KR102303922B1 - Method for estimating bone mineral density and bone structure using ultrasonic attenuation coefficient and phase velocity - Google Patents

Abstract

A measuring step of irradiating a transmitting ultrasound wave to the cancellous bone positioned between a pair of ultrasound transducers, and receiving the receiving ultrasound wave passing through the spongy bone by the other transducer; a first analysis step of analyzing the electrical signal of the received ultrasonic wave received in the measuring step to calculate an attenuation coefficient and a phase speed for a frequency region in the range of 0.2 MHz to 0.6 MHz; a second analysis step of deriving a regression equation by performing nonlinear regression analysis between each of the attenuation coefficient and phase velocity calculated in the first analysis step and the ultrasonic frequency, and extracting the coefficient of the derived regression equation as a variable; and applying the variable extracted in the second analysis step as an independent variable to a multiple regression model in which any one of the bone density and bone structure of the cancellous bone is a dependent variable and at least two or more variables is an independent variable, so that the bone density of the cancellous bone and a prediction step of predicting the bone structure; It provides a method for predicting bone density and bone structure using an ultrasonic damping coefficient and phase velocity, including a.

Description

Bone density and bone structure prediction method using ultrasonic attenuation coefficient and phase velocity {METHOD FOR ESTIMATING BONE MINERAL DENSITY AND BONE STRUCTURE USING ULTRASONIC ATTENUATION COEFFICIENT AND PHASE VELOCITY}

The present invention relates to a method for predicting bone density and bone structure using an ultrasonic damping coefficient and phase velocity.

Osteoporosis is defined as a systemic bone disease in which fractures easily occur even with a small impact due to a decrease in bone mass and destruction of bone structure. is becoming

At this time, since osteoporosis is an irreversible disease that cannot be returned to a normal state once it is onset, early diagnosis and prevention by diagnosis are important. There is bone density measurement using dual energy X-ray absorptiometry (DEXA).

However, since measurement of bone density for the diagnosis of osteoporosis requires repeated measurement not only for the diagnosis of osteoporosis but also for evaluation of treatment response, there is a risk that the dual energy X-ray absorptiometry using radiation requires repeated exposure to radiation. In particular, it was difficult to measure bone density in pregnant women.

Therefore, research on a method for diagnosing osteoporosis that is harmless to the human body is being conducted from various angles, and a quantitative ultrasound (QUS) technology for diagnosing osteoporosis using ultrasound has been proposed as part of this research.

Here, the quantitative ultrasound technology irradiates ultrasound to the heel bone of the human body, that is, the calcaneus, and measures ultrasound characteristics such as the speed of sound (SOS) and broadband ultrasound attenuation (BUA) of the calcaneus. By measuring, the bone mineral density (BMD) of the whole body is indirectly predicted. At this time, osteoporosis can be diagnosed by the predicted bone density.

Compared with the method of diagnosing osteoporosis using radiation, this quantitative ultrasound technology has the advantage that there is no risk of radiation exposure even in repeated examinations, and the price and examination cost of the diagnostic device are relatively low. -2005-0038812 (application date: October 23, 2003, publication date: 04. 29. 2005), Republic of Korea Patent Publication No. 10-2003-0034550 (file date: October 26, 2001, publication date: 2004. 06. 11.), etc. have been suggested.

On the other hand, even with the same bone density, the incidence of fractures differs depending on the age difference, fracture-related history, steroid drug treatment, and the difference in the spongy bone trabecular interval and bone volume ratio of the human bone structure. In addition, there is a need to consider changes in bone structure together.

However, since the conventional quantitative ultrasound technology measures only the change in bone density for the diagnosis of osteoporosis, it is difficult to identify and diagnose the cause of osteoporosis caused by the change in bone structure.

An object of the present invention is to provide a technique capable of predicting bone density and bone structure together through ultrasound measurement and improving the accuracy of diagnosis of osteoporosis from the predicted result in order to solve the above problems.

