WO2017152629A1

WO2017152629A1 - Method and device for automatically triggering elasticity measurement

Info

Publication number: WO2017152629A1
Application number: PCT/CN2016/103517
Authority: WO
Inventors: 邵金华; 孙锦; 段后利; 王强
Original assignee: 无锡海斯凯尔医学技术有限公司
Priority date: 2016-03-10
Filing date: 2016-10-27
Publication date: 2017-09-14

Abstract

Provided are a method and device for automatically triggering elasticity measurement, said method for automatically triggering elasticity measurement comprising: receiving structure imaging information of a biological tissue (11); dividing said structure imaging information into regions to obtain a plurality of sub-regions, obtaining signal characteristics of each sub-region, and determining, according to said signal characteristics, whether the number of sub-regions satisfying preset conditions is greater than a preset numerical value (12); if so, then triggering the measurement of the elasticity of said biological tissue (13). The method achieves automatic triggering of elasticity measurement, avoiding a manual determination and improving accuracy in determining when to trigger elasticity measurement.

Description

Method and device for automatically triggering elastic detection

Technical field

The present invention relates to the field of medical devices, and more particularly to a method and apparatus for automatically triggering elastic detection.

Background technique

Non-destructive elastic testing of biological tissues is of great importance in the medical field, especially for the detection of liver diseases. Various chronic liver diseases (eg, viral hepatitis, alcoholic hepatitis, nonalcoholic steatohepatitis, autoimmune liver disease, etc.) will lead to liver fibrosis and cirrhosis, liver fibrosis and liver cirrhosis accompanied by liver elasticity The change, through non-destructive elasticity testing, can monitor and evaluate the condition of liver disease, so as to adopt a timely and effective treatment plan.

Existing elastic detecting devices typically include an ultrasonic transducer contact, a servo electric actuator capable of generating an instantaneous low frequency impact, and a pressure button for manually triggering the elastic detection. The medical staff judges whether to perform the elastic detection according to the experience. If necessary, the medical staff activates the elastic detecting device by pressing the pressure button, and the servo electric actuator emits the low-frequency vibration shear wave into the liver tissue, and the ultrasonic transducer contact transmits the ultrasonic wave for the ultrasonic transducer contact. The speed of shear wave propagation in the liver tissue was measured, and the elastic modulus of the liver was estimated in real time as a quantitative basis for the degree of liver fibrosis and cirrhosis.

However, the artificially triggered elastic detection is subjectively influenced by the medical staff, which may cause unnecessary elasticity detection and increase the burden on the patient, or may cause missed detection and delay the treatment of the patient. Therefore, the method of manually triggering the elastic detection reduces the accuracy of triggering the elastic detection.

Summary of the invention

The invention provides a method and a device for automatically triggering elastic detection, which can realize automatic triggering elastic detection, avoiding manual judgment and improving the accuracy of triggering elastic detection.

The method for automatically triggering elastic detection provided by the invention includes:

Receiving structural imaging information of biological tissue;

Performing area division on the structure imaging information to obtain a plurality of sub-areas, acquiring each sub-area a signal characteristic of the domain, determining, according to the signal characteristic, whether the number of sub-regions satisfying the preset condition is greater than a preset value;

If so, an elastic detection of the biological tissue is triggered.

The device for automatically triggering elastic detection provided by the invention comprises:

a receiving unit, a signal processor, a triggering unit, and an elastic detecting unit; the receiving unit is connected to the signal processor, the signal processor is connected to the triggering unit, and the triggering unit is connected to the elastic detecting unit;

The receiving unit is configured to receive structural imaging information of the biological tissue;

The signal processor is configured to perform area division on the structure imaging information to obtain a plurality of sub-regions, acquire signal features of each sub-region, and determine, according to the signal feature, whether the number of sub-regions satisfying the preset condition is greater than a preset value. ;

If yes, the triggering unit sends a triggering instruction to the elastic detecting unit, where the triggering instruction is used to instruct the elastic detecting unit to perform elastic detection on the biological tissue.

The present invention provides a method and apparatus for automatically triggering elastic detection, wherein the method for automatically triggering elastic detection comprises: receiving structural imaging information of a biological tissue, performing region division on the structural imaging information to obtain a plurality of sub-regions, and acquiring signals of each sub-region And determining, according to the signal feature, whether the number of sub-regions satisfying the preset condition is greater than a preset value, and if so, triggering elastic detection on the biological tissue. The method for automatically triggering elastic detection provided by the invention can realize automatic triggering elastic detection, avoiding manual judgment and improving the accuracy of triggering elastic detection.

DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description of the drawings used in the embodiments or the prior art description will be briefly described below. Obviously, the drawings in the following description It is a certain embodiment of the present invention, and other drawings can be obtained from those skilled in the art without any inventive labor.

1 is a flowchart of a method for automatically triggering elastic detection according to Embodiment 1 of the present invention;

2 is a flowchart of a method for automatically triggering elastic detection according to Embodiment 2 of the present invention;

3 is a flowchart of a method for automatically triggering elastic detection according to Embodiment 3 of the present invention;

4 is a flowchart of a method for automatically triggering elastic detection according to Embodiment 4 of the present invention;

FIG. 5 is a schematic structural diagram of an apparatus for automatically triggering elastic detection according to Embodiment 1 of the present invention; FIG.

FIG. 6 is a schematic structural diagram of an apparatus for automatically triggering elastic detection according to Embodiment 2 of the present invention.

detailed description

The technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. It is a partial embodiment of the invention, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

FIG. 1 is a flowchart of a method for automatically triggering elastic detection according to Embodiment 1 of the present invention. As shown in FIG. 1 , the method for automatically triggering the elastic detection provided in this embodiment may include:

Step 11. Receive structural imaging information of the biological tissue.

