 4 is a schematic structural diagram of an apparatus for detecting tissue hardness according to Embodiment 4 of the present invention.
  • FIG. 1A is a schematic flowchart of a method for detecting tissue hardness according to Embodiment 1 of the present invention. As shown in FIG. 1A, this embodiment is illustrated by applying the method to a device for detecting tissue hardness. The method includes:
  • the motion parameters may include, but are not limited to: displacement data, strain data, angular velocity data, velocity data, acceleration data, and so on.
  • the execution subject of this embodiment may be a device for detecting tissue hardness, and the device may be provided in an elasticity detection device. Take the motion parameter as displacement data as an example, and combine the actual application scenarios to illustrate:
  • the process of acquiring tissue hardness it is necessary to excite shear waves in the tissue, for example, to excite the shear waves in the tissue by means of acoustic radiation, and then collect echo data including the propagation information of the shear waves in the tissue, and Deformation estimation data is obtained by using echo data, and the data includes displacement data of shear waves propagating along the depth direction at multiple positions to be measured over time. Based on the displacement data, the hardness information at each depth of each position to be measured is calculated to obtain the tissue hardness. In practical applications, the division of each depth of the tissue to be measured can be determined according to the actual required calculation accuracy, and this embodiment does not limit it here.
  • the method may further include:
  • the horizontal axis of the hardness graph is position information, and the vertical axis is depth information.
  • generating the hardness map according to the hardness information at each depth of the plurality of locations to be measured may specifically include:
  • two-dimensional median filtering is performed on the hardness values first, and then the corresponding elastic hardness map is generated, which can eliminate the influence of salt and pepper noise, thereby obtaining a more accurate and reliable elastic hardness map.
  • a corresponding elastic hardness map of the tissue to be measured can be generated.
  • the results of the two-dimensional slice elastic hardness map obtained by the hardness information obtained by this scheme are consistent with the phantom data, which can be well used in clinical applications.
  • FIG. 1B is a flowchart of another method for detecting tissue hardness provided by Embodiment 1 of the present invention.
  • Schematic diagram, based on the embodiment shown in FIG. 1A, 102 may include:
  • FIG. 1C is a schematic flowchart of another method for detecting tissue hardness according to Embodiment 1 of the present invention. Based on the implementation shown in FIG. 1B, 103 includes:
  • motion parameters sequentially generate a motion parameter-time two-dimensional data map corresponding to each depth, where the two-dimensional data map characterizes the propagation of the shear wave at the current depth at different times;
  • the motion parameter-time two-dimensional data map corresponding to each depth obtain the velocity information of the shear wave at the plurality of positions to be measured in each depth of the tissue to be measured.
  • the two-dimensional data graph represents the propagation of the shear wave at the current depth at different times. Still taking the motion parameter as displacement data as an example, the actual application scenario will be used as an example to illustrate:
  • the displacement data of multiple locations to be measured at the same depth of the tissue to be measured at different times are selected in turn, and the displacement data corresponding to each depth is sequentially selected , Generate a displacement-time two-dimensional data map corresponding to the depth, and obtain the velocity information based on the two-dimensional data map.
  • the speed information of the shear wave at the plurality of positions to be measured in each depth of the tissue to be measured can be quickly and accurately obtained, optionally, in the embodiment shown in FIG. 1C
  • the specifics may include:
  • N is a preset value
  • the reference point includes peaks and troughs of shear waves in the two-dimensional data map.
  • N may be a preset positive integer, for example, set to 7.
  • the motion parameter as displacement data combined with the actual scene for example: obtaining displacement data of all positions at the same depth at different times, and constructing a displacement-time two-dimensional data map.
  • a displacement-time two-dimensional data map For example, suppose that the tissue to be tested is The depth is divided into A depths, and finally A two-dimensional data maps can be obtained. According to each generated two-dimensional data map, with the position of the focus of the acoustic radiation force as the center, a certain area is selected, and the initial position to be measured is adaptively selected from the area, and the current position to be measured is used as the starting point.
  • the least squares linear fitting method is used to obtain the velocity value of the shear wave at the position to be measured at the same depth. After that, update the position to be measured. Specifically, the reference point adjacent to the position to be measured is used as the updated position to be measured. Using the updated position to be measured as the starting point, again select N references in the direction of shear wave propagation. Point, based on the current motion parameters of these reference points, the least squares linear fitting method is also used to obtain the shear wave velocity value at the position to be measured, and so on, until the shear wave velocity values at all positions at this depth are obtained Out.
  • the selected reference point can be specifically selected in the shear wave propagation direction to both sides of the current position to be measured, when the position to be measured is located at the edge of the detection area, then Select in the direction of shear wave propagation to the side of the current position to be measured.
  • the above process is performed for the motion parameters corresponding to each depth of the tissue to be measured, and finally the velocity information of the plurality of positions to be measured of the shear wave at each depth of the tissue to be measured is finally obtained.
  • the shear wave velocity values at all positions of the tissue to be measured can be accurately and reliably obtained, thereby improving the final hardness map. Accuracy and reliability.
  • Embodiment 1 of the present invention provides another method for detecting tissue hardness.
  • 108 may specifically include:
  • N reference points to both sides or one side in the shear wave propagation direction
  • the M adjacent reference points in the reference points are sequentially subjected to linear fitting based on least squares to obtain a corresponding set of fitted straight lines, M is less than N;
  • M and N are positive integers, for example, N is set to 7, and M is set to 5.
  • the motion parameter is still used as the displacement data, and in combination with the actual scene as an example: after generating a two-dimensional data map corresponding to each depth, first of all, based on the area to be measured selected by the focus of the acoustic radiation force, the current to be measured is adaptively determined Position, taking the current position to be measured as the starting point, select the 7 most-valued coordinates to both sides or one side in the shear wave propagation direction, that is, determine 7 reference points, and select 5 adjacent points in turn Carry out linear fitting based on least squares, and finally select the fitting line with the smallest residual error to these 7 reference points as the final target fitting line, and obtain the shear wave velocity at the position to be measured according to the target fitting line value.
  • next calculated position to be measured is continuously updated based on the reference point adjacent to the current position to be measured, and based on the updated position to be measured, the above steps are repeated until the shear wave velocity of all positions at the current depth The values are all calculated.
  • reference points are selected according to the current position to be measured, and the linear fitting of the least squares based on these reference points is used to accurately obtain the velocity value of the current position of the shear wave at the current depth, and finally the generated The accuracy and reliability of the elastic hardness chart.
  • the hardness value of the tissue to be measured can be calculated by various methods, which is not limited in this embodiment.
  • 104 may specifically include:
  • the hardness information is calculated.
  • the first formula is used to calculate the hardness values at all positions of each depth.
  • the hardness values of all positions of the tissue to be measured can be accurately and quickly obtained, so that the corresponding elastic hardness map can be accurately and quickly obtained.
  • the method for detecting tissue hardness takes the tissue cut plane of each depth of the tissue to be measured as a unit, and calculates multiple The hardness information of the position to be measured at each depth is used to obtain the tissue hardness.
  • the calculation amount of the above scheme is small, the result is accurate and real-time, and the tissue hardness can be obtained accurately and efficiently.
  • FIG. 2 is a schematic flowchart of a method for detecting tissue hardness according to Embodiment 2 of the present invention. As shown in FIG. 2, this embodiment is still illustrated by applying the method to a device for detecting tissue hardness. Based on FIG. 1C and any embodiment based on the embodiment shown in FIG. 1C, before 105, further includes:
  • the motion parameters of the shear wave at all positions and all positions of the tissue under test at each time are subjected to direction filtering, and then the shear wave at all positions of each depth of the tissue under test at different times
  • the area to be measured is selected with the position of the focus of the acoustic radiation force as the center, and the current to-be-measured is adaptively selected from the area to be measured
  • the shear wave velocity values of the current position to be measured are obtained first, and then the shear wave velocity values of all positions at the current depth are obtained by updating and calculating the position to be measured.