In order to achieve this object, in the method for predicting bone density and bone structure using ultrasound attenuation coefficient and phase velocity according to an embodiment of the present invention, any one transducer is located in the spongy bone located between a pair of ultrasound transducers. A measuring step of irradiating the transmitting ultrasound, the other transducer receiving the receiving ultrasound that has passed through the spongy bone; a first analysis step of analyzing the electrical signal of the received ultrasonic wave received in the measuring step to calculate an attenuation coefficient and a phase speed for a frequency domain ranging from 0.2 MHz to 0.6 MHz; a second analysis step of deriving a regression equation by performing nonlinear regression analysis between each of the attenuation coefficient and phase velocity calculated in the first analysis step and the ultrasonic frequency, and extracting the coefficient of the derived regression equation as a variable; and applying the variable extracted in the second analysis step as an independent variable to a multiple regression model in which any one of the bone density and bone structure of the cancellous bone is a dependent variable and at least two or more variables is an independent variable, so that the bone density of the cancellous bone And a prediction step of predicting the bone structure; Including, in the first analysis step, the attenuation coefficient is calculated through Equation 1 below, and the phase speed is calculated through Equation 2 below.

[Equation 1]

[dB/cm]는 해면질골의 감쇠계수, d는 해면질골의 두께,

및

는 한 쌍의 초음파 트랜스듀서 사이에 해면질골이 없는 경우와 있는 경우에 각각 수신된 신호의 파워스펙트럼레벨)(here,

[dB/cm] is the attenuation coefficient of spongy bone, d is the thickness of spongy bone,

and

is the power spectrum level of the received signal when there is no cancellous bone between the pair of ultrasonic transducers, respectively)

[Equation 2]

[m/s]는 해면질골의 위상속도,

는 주파수, d는 해면질골의 두께,

는 온도에 의존하는 수중에서의 음속(1482m/s, 20℃),

는 한 쌍의 초음파 트랜스듀서 사이에 해면질골이 없는 경우와 있는 경우에 각각 수신된 신호의 위상차) (here,

[m/s] is the phase velocity of cancellous bone,

is the frequency, d is the thickness of the spongy bone,

is the temperature-dependent speed of sound in water (1482 m/s, 20 °C),

is the phase difference of the received signal between a pair of ultrasonic transducers in the absence and presence of spongy bone)

(이하, ‘제1 변수’라 칭함) 및

(이하, ‘제2 변수’라 칭함)을 변수로 추출하는 추출단계; 를 포함하는 것을 특징으로 한다.Here, in the second analysis step, the following equations 3 and 4, which are regression equations, are derived through nonlinear regression analysis between the ultrasonic frequency and any one of the attenuation coefficient and the phase velocity calculated in the first analysis step. step; And through Equations 3 and 4 derived in the derivation step

(hereinafter referred to as the 'first variable') and

an extraction step of extracting (hereinafter referred to as a 'second variable') as a variable; It is characterized in that it includes.

[Equation 3]

[dB/cm]는 초음파 감쇠계수,

는 y절편,

[

]은 계수, f는 주파수, n은 지수)(here,

[dB/cm] is the ultrasonic attenuation coefficient,

is the y-intercept,

[

] is the coefficient, f is the frequency, n is the exponent)

[Equation 4]

[m/s]는 위상속도,

는 y절편,

[

]은 계수, n은 지수) (here,

[m/s] is the phase velocity,

is the y-intercept,

[

] is the coefficient, n is the exponent)

In this case, the bone structure of the cancellous bone is characterized in that it comprises at least one of a bone volume ratio (BT/TV), a trabecular thickness (Tb.Th), and a trabecular distance (Tb.Sp) of the cancellous bone.

In addition, the first variable and the second variable measured in advance from a plurality of cancellous bone samples are used as independent variables, and any one of the bone density and bone structure of the cancellous bone sample is used as a dependent variable, and a plurality of independent variables and each dependent variable are used. a multiple regression model generation step of performing multiple regression analysis of variables and obtaining a regression equation derived therefrom as a multiple regression model; It is characterized in that it includes.

And, an output step of outputting the results predicted in the bone density and bone structure prediction step; characterized in that it further comprises.

As described above, according to the present invention, there are the following effects.

First, the present invention can predict bone density and bone structure by providing a multiple regression model using two new ultrasound variables as independent variables in addition to the conventional parameters presented for bone density prediction.

Second, by providing bone density and bone structure prediction results through new ultrasound parameters in addition to conventional parameters, it is possible to improve the accuracy of osteoporosis diagnosis compared to the method of measuring bone density using only sound velocity and broadband ultrasound attenuation.

Third, in the present invention, by evaluating bone density and bone structure together, it is possible to identify and diagnose the cause of osteoporosis caused by changes in bone structure as well as changes in bone density.