The structural imaging information may include a one-dimensional ultrasonic signal and/or a two-dimensional structural image, such as an A-super signal and an M-super signal, and the two-dimensional structure image may be a two-dimensional ultrasonic image, such as a B-ultrasound image. It may be a scanned image such as Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). This embodiment does not limit the type of structural imaging information.

Optionally, a specific implementation manner of this step may be:

The ultrasonic transducer that is ultrasonically imaged emits ultrasonic waves into the interior of the biological tissue to receive structural imaging information of the biological tissue.

Wherein, if it is a single ultrasonic transducer, a one-dimensional ultrasonic signal of the biological tissue can be obtained, and if it is a row of ultrasonic transducers, a two-dimensional ultrasonic image of the biological tissue can be obtained.

Step 12: Perform area division on the structure imaging information to obtain a plurality of sub-areas, acquire signal characteristics of each sub-area, and determine, according to the signal characteristics, whether the number of sub-areas satisfying the preset condition is greater than a preset value.

In this step, it is possible to automatically determine whether the biological tissue needs to be elastically detected. Specifically, the structural imaging information is subjected to block processing to acquire the signal characteristics of each sub-region, and then the signal characteristics are used to determine whether the biological tissue needs to be elastically detected. Biological organization needs The condition for performing the elasticity detection is that the number of sub-regions satisfying the preset condition is greater than a preset value. Among them, the preset conditions and preset values are set as needed.

Through this step, it is realized whether the biological tissue needs to be elastically detected, and the manual judgment mode is avoided. Since the experience of the medical staff and the subjective judgment of the medical staff are not required, the accuracy of triggering the elastic detection judgment is improved.

Optionally, the area of the structural imaging information is divided into a plurality of sub-areas, and the implementation may be: dividing the scanning depth corresponding to the structural imaging information into a plurality of sub-areas according to a preset spacing.

Optionally, the signal feature may include: a mean of the signal envelope in the sub-area and a standard deviation of the signal envelope.

The mean finger and the standard deviation can reflect whether the structural imaging information of the biological tissue has severe rapid jitter and large fluctuations. Whether the elastic detection is automatically triggered by the average finger and the standard deviation can improve the accuracy of the automatic triggering elastic detection.

Optionally, if the structural imaging information includes at least one one-dimensional ultrasonic signal, the signal characteristic may further include: an m value of a Nakagami distribution of the signal envelope in the sub-area.

Optionally, if the structural imaging information includes at least two one-dimensional ultrasonic signals, the signal characteristics may further include: an m value of a Nakagami distribution of the signal envelope in the sub-region, and the same scanning depth on any two one-dimensional ultrasonic signals. The number of correlations between signals between two sub-areas.

The cross-correlation coefficient of the signals between the two sub-regions may be the mutual conversion coefficient of the original signals between the two sub-regions, or may be the mutual conversion coefficient of the signal envelope between the two sub-regions, and the original signal may include Positive values can also include negative values. The signal envelope is obtained by processing the original signal through a signal, including only positive values.

Optionally, a specific implementation manner of this step may be:

Performing area division on at least one of the one-dimensional ultrasonic signal and the two-dimensional structure image to obtain a plurality of sub-regions, acquiring signal characteristics of each sub-region, and determining, according to the signal feature, whether the number of sub-regions satisfying the preset condition is greater than the preset Value.

It should be noted that, for different types of structural imaging information, the preset condition and the preset value may be set to be the same or may be set to be different.

Step 13. If yes, trigger the elastic detection of the biological tissue.

Among them, the elastic detection of the biological tissue can be carried out by any of the existing methods. Optionally, a specific implementation manner may include:

Shear waves are excited into biological tissues.

A characteristic parameter that detects the propagation of shear waves in biological tissues.

The viscoelastic parameters of the biological tissue are calculated according to the characteristic parameters.

Among them, there are various ways of exciting the shear wave, for example, applying a low-frequency instantaneous vibration to the outer surface of the biological tissue through the vibrator, or transmitting ultrasonic waves to the biological tissue through the ultrasonic transducer, or using the loudspeaker on the outer surface of the biological tissue. Sound waves. If the ultrasonic transducer emits ultrasonic waves to the biological tissue, the same ultrasonic transducer as the structurally imaged ultrasonic transducer can be used, or an ultrasonic transducer different from the structurally imaged ultrasonic transducer can be used.

Wherein, the characteristic parameter may include at least one of a propagation speed of the shear wave and a propagation attenuation coefficient of the shear wave.

Wherein, the viscoelastic parameter may include at least one of the following: shear modulus, Young's modulus, shear elasticity, shear viscosity, mechanical resistance, mechanical relaxation time, and anisotropy.

Optionally, after calculating the viscoelastic parameter of the biological tissue according to the characteristic parameter, the method may further include:

Imaging the viscoelastic parameters of biological tissue.

The viscoelastic parameters are imaged, and the viscoelastic parameters of the biological tissue can be mapped into grayscale or color images by using a color mapping algorithm. The color mapping algorithm can be any existing algorithm, such as gray mapping and color mapping. Wait.

The embodiment provides a method for automatically triggering elastic detection, comprising: receiving structural imaging information of a biological tissue, performing region division on the structural imaging information to obtain a plurality of sub-regions, acquiring signal characteristics of each sub-region, and judging satisfaction according to signal characteristics. Whether the number of conditional sub-regions is greater than a preset value, and if so, triggers elastic detection of biological tissue. The method for automatically triggering the elastic detection provided in this embodiment can automatically trigger the elastic detection, avoiding manual judgment, and improving the accuracy of triggering the elastic detection.