  • the two-dimensional data map can be optimized first.
  • the two-dimensional data map can be optimized first.
  • it may further include:
  • the two-dimensional data graph is band-pass filtered on the time axis and linearly interpolated.
  • the two-dimensional data map after generating a two-dimensional data map according to the motion parameters corresponding to a certain depth, the two-dimensional data map can be first band-filtered and linearly interpolated. Then, referring to the aforementioned method, the shear wave velocity values of all positions at the current depth are obtained based on the two-dimensional data map after band-pass filtering and linear interpolation. Furthermore, the above operations are performed on the remaining data to be processed corresponding to each of the other depths, and finally the speed values of all positions at all depths of the tissue to be measured are finally obtained, so as to obtain the hardness values of all positions of all depths of the tissue to be measured.
  • the above optimized implementations can be implemented independently or in combination. For example, after directional filtering is performed on the motion parameters at each moment in the deformation estimation data, the two are generated according to the motion parameters corresponding to each depth at different moments. Dimensional data graph, and then perform band-pass filtering and linear interpolation on the two-dimensional data graph in sequence.
  • 201 may specifically include:
  • the motion parameters of the shear wave at all positions and depths of the tissue under test at each moment are converted from the time domain to the frequency domain;
  • the motion parameters of the shear wave at all depths and positions of the tissue to be measured are divided into two parts at each moment after conversion, and the frequency domain data of each part is divided into four quadrants.
  • the data of the quadrant corresponding to the noise to be filtered is assigned 0, and the data of other quadrants is unchanged;
  • the motion parameters of the shear wave at all positions and depths of the tissue under test at each moment are converted from the frequency domain to the time domain.
  • the fast Fourier transform method is used to convert the motion parameters from the time domain to the frequency domain, and then the focus of the acoustic radiation force is The position is the axis, and the motion parameters after the fast Fourier transform are divided into left and right parts.
  • the frequency domain data of each part is divided into four quadrants.
  • the value of the quadrant corresponding to the noise to be filtered is assigned 0, and the other quadrant data is unchanged.
  • the inverse fast Fourier transform is performed, that is, from the frequency domain to the time domain, the directional filtering can be completed to remove the influence caused by the side lobes. After that, it is also possible to perform band-pass filtering on the time axis on the two-dimensional data map generated according to the motion parameters, and then perform linear interpolation.
  • the method for detecting the tissue hardness takes the tissue cut plane of each depth of the tissue to be measured as the unit, and first performs the direction of the motion parameters of the shear wave at all positions of the tissue cut plane corresponding to all depths at each moment Filter and generate a better two-dimensional data map according to the processed motion parameters, improve the accuracy and reliability of the calculation results, and obtain more accurate and reliable tissue hardness.
  • FIG. 3A is a schematic structural diagram of a device for detecting tissue hardness according to Embodiment 3 of the present invention. As shown in FIG. 3A, the device includes:
  • the obtaining module 31 is used to obtain the motion parameters of the shear wave propagating along the depth direction at multiple positions to be measured over time;
  • the calculation module 32 is configured to calculate the hardness information of the plurality of positions to be measured at each depth according to the motion parameters.
  • the motion parameters may include, but are not limited to: displacement data, strain data, angular velocity data, velocity data, acceleration data, and so on.
  • the device for detecting tissue hardness of this embodiment may be provided in the elasticity detection device. In practical applications, the division of each depth of the tissue to be measured can be determined according to the actual required calculation accuracy, and this embodiment does not limit it here.
  • the device may further include:
  • the processing module is configured to generate a hardness map according to the hardness information at each depth of the plurality of positions to be measured; the horizontal axis of the hardness map is position information, and the vertical axis is depth information.
  • the processing module may include:
  • the processing submodule is configured to generate a corresponding hardness map according to the hardness information after performing two-dimensional median filtering.
  • the filtering sub-module after calculating the hardness values, the filtering sub-module first performs two-dimensional median filtering on these hardness values, and the processing sub-module then generates the corresponding elastic hardness map, which can eliminate the influence of salt and pepper noise, thereby obtaining more accurate and reliable The elastic hardness chart.
  • FIG. 3B is another method for detecting tissue hardness provided in Embodiment 3 of the present invention.
  • FIG. 3A A schematic structural diagram of the device. Based on the embodiment shown in FIG. 3A, the calculation module 32 may include:
  • the speed sub-module 321 is used to calculate the speed information of the shear wave at the plurality of positions to be measured in each depth of the tissue to be measured according to the motion parameters;
  • the hardness submodule 322 is configured to calculate the hardness information according to the speed information.
  • FIG. 3C is a schematic structural diagram of another apparatus for detecting tissue hardness according to Embodiment 3 of the present invention.
  • the speed submodule 321 includes :
  • the generating unit 3211 is configured to sequentially generate a motion parameter-time two-dimensional data map corresponding to each depth according to the motion parameters, the two-dimensional data map characterizing the propagation of the shear wave at the current depth at different times;
  • the analyzing unit 3212 is configured to obtain the velocity information of the shear wave at the plurality of positions to be measured in each depth of the tissue to be measured according to the motion parameter-time two-dimensional data map corresponding to each depth.
  • the two-dimensional data graph represents the propagation of the shear wave at the current depth at different times. Still taking the motion parameter as displacement data as an example, the actual application scenario will be used as an example to illustrate:
  • the displacement data of multiple positions to be measured at the same depth of the tissue to be measured at different times are sequentially selected, and the generating unit 3211 sequentially corresponds to the depth corresponding to each depth.
  • the displacement data generates a displacement-time two-dimensional data map corresponding to the depth, and the analysis unit 3212 obtains the velocity information based on the two-dimensional data map.
  • the analysis unit 3212 may specifically include:
  • the current position to be measured is adaptively determined in the area.
  • the fitting subunit is used to select N reference points on both sides or one side in the shear wave propagation direction from the current position to be measured, and based on the least squares linear fitting method, the N
  • the motion parameters corresponding to the reference point are processed to obtain the velocity value of the shear wave at the current position of the depth to be measured.
  • An update subunit configured to set the position of the reference point adjacent to the position to be measured as the current position to be measured, and instruct the fitting subunit to execute the starting from the current position to be measured, The step of selecting N reference points to both sides or one side in the propagation direction of the shear wave until the shear wave velocity values at all positions of the current depth are obtained.
  • N is a preset value
  • the reference point includes peaks and troughs of shear waves in the two-dimensional data map.
  • N may be a preset positive integer, for example, set to 7.
  • the motion parameter as displacement data as an example, combined with the actual scene for example: according to each two-dimensional data map generated, select the subunit centering on the position of the focus of the acoustic radiation force, select a certain area, and select The initial position to be measured is adaptively selected.
  • the fitting subunit uses the current position to be measured as the starting point, and selects N reference points adjacent to the position to be measured in the shear wave propagation direction. Based on the displacement data of these reference points, the The least squares linear fitting method is used to obtain the velocity value of the shear wave at the position to be measured at the same depth.
  • the update subunit updates the position to be measured, specifically taking the reference point adjacent to the position to be measured as the updated position to be measured, and again taking the updated position to be measured as the starting point and selecting again in the shear wave propagation direction N reference points, based on the current motion parameters of these reference points, the least squares linear fitting method is also used to obtain the shear wave velocity values at the position to be measured, and so on, until the shear wave velocity values at all positions at this depth Are all found.
  • the selected reference point can be specifically selected in the shear wave propagation direction to both sides of the current position to be measured, when the position to be measured is located at the edge of the detection area, then Select in the direction of shear wave propagation to the side of the current position to be measured.