Fourth, since the present invention diagnoses osteoporosis by irradiating ultrasound, it is possible to provide a safe measurement method to the measurer because there is no risk of the measurer being exposed to radiation.

1 shows an apparatus for predicting bone density and bone structure using an ultrasonic damping coefficient and a phase velocity according to an embodiment of the present invention.
2 is a cross-sectional view illustrating a bone structure.
3 is a flowchart illustrating a method for predicting bone density and bone structure using an ultrasonic damping coefficient and a phase velocity according to an embodiment of the present invention.
4 is a frequency domain of 0.2 MHz to 0.6 MHz calculated in the first analysis step according to an embodiment of the present invention. It is a graph showing (a) attenuation coefficient and (b) phase velocity.
5 is a method for predicting bone density and bone structure using an ultrasonic damping coefficient and a phase velocity according to an embodiment of the present invention. As an example to explain the reliability of

A preferred embodiment of the present invention will be described in more detail with reference to the accompanying drawings, but already known technical parts will be omitted or compressed for the sake of brevity of description.

<Description of the apparatus for predicting bone density and bone structure using ultrasonic damping coefficient and phase velocity>

1 shows an apparatus for predicting bone density and bone structure (hereinafter, referred to as 'bone density and bone structure prediction apparatus') using an ultrasonic damping coefficient and phase velocity according to an embodiment of the present invention, and FIG. 2 is a bone structure It is an illustrative cross-sectional view.

1, the apparatus 100 for predicting bone density and bone structure according to an embodiment of the present invention includes a pair of ultrasound transducers 110 (hereinafter referred to as 'transducers'), a waveform generator 120, and analysis It is configured to include a unit 130 , a prediction unit 140 , and an output unit 150 .

The pair of transducers 110 may convert an applied electrical signal into an ultrasonic wave or convert a received ultrasonic wave into an electrical signal. is converted into ultrasonic waves and operates as the transmitter 111 for transmitting the ultrasonic waves, and the other transducer operates as the receiver 112 for converting the ultrasonic waves into electrical signals.

Here, the pair of transducers 110 are positioned so that one surface for transmitting and receiving ultrasonic waves faces, and the pair of transducers 110 are spaced apart from each other by a predetermined distance.

In this case, an object to be measured may be positioned between the pair of ultrasound transducers 110 , and a part of the human body for which the bone density and bone structure are to be determined may be positioned. At this time, in the apparatus 100 for predicting bone density and bone structure according to an embodiment of the present invention, a trabecular bone (T) may be located, but is not limited thereto.

Here, as shown in Fig. 2, the cancellous bone (T) is a bone tissue constituting the bones of the human body together with cortical bone (C) having a very dense structure. Most of the metabolism occurs in the cancellous bone (T), and the bone density of the cancellous bone (T) is currently measured as an index for diagnosing osteoporosis in clinical practice.

And, a pair of transducers 110 of the apparatus 100 for predicting bone density and bone structure according to an embodiment of the present invention may be installed in water. In this case, water has acoustic properties similar to those of soft tissues of the human body, and is used as an ultrasonic wave propagation medium. For example, the water may be distilled water.

The waveform generator 120 generates an electrical signal for the transmitted ultrasonic wave and provides it to the transmitter 111 , and the electrical signal generated by the waveform generator 120 may be in the form of a pulse or continuous wave. At this time, in an embodiment of the present invention, an electrical signal for generating a transmission ultrasonic wave in the form of a pulse is generated and provided to the transmission unit 111 .

The analysis unit 130 analyzes the electrical signal of the received ultrasonic wave received by the receiving unit 112 and calculates an attenuation coefficient and a phase velocity for a frequency region in the range of 0.2 MHz to 0.6 MHz, and the calculated attenuation coefficient and phase velocity, respectively A regression equation may be derived by performing a nonlinear regression analysis between the and ultrasonic frequency, and coefficients of the derived regression equation may be extracted as variables. At this time, the analysis unit 130 may perform a signal processing process such as amplification and filtering in the process of analyzing the electrical signal of the received ultrasonic wave.

The prediction unit 140 uses the variable extracted from the analysis unit 130 as an independent variable in a multiple regression model in which any one of bone density and bone structure of the cancellous bone (T) is a dependent variable, and at least two or more variables are independent variables. It is applied to predict the bone density and bone structure of the cancellous bone.