2 is a flowchart of a method for automatically triggering elastic detection according to Embodiment 2 of the present invention. On the basis of Embodiment 1, this embodiment provides an automatic triggering of elastic detection when structural imaging information includes a one-dimensional ultrasonic signal. A specific implementation of the method. As shown in FIG. 2, the method for automatically triggering the elastic detection provided in this embodiment may include:

Step 21: Receive structural imaging information of the biological tissue.

The structural imaging information includes a one-dimensional ultrasonic signal, such as an A-super signal.

Step 22: Divide the one-dimensional ultrasonic signal into a plurality of sub-regions S _i according to a preset interval z.

Where i is a sub-area identifier, i is greater than or equal to 1 and less than or equal to

d is the scan depth corresponding to the one-dimensional ultrasonic signal. The units of d and z are both millimeters.

In this step, since in the ultrasonic imaging, the bottom of the signal (ie, the deepest part of the scanning signal) generally does not contain the target to be detected, the information at the bottom of the signal can be ignored, so when the one-dimensional ultrasonic signal is divided into regions, the last A sub-area can be ignored, and the number of all sub-areas is obtained by rounding up, ie,

The following steps are described in detail by way of specific numerical examples. It is assumed that the scanning depth d corresponding to the one-dimensional ultrasonic signal is 20 mm, and the preset spacing z is 3 mm.

The one-dimensional ultrasonic signal can be divided into six sub-regions S ₁ to S ₆ , which are: S ₁ corresponds to 0 to 3 mm interval, S ₂ corresponds to 3 to 6 mm interval, S ₃ corresponds to 6 to 9 mm interval, and S ₄ corresponds to 9 to The 12mm interval, S ₅ corresponds to the 12-15mm interval, and S ₆ corresponds to the 15-18mm interval. The bottom of the one-dimensional ultrasonic signal (18-20mm interval) usually ignores the target to be detected.

Incidentally, each sub-area S _i corresponding to the depth of scanning, the scan depth may be the depth of the sub-regions refer to S _i, may be the depth value of the sub-terminal region of the S _i, and the embodiment is not limited thereto. For example: ₄ S 9 ~ 12mm interval corresponding to the S scan depth corresponding to ₄ may be (9 + 12) /2=10.5mm, may be 9mm, or may be 12mm.

Step 23: Acquire signal characteristics of each sub-area S _i .

The signal characteristics include: the average of the signal envelope in the sub-area, the standard deviation of the signal envelope, and the m-value of the Nakagami distribution of the signal envelope.

Among them, the Nakagami statistical model is a common model in signal processing. Under the Nakagami distribution, the probability density function of the signal envelope R can be expressed as:

Where Г(.) is a gamma function, Ω=E(R ² ), U(.) is a unit step function, m is a Nakagami distribution value, r is a dependent variable of the probability distribution function f(r), r≥ 0, m ≥ 0.

For the sub-region S _{_i,} m _i is the value of m in the sub-area S _{_i,} R _i is a value of the envelope within a sub-area S _{i-dimensional} ultrasound signal. The m value of the Nakagami distribution can be calculated from the following formula:

Where E(.) is the mean function.

Where, when the m value is in the range of (0, 1), the one-dimensional ultrasonic echo signal of the biological tissue obeys the pre-Rayleigh distribution; when the m value is equal to 1, the one-dimensional ultrasonic echo signal of the biological tissue obeys the Rayleigh distribution; the m value is greater than At 1 o'clock, the one-dimensional ultrasonic echo signal of the biological tissue obeys the post-Rayleigh distribution.

Step 24: sequentially determine whether the scan depth, the mean finger, the standard deviation, and the m value of the Nakagami distribution of each sub-area satisfy the second preset condition.

The second preset condition includes: the scan depth of the sub-area is within a preset scan depth range, and the sub-areas are all within a preset mean range, the standard deviation of the sub-areas is within a preset standard deviation range, and the sub-area The m value of the Nakagami distribution is within a preset m value range.

In this step, traversing each sub-region S _i ,

If sub-region S _i corresponds to d _i ∈ [d _lower , d _upper ], M _i ∈ [M _lower , M _upper ], SD _i ∈ [SD _lower , SD _upper ] and m _i ∈ [m _lower , m _upper ], Then, the sub-region S _i satisfies a second preset condition, wherein d _i , M _i , SD _i and m _i are respectively the scan depth, the mean finger, the standard deviation, and the m value of the Nakagami distribution of the sub-region S _i , d _lower and The d _upper is the upper and lower thresholds of the preset scanning depth range, respectively, M _lower and M _upper are the upper and lower thresholds of the preset mean range respectively, and SD _lower and SD _upper are the upper and lower thresholds of the preset standard deviation range, respectively. m _lower and m _upper are the upper and lower thresholds of the preset m value range, respectively.

Step 25: Determine whether the number of sub-regions satisfying the second preset condition is greater than a second preset value.

In this step, if the number of sub-regions satisfying the second preset condition is greater than the second preset value, it is considered that the biological tissue needs to be elastically detected, so that it is possible to automatically determine whether the biological tissue needs to be elastically detected.

The second preset value is set as needed.

Step 26, if yes, triggering an elastic detection of the biological tissue.

It should be noted that, in the method for automatically triggering the elastic detection provided in this embodiment, in step 23, the signal feature may include only the mean value of the signal envelope and the standard deviation of the signal envelope in the sub-area, and correspondingly, in step 24 The second preset condition includes: the scan depth of the sub-area is within a preset scan depth range, the sub-areas are all within a preset mean range, and the standard deviation of the sub-areas is within a preset standard deviation range.