  • the above process is performed for the motion parameters corresponding to each depth of the tissue to be measured, and finally the velocity information of the plurality of positions to be measured of the shear wave at each depth of the tissue to be measured is finally obtained.
  • the shear wave velocity values at all positions of the tissue to be measured can be accurately and reliably obtained, thereby improving the final hardness map. Accuracy and reliability.
  • Embodiment 3 of the present invention provides yet another device for detecting tissue hardness.
  • the fitting subunit may include:
  • the fitting component is used to sequentially perform linear fitting based on least squares to the M adjacent reference points in the reference points to obtain a corresponding set of fitted straight lines, M is less than N;
  • the fitting component is further configured to select the first fitted line in the set of fitted lines as the target fitted line, and the residual from the reference point to the first fitted line is the smallest;
  • the calculating component is used to fit a straight line according to the target to obtain the velocity value of the shear wave at the current position of the depth to be measured.
  • M and N are positive integers, for example, N is set to 7, and M is set to 5.
  • the motion parameter is still used as the displacement data, and in combination with the actual scene as an example: after generating a two-dimensional data map corresponding to each depth, first select a subunit to select the area to be measured based on the acoustic radiation force focus, and adaptively determine the current For the location to be measured, select the component to take the current location to be measured as the starting point, and select the 7 most-valued coordinates to both sides or one side in the shear wave propagation direction, that is, determine 7 reference points, and the fitting component to select these in turn 5 adjacent points in the point are linearly fitted based on least squares, and finally the fitting line with the smallest residual error to these 7 reference points is selected as the final target fitting line, and the calculation part fits the straight line according to the target Obtain the shear wave velocity value of the position to be measured.
  • next calculated position to be measured is continuously updated based on the reference point adjacent to the current position to be measured, and based on the updated position to be measured, the above steps are repeated until the shear wave velocity of all positions at the current depth The values are all calculated.
  • reference points are selected according to the current position to be measured, and the linear fitting of the least squares based on these reference points is used to accurately obtain the velocity value of the current position of the shear wave at the current depth, and finally the generated The accuracy and reliability of the elastic hardness chart.
  • the hardness value of the tissue to be measured can be calculated by various methods, which is not limited in this embodiment.
  • the hardness submodule 322 may specifically include:
  • the calculation unit is configured to calculate the hardness information using the first formula.
  • the first formula is used to calculate the hardness values at all positions of each depth.
  • the hardness values of all positions of the tissue to be measured can be obtained accurately and quickly, so as to obtain the corresponding elastic hardness map accurately and quickly.
  • the device for detecting the hardness of the tissue takes the tissue cut plane of each depth of the tissue to be measured as a unit, and calculates multiple The hardness information of the position to be measured at each depth is used to obtain the tissue hardness.
  • the calculation amount of the above scheme is small, the result is accurate and real-time, and the tissue hardness can be obtained accurately and efficiently.
  • FIG. 4 is a schematic structural diagram of an apparatus for detecting tissue hardness according to Example 4 of the present invention. As shown in FIG. 4, based on FIG. 3C and any of the embodiments based on the embodiment shown in FIG. 3C, the speed Submodule 321 also includes:
  • the filtering unit 41 is used to generate shear wave at all positions and depths of the tissue under test for each moment before the generating unit sequentially generates the motion parameter-time two-dimensional data map corresponding to each depth according to the motion parameters The motion parameters are filtered in the direction.
  • the filtering unit 41 performs directional filtering on the motion parameters of all positions of the shear wave at all depths of the tissue under test at each moment, and then the generation unit generates shear waves in the tissue under test according to different times.
  • the two-dimensional data map can be optimized.
  • the device may further include:
  • the optimization module is configured to perform band-pass filtering on the time axis of the two-dimensional data map after the generation unit 3211 sequentially generates the motion parameter-time two-dimensional data map corresponding to each depth according to the motion parameters Linear interpolation.
  • the optimization module may first perform band-pass filtering and linear interpolation on the two-dimensional data map in sequence. Then, referring to the aforementioned method, the shear wave velocity values of all positions at the current depth are obtained according to the two-dimensional data map after band-pass filtering and linear interpolation. Furthermore, the above operations are performed on the remaining data to be processed corresponding to each of the other depths, and finally the speed values of all positions at all depths of the tissue to be measured are finally obtained, so as to obtain the hardness values of all positions of all depths of the tissue to be measured.
  • the above optimized implementations can be implemented independently or in combination. For example, after directional filtering is performed on the motion parameters at each moment in the deformation estimation data, the two are generated according to the motion parameters corresponding to each depth at different moments. Dimensional data graph, and then perform band-pass filtering and linear interpolation on the two-dimensional data graph in sequence.
  • the filtering unit 41 may include:
  • a transform subunit used to perform fast Fourier transform to convert the motion parameters of the shear wave at all positions and depths of the tissue to be measured from the time domain to the frequency domain at each moment;
  • the transform subunit is also used to convert the motion parameters of the shear wave at all depths and all positions of the tissue to be measured from the frequency domain to the time domain by performing inverse fast Fourier transform.
  • the motion parameters of the shear wave at all depths and all positions of the tissue to be measured at each moment first transform the subunits using fast Fourier transform to convert the motion parameters from the time domain to the frequency domain, and then divide the subunits Taking the position of the focus of the acoustic radiation force as the axis, the motion parameters after the fast Fourier transform are divided into left and right parts.
  • the frequency domain data of each part is divided into four quadrants. The value of the quadrant corresponding to the noise to be filtered is assigned 0.
  • the transform subunit performs inverse fast Fourier transform, that is, then transforms from the frequency domain to the time domain, the directional filtering can be completed to remove the influence caused by the side lobes. After that, it is also possible to perform band-pass filtering on the time axis on the two-dimensional data map generated according to the motion parameters, and then perform linear interpolation.
  • the device for detecting the hardness of the tissue takes the tissue cut plane of each depth of the tissue to be measured as a unit, and performs the direction of the motion parameters of the shear wave at all positions of the tissue cut plane corresponding to all depths at each moment Filter and generate a better two-dimensional data map according to the processed motion parameters, improve the accuracy and reliability of the calculation results, and obtain more accurate and reliable tissue hardness.
  • Embodiment 5 of the present invention provides a system for detecting tissue hardness.
  • the system includes: a transducer array, and a device as described in any one of the foregoing embodiments.
  • the transducer array is connected to the device, and the transducer array corresponds to a plurality of positions to be measured.
  • the device for detecting the hardness of the tissue can obtain the motion parameters of the shear wave propagating along the depth direction with time at a plurality of positions to be measured, and calculate the motion parameters according to the motion parameters Hardness information at each depth of multiple locations to be measured.
  • the system for detecting the hardness of the tissue takes the tissue cut plane of each depth of the tissue to be measured as a unit, and calculates multiple The hardness information of the position to be measured at each depth is used to obtain the tissue hardness.
  • the calculation amount of the above scheme is small, the result is accurate and real-time, and the tissue hardness can be obtained accurately and efficiently.