At this time, as shown in Figure 2, the bone structure of cancellous bone is a bone volume ratio (bone volume/tissue volume, BT/TV), which is expressed as the ratio of the bone volume occupied by the bone trabeculae (Tb) to the tissue volume, and the trabecular thickness ; Tb.Th) and at least one of trabecular spacing (Tb.Sp), which means the spacing between the trabecular trabeculae (Tb).

In addition, the multiple regression model included in the prediction unit 140 may include results of measurements made when not only the spongy bone (T) but also the cortical bone (C) and soft tissue are included.

A detailed description of the prediction of bone density and bone structure as well as the variable extraction performed by the analysis unit 130 and the prediction unit 140 described above will be mentioned in the method for predicting bone density and bone structure using the ultrasonic damping coefficient and phase velocity to be described later. .

In this case, from the variables derived through ultrasound measurement, the bone density and bone structure corresponding to the variables may be derived through the multiple regression model included in the prediction unit 140 , and the predicted osteoporosis diagnosis result may be predicted therefrom.

For reference, the multiple regression model uses a plurality of variables extracted from a plurality of pre-measured cancellous bone samples as independent variables, and any one of bone density and bone structure of the cancellous bone sample as a dependent variable, As a regression equation derived by performing multiple regression analysis with each dependent variable, it may be pre-stored in the predictor 140 .

The output unit 150 includes any one of the bone density predicted from the prediction unit 140 and the bone volume ratio (BT/TV) of the cancellous bone, the trabecular trabecular thickness (Tb.Th) and the trabecular trabecular interval (Tb.Sp) Bone structure can be printed. At this time, the output unit 150 may output the osteoporosis diagnosis result predicted from the bone density and bone structure derived through the measurement together.

In this case, the output unit 150 may be provided as a display monitor, a sound reproducing device, a printing device, etc. to provide the above-described output contents in at least one of an image, a sound, and a print.

<Description of the method for predicting bone density and bone structure using ultrasonic damping coefficient and phase velocity>

3 is a flowchart illustrating a method for predicting bone density and bone structure using ultrasound attenuation coefficient and phase velocity according to an embodiment of the present invention, and FIG. 4 is 0.2 calculated in the first analysis step according to an embodiment of the present invention. In the frequency range from MHz to 0.6 MHz (a) is a graph showing the attenuation coefficient and (b) the phase velocity, and FIG. 5 is a first, new parameter, in the method for predicting bone density and bone structure using the ultrasonic damping coefficient and phase velocity according to an embodiment of the present invention. As an example to explain the reliability of bone density and bone structure predicted from the multiple regression model to which the variable and the second variable are applied, this table compares the coefficients of determination of the simple regression model and the multiple regression model.

1. Measurement step <S100>

In the measuring step (S100), any one of the transducers (111, hereinafter, referred to as a 'transmitter') irradiates the transmission ultrasound to the spongy bone (T) located between the pair of ultrasound transducers 110, and the other of the transducer (112, hereinafter referred to as a 'receiver') is a step of receiving the receiving ultrasound that has passed through the spongy bone (T).

At this time, the transmission ultrasound may be provided in the form of a pulse, and when the waveform generating unit 120 transmits an electrical signal for the pulse wave to the transmitting unit 111 , the transmitting unit 111 converts the electrical signal into an ultrasound signal and pulses Transmission ultrasound in the form of a pulse is generated, and the transmitted ultrasound in the form of a pulse is transmitted through the spongy bone (T). Thereafter, the ultrasonic wave passing through the spongy bone (T) is received by the receiver 112, the receiver 112 converts the received ultrasonic wave into an electrical signal.

Here, the transmitter 111 and the receiver 112 are positioned so that one side of the transmitter and receiver faces to each other, and the distance between the transmitter 111 and the receiver 112 may be determined by the near field of the transducer 110. .

In this case, the transmitter 111 and the receiver 112 may use a non-focused transducer having a diameter of 25.4 mm and a center frequency of 0.5 MHz, but is not limited thereto.

For reference, the near sound field refers to the point at which the beam width of the ultrasound is the narrowest when ultrasound is generated through the non-focused transducer, and the spongy bone has a center that coincides with the near field length (NFD) of the transmitter 111. placed at the point.

2. First analysis step <S200>

The analysis step (S200) is a step of analyzing the electrical signal of the received ultrasound received in the measurement step (S100) to calculate the attenuation coefficient and the phase speed for the frequency region in the range of 0.2 MHz to 0.6 MHz.