This embodiment provides a method for automatically triggering elastic detection, and specifically provides a method for automatically triggering elastic detection when the structural imaging information includes a one-dimensional ultrasonic signal. The method for automatically triggering the elastic detection provided in this embodiment can automatically trigger the elastic detection, avoiding manual judgment, and improving the accuracy of triggering the elastic detection.

FIG. 3 is a flowchart of a method for automatically triggering elastic detection according to Embodiment 3 of the present invention. On the basis of Embodiment 1, the present embodiment provides an automatic triggering elasticity when the structural imaging information includes at least two one-dimensional ultrasonic signals. A specific implementation of the method of detection. As shown in FIG. 3, the method for automatically triggering the elastic detection provided in this embodiment may include:

Step 31: Receive structural imaging information of the biological tissue.

Wherein, the structural imaging information includes at least two one-dimensional ultrasonic signals, such as an M super-signal, and the M-super signal can be regarded as a dynamic representation of the A-super-signal over time.

Step 32: Divide at least two one-dimensional ultrasonic signals into a plurality of sub-regions T _jk according to a preset interval z.

Where j is a one-dimensional ultrasonic signal identifier, j is greater than or equal to 1 and less than or equal to G, G is the number of one-dimensional ultrasonic signals, and k is a sub-region identifier on each one-dimensional ultrasonic signal, and k is greater than or equal to 1 and less than or equal to

p _j is the scanning depth corresponding to the j-th one-dimensional ultrasonic signal. The units of p _j and z are both millimeters.

The division of each of the one-dimensional ultrasonic signals is similar to the division of the step 22 in the second embodiment, and details are not described herein again.

Step 33: Acquire signal features of each sub-region T _jk .

The signal characteristics include: the average of the signal envelope in the sub-area, the standard deviation of the signal envelope, the m-value of the Nakagami distribution of the signal envelope, and the two sub-areas at the same scanning depth on any two one-dimensional ultrasonic signals. The number of correlations between the signals.

The calculation of the m value of the Nakagami distribution is similar to the calculation of the step 23 in the second embodiment, and details are not described herein again.

The mutual relationship number c _jk is the number of correlations between the signals between the sub-region T _jk and the sub-region T _{(j+1) k} .

Step 34: sequentially determine whether the scanning depth, the mean finger, the standard deviation, the m value of the Nakagami distribution, and the cross-correlation coefficient of each sub-area satisfy the third preset condition.

The third preset condition includes: the scan depth of the sub-area is within a preset scan depth range Within the preset mean range, the standard deviation of the sub-area is within the preset standard deviation, the m-value of the Nakagami distribution of the sub-area is within the preset m-value range, and the cross-correlation of the sub-areas Within the range of preset cross-correlation.

In this step, traversing each sub-region T _jk , j=[1, G],

If d _jk子 [d _lower , d _upper ], M _jk ∈ [M _lower , M _upper ], SD _jk ∈ [SD _lower , SD _upper ], m _jk ∈ [m _lower , m _upper ] and c of the sub-region T _jk _Jk ∈[c _lower ,c _upper ], the sub-region T _jk satisfies a third preset condition, wherein d _jk , M _jk , SD _jk , m _jk , c _jk are the scanning depths of the sub-regions T _jk , respectively , standard deviation, m value and correlation coefficient of Nakagami distribution, d _lower and d _upper are the upper and lower thresholds of the preset scanning depth range respectively, and M _lower and M _upper are respectively upper and lower thresholds of the preset mean range, SD _Lower and SD _upper are the upper and lower thresholds of the preset standard deviation range, respectively, m _lower and m _upper are the upper and lower thresholds of the preset m value range, respectively, and c _lower and c _upper are respectively the preset cross-correlation range. Lower threshold.

Step 35: Determine that the one-dimensional ultrasonic signal whose number of sub-regions satisfying the third preset condition on the one-dimensional ultrasonic signal is greater than the third preset value is an effective one-dimensional ultrasonic signal.

Step 36: Determine whether the number of valid one-dimensional ultrasonic signals is greater than a fourth preset value.

Steps

35 and 36, if the number of sub-regions satisfying the third preset condition on each one-dimensional ultrasonic signal is greater than the third preset value, and the number of effective one-dimensional ultrasonic signals is greater than the fourth predetermined value, Biological tissue needs to be tested for elasticity, so it is possible to automatically determine whether the biological tissue needs to be elastically tested.

The third preset value and the fourth preset value are set as needed.

Step 37, if yes, triggering elastic detection of the biological tissue.

It should be noted that, in the method for automatically triggering the elastic detection provided in this embodiment, in step 33, the signal feature may include only the mean value of the signal envelope and the standard deviation of the signal envelope in the sub-area, and correspondingly, in step 34. The third preset condition includes: the scan depth of the sub-area is within a preset scan depth range, the sub-areas are all within a preset mean range, and the standard deviation of the sub-areas is within a preset standard deviation range. The signal feature may also include only the mean of the signal envelope in the sub-area, the standard deviation of the signal envelope, and the m-value of the Nakagami distribution of the signal envelope. Correspondingly, in step 34, the third preset condition includes: the sub-area The scanning depth is within the preset scanning depth range, the sub-regions are all within the preset mean range, the standard deviation of the sub-regions is within the preset standard deviation, and the m-value of the sub-region Nakagami distribution is at the preset m-value. Within the scope.

The embodiment provides a method for automatically triggering elastic detection, and specifically provides a method for automatically triggering elastic detection when the structural imaging information includes at least two one-dimensional ultrasonic signals. The method for automatically triggering the elastic detection provided in this embodiment can automatically trigger the elastic detection, avoiding manual judgment, and improving the accuracy of triggering the elastic detection.