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Abstract

一种用于检测组织硬度的方法、设备及系统，包括：获取剪切波在多个待测位置随着时间沿深度方向传播的运动参数（101）；根据运动参数，计算多个待测位置在每一深度的硬度信息（102），以待测组织每个深度的组织切面为单位，根据剪切波在多个待测位置随着时间沿深度方向传播的运动参数，计算多个待测位置在每一深度的硬度信息，从而获得组织硬度，计算量小，结果准确且实时性很好，能够准确、高效地获得组织硬度。

Description

用于检测组织硬度的方法、设备及系统 技术领域
本发明涉及超声医学影像领域，尤其涉及一种用于检测组织硬度的方法、设备及系统。
背景技术
组织的弹性是受生理和病理因素影响最大的生物组织力学参数，人体大量的生理和病理上的变化都伴随着组织弹性的变化，如随着肝脏纤维化程度的加深，肝脏的硬度会逐渐变大。因此可以将组织的弹性作为反应生物组织特性的一个重要参数。20世纪90年代初日本学者Y.Yamakoshi与美国学者J.Ophir首先提出了超声弹性成像技术，该技术以组织的剪切模量、杨氏模量、应力和应变等弹性参数为成像对象。
其中，组织二维超声弹性成像的面积远远大于一维超声弹性成像的面积，更有利于医生进行诊断，增加了发现病灶的可能性。具体的，二维超声弹性成像首先通过声辐射力的方式产生一个在组织内传播的剪切波，继而通过超高速超声数据采集系统采集包括剪切波在组织内传播信息的回波数据。得到回波数据后可以通过以下两种方式求得组织的形变信息，从而获得组织的形变估计数据：一种是以多普勒超声图像或超声序列图像为基础，使用一些基于图像数据的位移估计算法得到相关的组织形变信息；另一种是组织产生形变时，得到组织产生形变前后超声换能器接收到的射频信号，直接对这种射频信号进行处理，估计出组织形变信息。进而在形变估计数据的基础上，通过一系列的算法求得剪切波在传播经过组织不同部位时的速度值，再根据该速度值得到该组织不同部位的硬度值，从而最终得到该组织的二维切面弹性硬度图。
通过上述过程可知从形变估计数据到最终获得硬度结果的方法至关重要，该方法将直接影响最终弹性硬度图的精度和效果，而效果较差的弹性硬度图在临床上甚至会误导医生的判断。因此，如何准确、高效地获得组织硬度成为亟待解决的问题。
发明内容
本发明提供一种用于检测组织硬度的方法、设备及系统，用于实现准确、高效地获得组织硬度。
本发明的第一个方面是提供一种用于检测组织硬度的方法，包括：获取剪切波在多个待测位置随着时间沿深度方向传播的运动参数；根据所述运动参数，计算所述多个待测位置在每一深度的硬度信息。
本发明的另一个方面是提供一种用于检测组织硬度的设备，包括：获取模块，用于获取剪切波在多个待测位置随着时间沿深度方向传播的运动参数；计算模块，用于根据所述运动参数，计算所述多个待测位置在每一深度的硬度信息。
本发明的又一个方面是提供一种用于检测组织硬度的系统，包括：换能器阵列，以及如前所述的设备，所述换能器阵列与所述设备连接，所述换能器阵列对应于多个待测位置。
本发明提供的用于检测组织硬度的方法、设备及系统，以待测组织每个深度的组织切面为单位，根据剪切波在多个待测位置随着时间沿深度方向传播的运动参数，计算多个待测位置在每一深度的硬度信息，从而获得组织硬度，上述方案的计算量小，结果准确且实时性很好，能够准确、高效地获得组织硬度。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1A为为本发明实施例一提供的一种用于检测组织硬度的方法的流程示意图；
图1B为为本发明实施例一提供的另一种用于检测组织硬度的方法的流程示意图；
图1C为本发明实施例一提供的又一种用于检测组织硬度的方法的流 程示意图；
图2为本发明实施例二提供的一种用于检测组织硬度的方法的流程示意图；
图3A为为本发明实施例三提供的一种用于检测组织硬度的设备的结构示意图；
图3B为为本发明实施例三提供的另一种用于检测组织硬度的设备的结构示意图；
图3C为本发明实施例三提供的又一种用于检测组织硬度的设备的结构示意图；
图4为本发明实施例四提供的一种用于检测组织硬度的设备的结构示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
图1A为为本发明实施例一提供的一种用于检测组织硬度的方法的流程示意图，如图1A所示，本实施例以该方法应用于用于检测组织硬度的设备中来举例说明，该方法包括：
101、获取剪切波在多个待测位置随着时间沿深度方向传播的运动参数；
102、根据所述运动参数，计算所述多个待测位置在每一深度的硬度信息。
其中，所述运动参数可以包括但不限于：位移数据、应变数据、角速度数据、速度数据、加速度数据等。本实施例的执行主体可以为用于检测组织硬度的设备，该设备可设置在弹性检测设备中。以运动参数为位移数据为例，结合实际应用场景来进行举例说明：
在获取组织硬度的过程中，需要在组织中激发剪切波，比如通过声 辐射力的方式在组织中激发出剪切波，继而采集包括剪切波在组织内传播信息的回波数据，并利用回波数据获得形变估计数据，该数据包括剪切波在多个待测位置随着时间沿深度方向传播的位移数据。基于所述位移数据，计算各待测位置在各深度的硬度信息，获得组织硬度。实际应用中，对待测组织各深度的划分可以根据实际需要的计算精度确定，本实施例在此不对其进行限制。
后续，基于硬度信息，可以快速准确的获得相应的弹性硬度图，对应的，在图1A所示实施方式的基础上，所述方法还可以包括：
根据所述多个待测位置在每一深度的硬度信息，生成硬度图；
所述硬度图横轴为位置信息，纵轴为深度信息。
进一步的，为了获得更加准确的弹性硬度图，在上述实施方式的基础上，所述根据所述多个待测位置在每一深度的硬度信息，生成硬度图，具体可以包括：
对所述硬度信息进行二维中值滤波；
根据进行二维中值滤波后的所述硬度信息，生成相应的弹性硬度图。
本实施方式中，在计算获得硬度值之后，先对这些硬度值进行二维中值滤波，再生成相应的弹性硬度图，可以消除椒盐噪声的影响，从而得到更加准确可靠的弹性硬度图。
基于硬度值即可生成相应的待测组织弹性硬度图。经过实验室中包含异物的体模数据验证，通过本方案获得的硬度信息得到的二维切面弹性硬度图结果与体模数据一致，可以很好的满足在临床上的应用。
可选的，基于运动参数获得硬度信息的方法可以有多种，举例来说，如图1B所示，图1B为为本发明实施例一提供的另一种用于检测组织硬度的方法的流程示意图，在图1A所示实施方式的基础上，102可以包括：
103、根据所述运动参数计算剪切波在待测组织的每个深度中所述多个待测位置处的速度信息；
104、根据所述速度信息计算所述硬度信息。
具体的，根据运动参数计算速度信息的方法有多种。可选的，如图1C所示，图1C为本发明实施例一提供的又一种用于检测组织硬度的方法 的流程示意图，在图1B所示实施方式的基础上，103包括：
105、根据所述运动参数，依次生成每个深度对应的运动参数-时间二维数据图，所述二维数据图表征在不同时刻下剪切波在当前深度的传播情况；
106、根据各深度对应的运动参数-时间二维数据图，获得剪切波在所述待测组织的每个深度中所述多个待测位置处的速度信息。
其中，所述二维数据图表征在不同时刻下剪切波在当前深度的传播情况。仍以运动参数为位移数据为例，结合实际应用场景来进行举例说明：
基于剪切波在多个待测位置随着时间沿深度方向传播的位移数据，依次选取不同时刻下待测组织同一深度多个待测位置的位移数据，并依次针对每个深度对应的位移数据，生成该深度对应的位移-时间二维数据图，基于二维数据图，获得所述速度信息。
具体的，基于二维数据图可以快速准确地获得剪切波在所述待测组织的每个深度中所述多个待测位置处的速度信息，可选的，在图1C所示实施方式的基础上，106具体可以包括：