At this time, the attenuation coefficient of the spongy bone is calculated through Equation 1 below.

[Equation 1]

[dB/cm]는 해면질골의 감쇠계수, d는 해면질골의 두께,

및

는 한 쌍의 초음파 트랜스듀서 사이에 해면질골이 없는 경우와 있는 경우에 각각 수신된 신호의 파워스펙트럼레벨)(here,

[dB/cm] is the attenuation coefficient of spongy bone, d is the thickness of spongy bone,

and

is the power spectrum level of the received signal when there is no cancellous bone between the pair of ultrasonic transducers, respectively)

And, the phase velocity of the spongy bone is calculated through Equation 2 below.

[Equation 2]

[m/s]는 해면질골의 위상속도,

는 주파수, d는 해면질골의 두께,

는 온도에 의존하는 수중에서의 음속(1482m/s, 20℃),

는 한 쌍의 초음파 트랜스듀서 사이에 해면질골이 없는 경우와 있는 경우에 각각 수신된 신호의 위상차) (here,

[m/s] is the phase velocity of cancellous bone,

is the frequency, d is the thickness of the spongy bone,

is the temperature-dependent speed of sound in water (1482 m/s, 20 °C),

is the phase difference of the received signal between a pair of ultrasonic transducers in the absence and presence of spongy bone)

3. Second analysis step <S300>

The second analysis step (S300) derives a regression equation by performing a nonlinear regression analysis between the ultrasonic frequency and each of the attenuation coefficient and phase velocity calculated in the first analysis step (S300), and extracts the coefficient of the derived regression equation as a variable is a step to

At this time, the second analysis step (S300) includes a derivation step (S310) and an extraction step (S320).

Here, the derivation step (S310) is to derive the following equations (3) and (4), which are regression equations, through nonlinear regression analysis between the ultrasonic frequency and any one of the attenuation coefficient and the phase velocity calculated in the first analysis step (S200). is a step

[Equation 3]

[dB/cm]는 초음파 감쇠계수,

는 y절편,

[

]은 계수, f는 주파수, n은 지수)(here,

[dB/cm] is the ultrasonic attenuation coefficient,

is the y-intercept,

[

] is the coefficient, f is the frequency, n is the exponent)

[Equation 4]

[m/s]는 위상속도,

는 y절편,

[

]은 계수, n은 지수) (here,

[m/s] is the phase velocity,

is the y-intercept,

[

] is the coefficient, n is the exponent)

(이하, ‘제1 변수’라 칭함) 및

(이하, ‘제2 변수’라 칭함)을 변수로 추출하는 단계이다.And, the extraction step (S320) through Equations 3 and 4 derived in the derivation step (S310)

(hereinafter referred to as the 'first variable') and

(hereinafter referred to as a 'second variable') is a step of extracting as a variable.

[

]은 주파수 변화에 따른 감쇠계수의 변화량 즉, 계수의 크기(magnitude)를 나타내며, 지수 n은 대역폭 내에서 감쇠계수의 주파수 의존성을 나타낸다.Referring to FIG. 4, to describe the first variable and the second variable in more detail, FIG. 4(a) is a graph showing the attenuation coefficient in the frequency domain in the range of 0.2 MHz to 0.6 MHz, and a nonlinear regression analysis ( By performing a nonlinear power fit, the above-described Equation 3 in the form of a nonlinear power law may be derived. Here, the first variable

[

] indicates the amount of change of the attenuation coefficient according to the frequency change, that is, the magnitude of the coefficient, and the exponent n indicates the frequency dependence of the attenuation coefficient within the bandwidth.

[

]은 주파수 변화에 따른 위상속도의 변화량 즉, 분산율(dispersion rate)를 나타내며, 지수 n은 대역폭 내에서 위상속도의 주파수 의존성을 나타낸다.And, Figure 4(b) is a graph showing the phase velocity in the frequency domain in the range of 0.2 MHz to 0.6 MHz, by performing a nonlinear regression analysis (Nonlinear power fit), a nonlinear power law (nonlinear power law) form Equation 4 described above can be derived. Here, the second variable

[

] represents the amount of change of the phase speed according to the frequency change, that is, the dispersion rate, and the exponent n represents the frequency dependence of the phase speed within the bandwidth.