4 is a flowchart of a method for automatically triggering elastic detection according to Embodiment 4 of the present invention. On the basis of Embodiment 1, the present embodiment provides a method for automatically triggering elastic detection when structural imaging information includes a two-dimensional structure image. A specific implementation. As shown in FIG. 4, the method for automatically triggering the elastic detection provided in this embodiment may include:

Step 41: Receive structural imaging information of the biological tissue.

The structural imaging information includes a two-dimensional structure image, such as a B-mode image, a CT image, an MRI image, and the like.

Step 42: Divide the two-dimensional structure image into a plurality of sub-regions V _xy according to a preset spacing z.

Where x is the sub-region identifier on the scanning depth of the two-dimensional structure image, and x is greater than or equal to 1 and less than or equal to

q is the scan depth corresponding to the two-dimensional structure image, y is the sub-region identifier on the width of the two-dimensional structure image, and y is greater than or equal to 1 and less than or equal to

w is the pixel value of the two-dimensional structure image in width, and h is the pixel value of the two-dimensional structure image at the scanning depth. The units of q and z are both millimeters.

Step 43: Acquire signal characteristics of each sub-area V _xy .

The signal characteristics include: the average of the signal envelope in the sub-area and the standard deviation of the signal envelope.

Step 44: sequentially determine whether the scan depth, the mean finger and the standard deviation of each sub-area satisfy the first preset condition.

The first preset condition includes: the scanning depth of the sub-area is within a preset scanning depth range, the sub-regions are all within a preset average range, and the standard deviation of the sub-areas is within a preset standard deviation range.

In this step, traversing each sub-region V _xy ,

If the sub-region V _xy corresponds to d _xy ∈ [d _lower , d _upper ], M _xy ∈ [M _lower , M _upper ] and SD _xy ∈ [SD _lower , SD _upper ], the sub-region V _xy satisfies the first preset condition. Where d _xy , M _{xy ,} and SD _xy are the scan depth, the mean and the standard deviation of the sub-region V _xy , respectively, d _lower and d _upper are the upper and lower thresholds of the preset scan depth range, respectively, M _lower and M _upper The upper and lower thresholds of the preset mean range, respectively, SD _lower and SD _upper are the upper and lower thresholds of the preset standard deviation range, respectively.

Step 45: Determine whether the number of sub-regions satisfying the first preset condition is greater than a first preset value.

In this step, if the number of sub-regions satisfying the first preset condition is greater than the first preset value, it is considered that the biological tissue needs to be elastically detected, so that it is possible to automatically determine whether the biological tissue needs to be elastically detected.

The first preset value is set as needed.

Step 46, if yes, triggering an elastic detection of the biological tissue.

It should be noted that the method for automatically triggering the elastic detection provided by this embodiment is also applicable to the case where the structural imaging information includes MRI or CT.

This embodiment provides a method for automatically triggering elastic detection, and specifically provides a method for automatically triggering elastic detection when structural imaging information includes a two-dimensional structural image. The method for automatically triggering the elastic detection provided in this embodiment can automatically trigger the elastic detection, avoiding manual judgment, and improving the accuracy of triggering the elastic detection.

FIG. 5 is a schematic structural diagram of an apparatus for automatically triggering elastic detection according to Embodiment 1 of the present invention. As shown in FIG. 5, the apparatus for automatically triggering the elastic detection provided by the embodiment for performing the automatic triggering elastic detection provided by the embodiment shown in FIG. 1 may include: a receiving unit 11, a signal processor 12, and a triggering unit. 13 and the elastic detecting unit 14, the receiving unit 11 is connected to the signal processor 12, the signal processor 12 is connected to the trigger unit 13, and the trigger unit 13 is connected to the elastic detecting unit 14.

The receiving unit 11 is configured to receive structural imaging information of the biological tissue.

The signal processor 12 is configured to perform area division on the structural imaging information to obtain a plurality of sub-areas, acquire signal characteristics of each sub-area, and determine, according to the signal characteristics, whether the number of sub-areas that satisfy the preset condition is greater than a preset value.

If so, the triggering unit 13 issues a triggering instruction to the elastic detecting unit 14 for instructing the elastic detecting unit 14 to perform elastic detection on the biological tissue.

Optionally, the structural imaging information includes at least one one-dimensional ultrasonic signal, and the signal characteristic may further include: an m value of a Nakagami distribution of the signal envelope in the sub-area.

Optionally, the structural imaging information includes at least two one-dimensional ultrasonic signals, and the signal characteristics may further include: an m value of a Nakagami distribution of the signal envelope in the sub-region, and two at the same scanning depth on any two one-dimensional ultrasonic signals. The number of correlations between signals between sub-areas.

The embodiment provides an apparatus for automatically triggering elastic detection, comprising: a receiving unit, a signal processor, a trigger unit and an elastic detecting unit, the receiving unit is connected with the signal processor, the signal processor is connected with the trigger unit, the trigger unit and the elastic The detection unit is connected. The device for automatically triggering the elastic detection provided by the embodiment can automatically trigger the elastic detection, avoiding manual judgment, and improving the accuracy of triggering the elastic detection.

FIG. 6 is a schematic structural diagram of an apparatus for automatically triggering elastic detection according to Embodiment 2 of the present invention. On the basis of Embodiment 1, another embodiment of an apparatus for automatically triggering elastic detection is provided. As shown in FIG. 6 , the apparatus for automatically triggering the elastic detection provided by the embodiment for performing the automatic triggering elastic detection provided by the embodiment shown in FIG. 1 to FIG. 4 may include: a receiving unit 11 and a signal processor 12 . The trigger unit 13 and the elastic detecting unit 14 are connected to the signal processor 12, the signal processor 12 is connected to the trigger unit 13, and the trigger unit 13 is connected to the elastic detecting unit 14.