107、针对每个深度对应的运动参数-时间二维数据图，以所述二维数据图中的声辐射力焦点为中心，选取一定区域为待测区域，从所述待测区域中自适应确定当前的待测位置。
108、以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个参考点，并基于最小二乘法线性拟合方法，对所述N个参考点对应的运动参数进行处理，获得剪切波在当前所述深度的所述待测位置的速度值。
109、将与所述待测位置相邻的参考点所在的位置设定为当前的待测位置，并返回执行108，直至获得剪切波在当前所述深度的所有位置的速度值。
其中，N为预设的值，所述参考点包括所述二维数据图中剪切波的波峰和波谷。具体的，N可以为预设的正整数，例如，设为7。
仍以运动参数为位移数据为例，结合实际场景举例来说：获得不同时刻同一深度下所有位置的位移数据，构建一幅位移-时间的二维数据图， 举例来说，假设将待测组织的深度划分为A个深度，则最终可得到A幅二维数据图。根据生成的每一幅二维数据图，以声辐射力焦点所在的位置为中心，选取一定区域，从该区域中自适应选取出初始的待测位置，以当前待测位置为起点，在剪切波传播方向上选取待测位置相邻的N个参考点，基于这些参考点的位移数据，通过最小二乘线性拟合法，获得剪切波在该同一深度下待测位置处的速度值。之后，更新待测位置，具体的将与待测位置相邻的参考点作为更新后的待测位置，再次以更新后的待测位置为起点，在剪切波传播方向上再次选取N个参考点，基于当前这些参考点的运动参数，同样通过最小二乘线性拟合法，获得待测位置处的剪切波速度值，依次类推，直至该深度下所有位置的剪切波速度值均被求出。
具体的，当待测位置未位于待测区域边缘时，选取的参考点具体可以在剪切波传播方向上向当前待测位置的两侧进行选取，当待测位置位于检测区域边缘时，则在剪切波传播方向上向当前待测位置的一侧进行选取。
进一步的，针对待测组织的每个深度对应的运动参数均执行上述过程，最终获得剪切波在所述待测组织的各深度的多个待测位置的速度信息。
通过本实施方式，根据各深度对应的二维数据图，基于最小二乘线性拟合法，可以准确可靠地求出剪切波在待测组织各深度所有位置的速度值，从而提高最终硬度图的准确性和可靠性。
可选的，本发明实施例一提供又一种用于检测组织硬度的方法，在上述实施方式的基础上，108具体可以包括：
以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个所述参考点；
依次将所述参考点中的M个相邻参考点进行基于最小二乘的线性拟合，获得相应的拟合直线集合，M小于N；
将所述拟合直线集合中的第一拟合直线选择为目标拟合直线，所述参考点至所述第一拟合直线的残差最小；
根据所述目标拟合直线，得到所述剪切波在当前所述深度的所述待 测位置的速度值。
其中，M和N均为正整数，例如，N设为7，M设为5。
结合上述举例，仍以运动参数为位移数据，结合实际场景举例来说：生成各深度对应的二维数据图后，首先基于声辐射力焦点选取的待测区域，自适应确定出当前的待测位置，以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧分别选取7个最值坐标，即确定7个参考点，依次选取这些点中的5个相邻点进行基于最小二乘的线性拟合，最终选择至这7个参考点的残差最小的拟合直线为最终的目标拟合直线，根据该目标拟合直线得到该待测位置的剪切波速度值。基于前述方案可以理解，后续基于当前待测位置相邻的参考点不断更新下次计算的待测位置，并基于更新后的待测位置，重复上述步骤直到当前深度下所有位置的剪切波速度值均被求出。
通过上述实施方式，根据当前的待测位置选取参考点，针对这些参考点基于最小二乘的线性拟合，准确地获得剪切波在当前深度的当前待测位置的速度值，最终提高生成的弹性硬度图的准确性和可靠性。
实际应用中，可以通过多种方法计算出待测组织的硬度值，本实施例在此不对其进行限制。举例来说，在图1B以及基于图1B所示实施方式的任一实施方式的基础上，104具体可以包括：
利用第一公式，计算所述硬度信息。
其中，所述第一公式为：E＝3ρV S 2，其中，E为所述硬度信息，ρ为所述待测组织的密度，V S为所述速度信息。
以实际场景举例来说：通过前述步骤获得剪切波在待测组织各深度所有位置的速度值后，利用第一公式分别计算出各深度所有位置的硬度值。
通过本实施方式，可以准确快速地获得待测组织各深度所有位置的硬度值，从而准确快速地获得相应的弹性硬度图。
本实施例提供的用于检测组织硬度的方法，以待测组织每个深度的组织切面为单位，根据剪切波在多个待测位置随着时间沿深度方向传播的运动参数，计算多个待测位置在每一深度的硬度信息，从而获得组织硬度，上述方案的计算量小，结果准确且实时性很好，能够准确、高效 地获得组织硬度。
图2为本发明实施例二提供的一种用于检测组织硬度的方法的流程示意图，如图2所示，本实施例仍以该方法应用于用于检测组织硬度的设备中来举例说明，在图1C以及基于图1C所示实施方式的任一实施方式的基础上，在105之前，还包括：
201、对每个时刻下剪切波在待测组织所有深度所有位置的运动参数进行方向滤波。
以实际场景举例来说：首先对每个时刻下剪切波在待测组织所有深度所有位置的运动参数进行方向滤波，之后根据不同时刻下剪切波在待测组织每个深度的所有位置的运动参数生成各深度对应的二维数据图后，根据每个二维数据图，以声辐射力焦点所在的位置为中心选取待测区域，并从待测区域中自适应选取出当前的待测位置，基于待测位置参照前述方法，先获得当前待测位置的剪切波速度值，后续通过更新待测位置并计算，获得当前深度下所有位置的剪切波速度值。进而针对其余每个深度对应的待处理数据均执行上述操作，最终获得待测组织所有深度的所有位置的速度值，从而求出待测组织所有深度所有位置的硬度值。
具体的，由于通过声辐射力的方式在产生剪切波时，会产生带有一定能量(位移)的旁瓣，这些能量(位移)会在主瓣附近产生一个振幅较小的剪切波，这会严重影响主瓣产生的剪切波在经过待测位置乃至后续传播部位速度值的计算，最终导致二维切面弹性硬度值计算不准确。通过采用方向滤波的方法，可以有效的滤除旁瓣引起的位移即消除旁瓣的能量(位移)，同时不会对主瓣产生影响，最终只保留了计算剪切波速度值所需的运动参数。
另外，运动参数的规模有限也会影响最终的计算结果，可选的，生成二维数据图之后，还可以先对二维数据图进行优化处理。相应的，在在图1C以及基于图1C所示实施方式的任一实施方式的基础上，在105之后，还可以包括：
对所述二维数据图在时间轴上进行带通滤波，并进行线性插值。
以实际场景举例来说：根据某一深度对应的运动参数生成二维数据图后，可以先对二维数据图依次进行带通滤波和线性插值。之后，参照 前述方法根据带通滤波和线性插值后的二维数据图，获得当前深度下所有位置的剪切波速度值。进而针对其余每个深度对应的待处理数据均执行上述操作，最终获得待测组织所有深度的所有位置的速度值，从而求出待测组织所有深度所有位置的硬度值。
通过带通滤波将得到效果很好的二维数据图，从而提高最终计算结果的准确性。通过线性插值可以增加数据规模，进一步提升线性拟合的精度，提高最终计算结果的准确性。
可以理解，上述各优化的实施方式可以独立实施，也可以结合实施，例如，对形变估计数据中每个时刻下的运动参数进行方向滤波后，根据不同时刻下每个深度对应的运动参数生成二维数据图，再对该二维数据图依次进行带通滤波和线性插值。
可选的，在图2所示实施方式的基础上，201具体可以包括：
通过进行快速傅里叶变换，将所述每个时刻下剪切波在待测组织所有深度所有位置的运动参数从时域转换至频域；
以声辐射力焦点所在的位置为轴，将转换后的每个时刻下剪切波在待测组织所有深度所有位置的运动参数划分为两部分，每部分的频域数据分为四个象限，其中待过滤噪声对应的象限的数据赋为0，其它象限的数据不变；
通过进行快速傅里叶逆变换，将当前每个时刻下剪切波在待测组织所有深度所有位置的运动参数从频域转换至时域。
具体的，对每个时刻下剪切波在待测组织所有深度所有位置的运动参数，首先采用快速傅里叶变换的方法将运动参数从时域转换到频域，之后以声辐射力焦点所在位置为轴，将快速傅里叶变换后的运动参数分为左右两部分，其中每部分的频域数据分为四个象限，待过滤噪声对应的象限的值赋0，其他象限数据不变，最后进行快速傅里叶逆变换，即再从频域转换到时域，即可完成方向滤波，去除旁瓣带来的影响。之后，还可以对根据运动参数生成的二维数据图，在时间轴上进行带通滤波，之后进行线性插值。
本实施例提供的用于检测组织硬度的方法，以待测组织每个深度的组织切面为单位，对每个时刻下剪切波在所有深度对应的组织切面的所 有位置的运动参数先进行方向滤波，并根据处理后的运动参数生成效果更好的二维数据图，提高计算结果的准确性和可靠性，从而获得更加准确可靠的组织硬度。
图3A为为本发明实施例三提供的一种用于检测组织硬度的设备的结构示意图，如图3A所示，该设备包括：
获取模块31，用于获取剪切波在多个待测位置随着时间沿深度方向传播的运动参数；
计算模块32，用于根据所述运动参数，计算所述多个待测位置在每一深度的硬度信息。
其中，所述运动参数可以包括但不限于：位移数据、应变数据、角速度数据、速度数据、加速度数据等。本实施例的用于检测组织硬度的设备可设置在弹性检测设备中。实际应用中，对待测组织各深度的划分可以根据实际需要的计算精度确定，本实施例在此不对其进行限制。
后续，基于硬度信息，可以快速准确的获得相应的弹性硬度图，对应的，在图3A所示实施方式的基础上，所述设备还可以包括：
处理模块，用于根据所述多个待测位置在每一深度的硬度信息，生成硬度图；所述硬度图横轴为位置信息，纵轴为深度信息。
进一步的，为了获得更加准确的弹性硬度图，在上述实施方式的基础上，所述处理模块可以包括：
滤波子模块，用于对所述硬度信息进行二维中值滤波；
处理子模块，用于根据进行二维中值滤波后的所述硬度信息，生成相应的硬度图。
本实施方式中，在计算获得硬度值之后，滤波子模块先对这些硬度值进行二维中值滤波，处理子模块再生成相应的弹性硬度图，可以消除椒盐噪声的影响，从而得到更加准确可靠的弹性硬度图。
可选的，计算模块32基于运动参数获得硬度信息的方法可以有多种，举例来说，如图3B所示，图3B为为本发明实施例三提供的另一种用于检测组织硬度的设备的结构示意图，在图3A所示实施方式的基础上，计算模块32可以包括：
速度子模块321，用于根据所述运动参数计算剪切波在待测组织的每 个深度中所述多个待测位置处的速度信息；
硬度子模块322，用于根据所述速度信息计算所述硬度信息。
具体的，速度子模块根据运动参数计算速度信息的方法有多种。可选的，如图3C所示，图3C为本发明实施例三提供的又一种用于检测组织硬度的设备的结构示意图，在图3B所示实施方式的基础上，速度子模块321包括：
生成单元3211，用于根据所述运动参数，依次生成每个深度对应的运动参数-时间二维数据图，所述二维数据图表征在不同时刻下剪切波在当前深度的传播情况；
分析单元3212，用于根据各深度对应的运动参数-时间二维数据图，获得剪切波在所述待测组织的每个深度中所述多个待测位置处的速度信息。
其中，所述二维数据图表征在不同时刻下剪切波在当前深度的传播情况。仍以运动参数为位移数据为例，结合实际应用场景来进行举例说明：
基于剪切波在多个待测位置随着时间沿深度方向传播的位移数据，依次选取不同时刻下待测组织同一深度多个待测位置的位移数据，生成单元3211依次针对每个深度对应的位移数据，生成该深度对应的位移-时间二维数据图，分析单元3212基于二维数据图，获得所述速度信息。
具体的，基于二维数据图可以快速准确地获得剪切波在所述待测组织的每个深度中所述多个待测位置处的速度信息，可选的，在图3C所示实施方式的基础上，分析单元3212具体可以包括：
选取子单元，用于针对每个深度对应的运动参数-时间二维数据图，以所述二维数据图中的声辐射力焦点为中心，选取一定区域为待测区域，从所述待测区域中自适应确定当前的待测位置。
拟合子单元，用于以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个参考点，并基于最小二乘法线性拟合方法，对所述N个参考点对应的运动参数进行处理，获得剪切波在当前所述深度的所述待测位置的速度值。
更新子单元，用于将与所述待测位置相邻的参考点所在的位置设定 为当前的待测位置，并指示所述拟合子单元执行所述以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个参考点的步骤，直至获得剪切波在当前所述深度的所有位置的速度值。
其中，N为预设的值，所述参考点包括所述二维数据图中剪切波的波峰和波谷。具体的，N可以为预设的正整数，例如，设为7。
仍以运动参数为位移数据为例，结合实际场景举例来说：根据生成的每一幅二维数据图，选取子单元以声辐射力焦点所在的位置为中心，选取一定区域，从该区域中自适应选取出初始的待测位置，拟合子单元以当前待测位置为起点，在剪切波传播方向上选取待测位置相邻的N个参考点，基于这些参考点的位移数据，通过最小二乘线性拟合法，获得剪切波在该同一深度下待测位置处的速度值。之后，更新子单元更新待测位置，具体的将与待测位置相邻的参考点作为更新后的待测位置，再次以更新后的待测位置为起点，在剪切波传播方向上再次选取N个参考点，基于当前这些参考点的运动参数，同样通过最小二乘线性拟合法，获得待测位置处的剪切波速度值，依次类推，直至该深度下所有位置的剪切波速度值均被求出。
具体的，当待测位置未位于待测区域边缘时，选取的参考点具体可以在剪切波传播方向上向当前待测位置的两侧进行选取，当待测位置位于检测区域边缘时，则在剪切波传播方向上向当前待测位置的一侧进行选取。
进一步的，针对待测组织的每个深度对应的运动参数均执行上述过程，最终获得剪切波在所述待测组织的各深度的多个待测位置的速度信息。
通过本实施方式，根据各深度对应的二维数据图，基于最小二乘线性拟合法，可以准确可靠地求出剪切波在待测组织各深度所有位置的速度值，从而提高最终硬度图的准确性和可靠性。
可选的，本发明实施例三提供又一种用于检测组织硬度的设备，在上述实施方式的基础上，所述拟合子单元可以包括：
选取部件，用于以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个所述参考点；
拟合部件，用于依次将所述参考点中的M个相邻参考点进行基于最小二乘的线性拟合，获得相应的拟合直线集合，M小于N；
所述拟合部件，还用于将所述拟合直线集合中的第一拟合直线选择为目标拟合直线，所述参考点至所述第一拟合直线的残差最小；
计算部件，用于根据所述目标拟合直线，得到所述剪切波在当前所述深度的所述待测位置的速度值。
其中，M和N均为正整数，例如，N设为7，M设为5。
结合上述举例，仍以运动参数为位移数据，结合实际场景举例来说：生成各深度对应的二维数据图后，首先选取子单元基于声辐射力焦点选取的待测区域，自适应确定出当前的待测位置，选取部件以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧分别选取7个最值坐标，即确定7个参考点，拟合部件依次选取这些点中的5个相邻点进行基于最小二乘的线性拟合，最终选择至这7个参考点的残差最小的拟合直线为最终的目标拟合直线，计算部件根据该目标拟合直线得到该待测位置的剪切波速度值。基于前述方案可以理解，后续基于当前待测位置相邻的参考点不断更新下次计算的待测位置，并基于更新后的待测位置，重复上述步骤直到当前深度下所有位置的剪切波速度值均被求出。