The signal processor 12 includes a region dividing unit 121.

The area dividing unit 121 is configured to divide the scanning depth corresponding to the structural imaging information into a plurality of sub-areas according to a preset spacing.

Optionally, the area dividing unit 121 may include: a one-dimensional signal dividing unit 1211 and/or a two-dimensional image dividing unit 1212.

The one-dimensional signal dividing unit 1211 is configured to: if the structural imaging information includes a one-dimensional ultrasonic signal, divide the one-dimensional ultrasonic signal into a plurality of sub-regions S _i according to a preset spacing z; wherein i is a sub-region identifier, i is greater than or equal to 1 and less than or equal to

d is the scan depth corresponding to the one-dimensional ultrasonic signal; or

If the structural imaging information includes at least two one-dimensional ultrasonic signals, the at least two one-dimensional ultrasonic signals are divided into a plurality of sub-regions T _jk according to a preset interval z; wherein j is a one-dimensional ultrasonic signal identifier, and j is greater than or equal to 1 and Less than or equal to G, G is the number of one-dimensional ultrasonic signals, k is the sub-region identification on each one-dimensional ultrasonic signal, and k is greater than or equal to 1 and less than or equal to

p _j is the scanning depth corresponding to the j-th one-dimensional ultrasonic signal.

The two-dimensional image dividing unit 1212 is configured to: if the structural imaging information includes a two-dimensional structural image, divide the two-dimensional structural image into a plurality of sub-regions V _xy according to a preset spacing z; wherein x is a two-dimensional structure image scanning depth Sub-area identification, x is greater than or equal to 1 and less than or equal to

w is the pixel value of the two-dimensional structure image in width, and h is the pixel value of the two-dimensional structure image at the scanning depth.

Optionally, the signal processor 12 includes a first determining unit 122.

The first determining unit 122 is configured to sequentially determine whether the scan depth, the average finger, and the standard deviation of each sub-area meet the first preset condition, where the first preset condition includes: the scan depth of the sub-area is within a preset scan depth range, The sub-areas are all within the preset mean range, and the standard deviation of the sub-areas is within the preset standard deviation.

Determining whether the number of sub-regions satisfying the first preset condition is greater than a first preset value.

Optionally, the signal processor 12 includes a second determining unit 123 and/or a third determining unit 124.

The second determining unit 123 is configured to:

If the structural imaging information includes at least one one-dimensional ultrasonic signal, it is sequentially determined whether the scanning depth, the mean finger, the standard deviation, and the m value of the Nakagami distribution of each sub-region satisfy the second preset condition, and the second preset condition includes: the sub-region The scanning depth is within the preset scanning depth range, the sub-regions are all within the preset mean range, the standard deviation of the sub-regions is within the preset standard deviation, and the m-value of the sub-region Nakagami distribution is at the preset m-value. Within the scope.

Determining whether the number of sub-regions satisfying the second preset condition is greater than a second preset value.

The third determining unit 124 is configured to:

If the structural imaging information includes at least two one-dimensional ultrasonic signals, sequentially determining whether the scanning depth, the mean finger, the standard deviation, the m value of the Nakagami distribution, and the cross-correlation coefficient of each sub-region satisfy the third preset condition, and the third preset The conditions include: the scanning depth of the sub-area is within the preset scanning depth range, the sub-area refers to the preset average range, the standard deviation of the sub-area is within the preset standard deviation, and the m-value of the Nakagami distribution of the sub-area is Within the preset m-value range, and the cross-correlation coefficient of the sub-areas is within the preset cross-correlation range.

The one-dimensional ultrasonic signal that determines that the number of sub-regions satisfying the third preset condition on the one-dimensional ultrasonic signal is greater than the third preset value is an effective one-dimensional ultrasonic signal.

It is determined whether the number of effective one-dimensional ultrasonic signals is greater than a fourth preset value.

Optionally, the receiving unit 11 includes an ultrasonic transducer 111 and an ultrasonic transceiving unit 112. The ultrasonic transducer 111 is connected to the ultrasonic transceiving unit 112, and the ultrasonic transceiving unit 112 is connected to the signal processor 12. The ultrasonic transceiver unit 112 is configured to emit ultrasonic waves through the ultrasonic transducer 111 and receive structural imaging information of the biological tissue.

Optionally, the elastic detecting unit 14 includes: a shear wave exciting unit 141, and at least one of a vibrator 142, a microphone 143, and an ultrasonic transducer 144, wherein the vibrator 142, the microphone 143, and the ultrasonic wave At least one of the transducers 144 is connected to the shear wave excitation unit 141, and the shear wave excitation unit 141 is connected to the trigger unit 13. The shear wave excitation unit 141 is configured to perform elastic detection by transmitting shear waves to the biological tissue through at least one of the vibrator 142, the microphone 143, and the ultrasonic transducer 144 according to the trigger command.

Alternatively, the shear wave excitation unit 141 is connected to the ultrasonic transducer 111 in the receiving unit 11. The shear wave excitation unit 141 is configured to perform elastic detection by sending a shear wave to the biological tissue through the ultrasonic transducer 111 according to the trigger command.

Optionally, the ultrasonic transducers 111 are plural, and the plurality of ultrasonic transducers 111 are laterally arranged in a one-dimensional array, or the plurality of ultrasonic transducers 111 are horizontally and vertically arranged in a two-dimensional array.

Optionally, the means for automatically triggering the elastic detection may further comprise an imaging unit (not shown) coupled to the signal processor 12 for imaging the viscoelastic parameters of the biological tissue.

Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, and are not intended to be limiting; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that The technical solutions described in the foregoing embodiments may be modified, or some or all of the technical features may be equivalently replaced; and the modifications or substitutions do not deviate from the technical solutions of the embodiments of the present invention. range.