通过上述实施方式，根据当前的待测位置选取参考点，针对这些参考点基于最小二乘的线性拟合，准确地获得剪切波在当前深度的当前待测位置的速度值，最终提高生成的弹性硬度图的准确性和可靠性。
实际应用中，可以通过多种方法计算出待测组织的硬度值，本实施例在此不对其进行限制。举例来说，在图3B以及基于图3B所示实施方式的任一实施方式的基础上，硬度子模块322具体可以包括：
计算单元，用于利用第一公式，计算所述硬度信息。
其中，所述第一公式为：E＝3ρV S 2，其中，E为所述硬度信息，ρ为所述待测组织的密度，V S为所述速度信息。
以实际场景举例来说：通过前述步骤获得剪切波在待测组织各深度所有位置的速度值后，利用第一公式分别计算出各深度所有位置的硬度值。
通过本实施方式，可以准确快速地获得待测组织各深度所有位置的 硬度值，从而准确快速地获得相应的弹性硬度图。
本实施例提供的用于检测组织硬度的设备，以待测组织每个深度的组织切面为单位，根据剪切波在多个待测位置随着时间沿深度方向传播的运动参数，计算多个待测位置在每一深度的硬度信息，从而获得组织硬度，上述方案的计算量小，结果准确且实时性很好，能够准确、高效地获得组织硬度。
图4为本发明实施例四提供的一种用于检测组织硬度的设备的结构示意图，如图4所示，在图3C以及基于图3C所示实施方式的任一实施方式的基础上，速度子模块321还包括：
滤波单元41，用于在所述生成单元根据所述运动参数，依次生成每个深度对应的运动参数-时间二维数据图之前，对每个时刻下剪切波在待测组织所有深度所有位置的运动参数进行方向滤波。
以实际场景举例来说：首先滤波单元41对每个时刻下剪切波在待测组织所有深度所有位置的运动参数进行方向滤波，之后生成单元根据不同时刻下剪切波在待测组织每个深度的所有位置的运动参数生成各深度对应的二维数据图后，选取子单元根据每个二维数据图，以声辐射力焦点所在的位置为中心选取待测区域，并从待测区域中自适应选取出当前的待测位置，基于待测位置参照前述方法，先获得当前待测位置的剪切波速度值，后续通过更新待测位置并计算，获得当前深度下所有位置的剪切波速度值。进而针对其余每个深度对应的待处理数据均执行上述操作，最终获得待测组织所有深度的所有位置的速度值，从而求出待测组织所有深度所有位置的硬度值。
可选的，生成二维数据图之后，还可以先对二维数据图进行优化处理。相应的，在在图3C以及基于图3C所示实施方式的任一实施方式的基础上，该设备还可以包括：
优化模块，用于在生成单元3211根据所述运动参数，依次生成每个深度对应的运动参数-时间二维数据图之后，对所述二维数据图在时间轴上进行带通滤波，并进行线性插值。
以实际场景举例来说：生成单元3211根据某一深度对应的运动参数生成二维数据图后，优化模块可以先对二维数据图依次进行带通滤波和 线性插值。之后，参照前述方法根据带通滤波和线性插值后的二维数据图，获得当前深度下所有位置的剪切波速度值。进而针对其余每个深度对应的待处理数据均执行上述操作，最终获得待测组织所有深度的所有位置的速度值，从而求出待测组织所有深度所有位置的硬度值。
通过带通滤波将得到效果很好的二维数据图，从而提高最终计算结果的准确性。通过线性插值可以增加数据规模，进一步提升线性拟合的精度，提高最终计算结果的准确性。
可以理解，上述各优化的实施方式可以独立实施，也可以结合实施，例如，对形变估计数据中每个时刻下的运动参数进行方向滤波后，根据不同时刻下每个深度对应的运动参数生成二维数据图，再对该二维数据图依次进行带通滤波和线性插值。
可选的，在图4所示实施方式的基础上，滤波单元41可以包括：
变换子单元，用于通过进行快速傅里叶变换，将所述每个时刻下剪切波在待测组织所有深度所有位置的运动参数从时域转换至频域；
划分子单元，用于以声辐射力焦点所在的位置为轴，将转换后的每个时刻下剪切波在待测组织所有深度所有位置的运动参数划分为两部分，每部分的频域数据分为四个象限，其中待过滤噪声对应的象限的数据赋为0，其它象限的数据不变；
所述变换子单元，还用于通过进行快速傅里叶逆变换，将当前每个时刻下剪切波在待测组织所有深度所有位置的运动参数从频域转换至时域。
具体的，对每个时刻下剪切波在待测组织所有深度所有位置的运动参数，首先变换子单元采用快速傅里叶变换的方法将运动参数从时域转换到频域，之后划分子单元以声辐射力焦点所在位置为轴，将快速傅里叶变换后的运动参数分为左右两部分，其中每部分的频域数据分为四个象限，待过滤噪声对应的象限的值赋0，其他象限数据不变，最后变换子单元进行快速傅里叶逆变换，即再从频域转换到时域，即可完成方向滤波，去除旁瓣带来的影响。之后，还可以对根据运动参数生成的二维数据图，在时间轴上进行带通滤波，之后进行线性插值。
本实施例提供的用于检测组织硬度的设备，以待测组织每个深度的 组织切面为单位，对每个时刻下剪切波在所有深度对应的组织切面的所有位置的运动参数先进行方向滤波，并根据处理后的运动参数生成效果更好的二维数据图，提高计算结果的准确性和可靠性，从而获得更加准确可靠的组织硬度。
本发明实施例五提供一种用于检测组织硬度的系统，该系统包括：换能器阵列，以及如前述任一实施例所述的设备。
其中，所述换能器阵列与所述设备连接，所述换能器阵列对应于多个待测位置。
具体的，通过换能器阵列进行采集，用于检测组织硬度的设备可以获取剪切波在多个待测位置随着时间沿深度方向传播的运动参数，并根据所述运动参数，计算所述多个待测位置在每一深度的硬度信息。
本实施例提供的用于检测组织硬度的系统，以待测组织每个深度的组织切面为单位，根据剪切波在多个待测位置随着时间沿深度方向传播的运动参数，计算多个待测位置在每一深度的硬度信息，从而获得组织硬度，上述方案的计算量小，结果准确且实时性很好，能够准确、高效地获得组织硬度。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的设备和系统的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
本领域普通技术人员可以理解：实现上述各方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成。前述的程序可以存储于一计算机可读取存储介质中。该程序在执行时，执行包括上述各方法实施例的步骤；而前述的存储介质包括：ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。

Claims (21)

  1. 一种用于检测组织硬度的方法，其特征在于，包括：
    获取剪切波在多个待测位置随着时间沿深度方向传播的运动参数；
    根据所述运动参数，计算所述多个待测位置在每一深度的硬度信息。
  2. 如权利要求1所述的方法，其特征在于，还包括：
    根据所述多个待测位置在每一深度的硬度信息，生成硬度图；
    所述硬度图横轴为位置信息，纵轴为深度信息。
  3. 如权利要求1所述的方法，其特征在于，所述根据所述运动参数，计算所述多个待测位置在每一深度的硬度信息，包括：
    根据所述运动参数计算剪切波在待测组织的每个深度中所述多个待测位置处的速度信息；
    根据所述速度信息计算所述硬度信息。
  4. 根据权利要求3所述的方法，其特征在于，所述根据所述运动参数计算剪切波在所述待测组织的每个深度中所述多个待测位置处的速度信息，包括：
    根据所述运动参数，依次生成每个深度对应的运动参数-时间二维数据图，所述二维数据图表征在不同时刻下剪切波在当前深度的传播情况；
    根据各深度对应的运动参数-时间二维数据图，获得剪切波在所述待测组织的每个深度中所述多个待测位置处的速度信息。
  5. 根据权利要求4所述的方法，其特征在于，所述根据所述运动参数，依次生成每个深度对应的运动参数-时间二维数据图之前，还包括：