Claims

A method for automatically triggering elastic detection, comprising:

Receiving structural imaging information of biological tissue;

Performing area division on the structure imaging information to obtain a plurality of sub-areas, acquiring signal characteristics of each sub-area, and determining, according to the signal characteristics, whether the number of sub-areas satisfying the preset condition is greater than a preset value;

If so, an elastic detection of the biological tissue is triggered.
The method according to claim 1, wherein the zoning the structural imaging information to obtain a plurality of sub-regions comprises:

The scanning depth corresponding to the structural imaging information is divided into a plurality of sub-regions according to a preset pitch.
The method according to claim 2, wherein the structural imaging information comprises a one-dimensional ultrasound signal, and the scanning depth corresponding to the imaging information of the structure is divided into a plurality of sub-regions according to a preset spacing, including:

Dividing the one-dimensional ultrasonic signal into a plurality of sub-regions S i according to a preset interval z; wherein i is a sub-region identifier, i is greater than or equal to 1 and less than or equal to
d is the scan depth corresponding to the one-dimensional ultrasonic signal;

or,

The structure imaging information includes at least two one-dimensional ultrasonic signals, and the scanning depth corresponding to the structural imaging information is divided into a plurality of sub-regions according to a preset pitch, including:

Dividing the at least two one-dimensional ultrasonic signals into a plurality of sub-regions T jk according to a preset interval z; wherein j is a one-dimensional ultrasonic signal identifier, j is greater than or equal to 1 and less than or equal to G, and G is a number of one-dimensional ultrasonic signals , k is the sub-area identification on each one-dimensional ultrasonic signal, k is greater than or equal to 1 and less than or equal to
p j is the scanning depth corresponding to the j-th one-dimensional ultrasonic signal;

or,

The structure imaging information includes a two-dimensional structure image, and the scanning depth corresponding to the imaging information of the structure is divided into a plurality of sub-areas according to a preset spacing, including:

Dividing the two-dimensional structure image into a plurality of sub-regions V xy according to a preset spacing z; wherein x is a sub-region identifier on a scanning depth of the two-dimensional structure image, and x is greater than or equal to 1 and less than or equal to
q is the scan depth corresponding to the two-dimensional structure image, y is the sub-region identifier on the width of the two-dimensional structure image, and y is greater than or equal to 1 and less than or equal to
w is the pixel value of the two-dimensional structure image in width, and h is the pixel value of the two-dimensional structure image at the scanning depth.
The method according to any one of claims 1 to 3, wherein the signal characteristic comprises: a mean value of a signal envelope and a standard deviation of a signal envelope in the sub-area.
The method according to claim 4, wherein said structural imaging information comprises at least one one-dimensional ultrasonic signal, said signal characteristic further comprising: an m value of a Nakagami distribution of a signal envelope in said sub-region;

or,

The structural imaging information includes at least two one-dimensional ultrasonic signals, the signal characteristics further including: an m value of a Nakagami distribution of signal envelopes in the sub-region, and at the same scanning depth on any two one-dimensional ultrasonic signals The number of correlations between signals between two sub-areas.
The method according to claim 4, wherein the determining, according to the signal characteristic, whether the number of sub-regions satisfying the preset condition is greater than a preset value comprises:

Determining, in sequence, whether the scan depth of each sub-area, the average finger, and the standard deviation meet the first preset condition, where the first preset condition includes: the scan depth of the sub-area is within a preset scan depth range, and the sub-area Means within the preset mean range, and the standard deviation of the sub-areas is within the preset standard deviation;

Determining whether the number of sub-regions satisfying the first preset condition is greater than a first preset value.
The method according to claim 5, wherein the structural imaging information comprises at least one one-dimensional ultrasonic signal, and the determining, according to the signal characteristic, whether the number of sub-regions satisfying the preset condition is greater than a preset value, including :

Determining, in sequence, whether the scan depth of each sub-area, the mean finger, the standard deviation, and the m value of the Nakagami distribution satisfy a second preset condition, where the second preset condition includes: the scan depth of the sub-area is in advance Within the scan depth range, the sub-regions are all within the preset mean range, the standard deviation of the sub-regions is within the preset standard deviation, and the m-value of the Nakagami distribution of the sub-regions is within the preset m-value range;

Determining whether the number of sub-regions satisfying the second preset condition is greater than a second preset value;

Or,

The structure imaging information includes at least two one-dimensional ultrasonic signals, and determining, according to the signal characteristics, whether the number of sub-regions satisfying the preset condition is greater than a preset value, including:

Determining, in sequence, the scanning depth of each sub-area, the mean finger, the standard deviation, the m value of the Nakagami distribution, and the correlation value satisfying a third preset condition, where the third preset condition includes: The scan depth of the region is within the preset scan depth range, and the sub-regions are all within the preset mean range. The standard deviation of the sub-region is within the preset standard deviation, and the m-value of the Nakagami distribution of the sub-region is at the preset m-value. Within the range, and the number of cross-correlations of the sub-areas within the preset cross-correlation range;

Determining that the one-dimensional ultrasonic signal on the one-dimensional ultrasonic signal that the number of sub-regions satisfying the third preset condition is greater than the third preset value is an effective one-dimensional ultrasonic signal;

It is determined whether the number of the effective one-dimensional ultrasonic signals is greater than a fourth preset value.
The method according to any one of claims 1 to 3, wherein the structural imaging information comprises at least one of a one-dimensional ultrasound signal and a two-dimensional structure image;

Performing area division on the structure imaging information to obtain a plurality of sub-areas, acquiring a signal feature of each sub-area, and determining, according to the signal feature, whether the number of sub-areas that meet the preset condition is greater than a preset value, including:

Performing area division on at least one of the one-dimensional ultrasonic signal and the two-dimensional structure image to obtain a plurality of sub-regions, acquiring signal characteristics of each sub-region, and determining, according to the signal feature, whether the number of sub-regions satisfying the preset condition is greater than the preset Value.
An apparatus for automatically triggering elastic detection, comprising: a receiving unit, a signal processor, a triggering unit, and an elastic detecting unit; the receiving unit is connected to the signal processor, the signal processor and the trigger Units are connected, and the trigger unit is connected to the elastic detecting unit;

The receiving unit is configured to receive structural imaging information of the biological tissue;

The signal processor is configured to perform area division on the structure imaging information to obtain a plurality of sub-regions, acquire signal features of each sub-region, and determine, according to the signal feature, whether the number of sub-regions satisfying the preset condition is greater than a preset value. ;

If yes, the triggering unit sends a triggering instruction to the elastic detecting unit, where the triggering instruction is used to instruct the elastic detecting unit to perform elastic detection on the biological tissue.
The apparatus of claim 9 wherein said signal processor comprises Area division unit;

The area dividing unit is configured to divide the scanning depth corresponding to the structural imaging information into a plurality of sub-areas according to a preset spacing.
The apparatus according to claim 10, wherein the area dividing unit comprises: a one-dimensional signal dividing unit and/or a two-dimensional image dividing unit;

The one-dimensional signal dividing unit is configured to: if the structural imaging information includes a one-dimensional ultrasonic signal, divide the one-dimensional ultrasonic signal into a plurality of sub-regions S i according to a preset spacing z; wherein i is a sub-region Identification, i is greater than or equal to 1 and less than or equal to
d is the scan depth corresponding to the one-dimensional ultrasonic signal; or

If the structural imaging information includes at least two one-dimensional ultrasonic signals, the at least two one-dimensional ultrasonic signals are divided into a plurality of sub-regions T jk according to a preset interval z; wherein j is a one-dimensional ultrasonic signal identifier, j Greater than or equal to 1 and less than or equal to G, G is the number of one-dimensional ultrasonic signals, k is a sub-region identification on each one-dimensional ultrasonic signal, and k is greater than or equal to 1 and less than or equal to
p j is the scanning depth corresponding to the j-th one-dimensional ultrasonic signal;

The two-dimensional image dividing unit is configured to: if the structural imaging information includes a two-dimensional structural image, divide the two-dimensional structural image into a plurality of sub-regions V xy according to a preset spacing z; wherein x is a two-dimensional structure Sub-area identification on the image scanning depth, x is greater than or equal to 1 and less than or equal to
q is the scan depth corresponding to the two-dimensional structure image, y is the sub-region identifier on the width of the two-dimensional structure image, and y is greater than or equal to 1 and less than or equal to
w is the pixel value of the two-dimensional structure image in width, and h is the pixel value of the two-dimensional structure image at the scanning depth.
The apparatus according to any one of claims 9 to 11, wherein said signal characteristic comprises: a mean value of a signal envelope and a standard deviation of a signal envelope in said sub-area.
The apparatus according to claim 12, wherein said structural imaging information comprises at least one one-dimensional ultrasonic signal, said signal characteristic further comprising: an m value of a Nakagami distribution of a signal envelope in said sub-region;

or,

The structural imaging information includes at least two one-dimensional ultrasonic signals, the signal characteristics further including: an m value of a Nakagami distribution of signal envelopes in the sub-region, and at the same scanning depth on any two one-dimensional ultrasonic signals The number of correlations between signals between two sub-areas.
The apparatus of claim 12 wherein said signal processor package Including a first determining unit;

The first determining unit is configured to sequentially determine whether the scanning depth of each sub-area, the average finger, and the standard deviation meet the first preset condition, where the first preset condition includes: the scanning depth of the sub-area is Within the preset scan depth range, the sub-areas are all within the preset mean range, and the standard deviation of the sub-areas is within the preset standard deviation range;

Determining whether the number of sub-regions satisfying the first preset condition is greater than a first preset value.
The apparatus according to claim 13, wherein said signal processor comprises a second determining unit and/or a third determining unit;

The second determining unit is configured to:

If the structural imaging information includes at least one one-dimensional ultrasonic signal, sequentially determining whether the scanning depth of each sub-region, the average finger, the standard deviation, and the m value of the Nakagami distribution satisfy a second preset condition, The second preset condition includes: the scan depth of the sub-area is within a preset scan depth range, the sub-areas are all within a preset mean range, the standard deviation of the sub-areas is within a preset standard deviation range, and the sub-area The m value of the Nakagami distribution is within a preset m value range;

Determining whether the number of sub-regions satisfying the second preset condition is greater than a second preset value;

The third determining unit is configured to:

If the structural imaging information includes at least two one-dimensional ultrasonic signals, sequentially determining whether the scanning depth of each sub-region, the average finger, the standard deviation, the m value of the Nakagami distribution, and the correlation coefficient satisfy a third preset condition, the third preset condition includes: the scan depth of the sub-area is within a preset scan depth range, and the sub-area refers to the preset mean value range, and the standard deviation of the sub-area is at a preset standard deviation Within the range, the m value of the Nakagami distribution of the sub-region is within a preset m-value range, and the cross-correlation coefficient of the sub-region is within a preset cross-correlation range;

Determining that the one-dimensional ultrasonic signal on the one-dimensional ultrasonic signal that the number of sub-regions satisfying the third preset condition is greater than the third preset value is an effective one-dimensional ultrasonic signal;

It is determined whether the number of the effective one-dimensional ultrasonic signals is greater than a fourth preset value.