    对每个时刻下剪切波在待测组织所有深度所有位置的运动参数进行方向滤波。
  6. 根据权利要求5所述的方法，其特征在于，所述对每个时刻下剪切波在待测组织所有深度所有位置的运动参数进行方向滤波，包括：
    通过进行快速傅里叶变换，将所述每个时刻下剪切波在待测组织所有深度所有位置的运动参数从时域转换至频域；
    以声辐射力焦点所在的位置为轴，将转换后的每个时刻下剪切波在 待测组织所有深度所有位置的运动参数划分为两部分，每部分的频域数据分为四个象限，其中待过滤噪声对应的象限的数据赋为0，其它象限的数据不变；
    通过进行快速傅里叶逆变换，将当前每个时刻下剪切波在待测组织所有深度所有位置的运动参数从频域转换至时域。
  7. 根据权利要求4所述的方法，其特征在于，所述根据各深度对应的运动参数-时间二维数据图，获得剪切波在所述待测组织的每个深度中所述多个待测位置处的速度信息，包括：
    针对每个深度对应的运动参数-时间二维数据图，以所述二维数据图中的声辐射力焦点为中心，选取一定区域为待测区域，从所述待测区域中自适应确定当前的待测位置；
    以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个参考点，并基于最小二乘法线性拟合方法，对所述N个参考点对应的运动参数进行处理，获得剪切波在当前所述深度的所述待测位置的速度值，其中，N为预设的值，所述参考点包括所述二维数据图中剪切波的波峰和波谷；
    将与所述待测位置相邻的参考点所在的位置设定为当前的待测位置，并返回执行所述以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个参考点的步骤，直至获得剪切波在当前所述深度的所有位置的速度值。
  8. 根据权利要求7所述的方法，其特征在于，所述以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个参考点，并基于最小二乘法线性拟合方法，对所述N个参考点对应的运动参数进行处理，获得剪切波在当前所述深度的所述待测位置的速度值，包括：
    以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个所述参考点；
    依次将所述参考点中的M个相邻参考点进行基于最小二乘的线性拟合，获得相应的拟合直线集合，M小于N；
    将所述拟合直线集合中的第一拟合直线选择为目标拟合直线，所述参考点至所述第一拟合直线的残差最小；
    根据所述目标拟合直线，得到所述剪切波在当前所述深度的所述待测位置的速度值。
  9. 根据权利要求3-8中任一项所述的方法，其特征在于，所述根据所述速度信息计算所述硬度信息，包括：
    利用第一公式，计算所述硬度信息，所述第一公式为：E＝3ρV S 2，其中，E为所述硬度信息，ρ为所述待测组织的密度，V S为所述所述速度信息。
  10. 根据权利要求2所述的方法，其特征在于，所述根据所述多个待测位置在每一深度的硬度信息，生成硬度图，包括：
    对所述硬度信息进行二维中值滤波；
    根据进行二维中值滤波后的所述硬度信息，生成相应的硬度图。
  11. 一种用于检测组织硬度的设备，其特征在于，包括：
    获取模块，用于获取剪切波在多个待测位置随着时间沿深度方向传播的运动参数；
    计算模块，用于根据所述运动参数，计算所述多个待测位置在每一深度的硬度信息。
  12. 如权利要求11所述的设备，其特征在于，还包括：
    处理模块，用于根据所述多个待测位置在每一深度的硬度信息，生成硬度图；所述硬度图横轴为位置信息，纵轴为深度信息。
  13. 如权利要求11所述的设备，其特征在于，所述计算模块包括：
    速度子模块，用于根据所述运动参数计算剪切波在待测组织的每个深度中所述多个待测位置处的速度信息；
    硬度子模块，用于根据所述速度信息计算所述硬度信息。
  14. 根据权利要求13所述的设备，其特征在于，所述速度子模块包括：
    生成单元，用于根据所述运动参数，依次生成每个深度对应的运动参数-时间二维数据图，所述二维数据图表征在不同时刻下剪切波在当前深度的传播情况；
    分析单元，用于根据各深度对应的运动参数-时间二维数据图，获得剪切波在所述待测组织的每个深度中所述多个待测位置处的速度信息。
  15. 根据权利要求14所述的设备，其特征在于，所述速度子模块还包括：
    滤波单元，用于在所述生成单元根据所述运动参数，依次生成每个深度对应的运动参数-时间二维数据图之前，对每个时刻下剪切波在待测组织所有深度所有位置的运动参数进行方向滤波。
  16. 根据权利要求15所述的设备，其特征在于，所述滤波单元包括：
    变换子单元，用于通过进行快速傅里叶变换，将所述每个时刻下剪切波在待测组织所有深度所有位置的运动参数从时域转换至频域；
    划分子单元，用于以声辐射力焦点所在的位置为轴，将转换后的每个时刻下剪切波在待测组织所有深度所有位置的运动参数划分为两部分，每部分的频域数据分为四个象限，其中待过滤噪声对应的象限的数据赋为0，其它象限的数据不变；
    所述变换子单元，还用于通过进行快速傅里叶逆变换，将当前每个时刻下剪切波在待测组织所有深度所有位置的运动参数从频域转换至时域。
  17. 根据权利要求14所述的设备，其特征在于，所述分析单元包括：
    选取子单元，用于针对每个深度对应的运动参数-时间二维数据图，以所述二维数据图中的声辐射力焦点为中心，选取一定区域为待测区域，从所述待测区域中自适应确定当前的待测位置；
    拟合子单元，用于以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个参考点，并基于最小二乘法线性拟合方法，对所述N个参考点对应的运动参数进行处理，获得剪切波在当前所述深度的所述待测位置的速度值，其中，N为预设的值，所述参考点包括所述二维数据图中剪切波的波峰和波谷；
    更新子单元，用于将与所述待测位置相邻的参考点所在的位置设定为当前的待测位置，并指示所述拟合子单元执行所述以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个参考点的步骤，直至获得剪切波在当前所述深度的所有位置的速度值。
  18. 根据权利要求17所述的设备，其特征在于，所述拟合子单元包括：
    选取部件，用于以当前的待测位置为起点，在剪切波传播方向上向两侧或一侧选取N个所述参考点；
    拟合部件，用于依次将所述参考点中的M个相邻参考点进行基于最小二乘的线性拟合，获得相应的拟合直线集合，M小于N；
    所述拟合部件，还用于将所述拟合直线集合中的第一拟合直线选择为目标拟合直线，所述参考点至所述第一拟合直线的残差最小；
    计算部件，用于根据所述目标拟合直线，得到所述剪切波在当前所述深度的所述待测位置的速度值。
  19. 根据权利要求13-18中任一项所述的设备，其特征在于，所述硬度子模块包括：
    计算单元，用于利用第一公式，计算所述硬度信息，所述第一公式为：E＝3ρV S 2，其中，E为所述硬度信息，ρ为所述待测组织的密度，V S为所述所述速度信息。
  20. 根据权利要求12所述的设备，其特征在于，所述处理模块包括：
    滤波子模块，用于对所述硬度信息进行二维中值滤波；
    处理子模块，用于根据进行二维中值滤波后的所述硬度信息，生成相应的硬度图。
  21. 一种用于检测组织硬度的系统，其特征在于，包括：换能器阵列，以及如权利要求11-20中任一项所述的设备，所述换能器阵列与所述设备连接，所述换能器阵列对应于多个待测位置。
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