및

를 통한 골밀도 및 골구조 예측결과를 제공함으로써, 종래의 음속 및 광대역 초음파 감쇠만을 이용해 골밀도를 측정하던 방식에 비해 골다공증 진단의 정확도를 향상시킬 수 있다.At this time, the present invention is a new parameter in addition to the conventional parameters presented for the prediction of bone density.

and

By providing the results of bone density and bone structure prediction through , it is possible to improve the accuracy of osteoporosis diagnosis compared to the conventional method of measuring bone density using only sound velocity and broadband ultrasound attenuation.

For reference, the above-described first analysis step ( S200 ) and the second analysis step ( S300 ) may be performed by the analysis unit 130 .

4. Prediction step <S400>

The prediction step (S400) is performed in the prediction unit 140, and the second analysis step (S300) is performed in a multiple regression model in which any one of the bone density and bone structure of cancellous bone is a dependent variable, and at least two or more variables are independent variables. ) is a step of predicting the bone density and bone structure of the cancellous bone by applying the extracted variables as independent variables. Here, the bone structure of the cancellous bone includes at least one of a bone volume ratio (BT/TV), a trabecular thickness (Tb.Th), and a trabecular distance (Tb.Sp) of the cancellous bone.

Here, in the prediction step (S400), each dependent variable and the first trabecular bone density, bone volume ratio (BT/TV), trabecular thickness (Tb. Bone density and bone structure can be predicted from a regression equation derived by performing multiple regression analysis on the independent variable including the variable and the second variable, ie, from the multiple regression model.

At this time, the multiple regression model used to predict the bone density and bone structure of cancellous bone together in the prediction step (S400) can be obtained through the multiple regression model generation step (not shown) performed before the measurement step (S100). . In this case, in the multiple regression model generation step, the first variable and the second variable measured in advance from a plurality of cancellous bone samples are used as independent variables, and any one of the bone density and bone structure of the cancellous bone sample is used as a dependent variable. In this step, multiple regression analysis is performed on the independent variable and each dependent variable, and the regression equation derived therefrom is obtained as a multiple regression model. In some cases, it may be possible to include the damping coefficient and phase velocity of cancellous bone in addition to the first and second variables as the independent variable for generating the multiple regression model.

For reference, a multiple regression model obtained by performing regression analysis using the first variable and the second variable as independent variables and bone density or bone structure as a dependent variable may be expressed by Equation 5 below.

[Equation 5]

및

는 독립변수(제1 변수 및 제2 변수), a 및 b는 계수, c는 y절편) (where y is the dependent variable (bone density or bone structure),

and

is the independent variable (first variable and second variable), a and b are coefficients, c is the y-intercept)

To describe the multiple regression model in more detail, regression coefficients corresponding to a, b, and c in Equation 5 can be derived through multiple regression analysis on a plurality of pre-measured cancellous bone samples, and from this A regression equation to which the regression coefficient is substituted can be obtained.

Thereafter, it is possible to predict the bone density and bone structure together by substituting a plurality of variables extracted from cancellous bone to be measured into the pre-stored regression equation prepared in the form of Equation 5 above.

In this case, in the present invention, by evaluating bone density and bone structure together, it is possible to identify and diagnose the cause of osteoporosis caused by changes in bone structure as well as changes in bone density, thereby improving the accuracy of diagnosis of osteoporosis.

4. Output step <S500>

The output step ( S500 ) is a step of outputting the result predicted in the prediction step ( S400 ).

Here, the predicted bone density and bone structure may be provided by the output unit 150 as at least one of an image, a sound, and a print. In this case, the predicted results may include osteoporosis diagnosis results expected therefrom along with bone density and bone structure.

Meanwhile, FIG. 5 is a method for predicting bone density and bone structure using an ultrasonic damping coefficient and a phase velocity according to an embodiment of the present invention. As an example to explain the reliability of the bone structure, ^{this table compares the coefficient of determination R 2} (the square of the Pearson correlation coefficient R) of the simple regression model and the multiple regression model.

) 및 제2 변수(

)를 추출하였다.In order to present the results of Fig. 5, the human cancellous bone (T) was subjected to a center frequency of 0.5 MHz for 22 spongy bone samples having a rectangular parallelepiped shape manufactured using the proximal part of a cow's femur with similar structural and acoustic characteristics. The attenuation coefficient and the phase velocity are simultaneously measured in the 0.2 MHz to 0.6 MHz frequency region using a pair of ultrasonic transducers having

) and the second variable (

) was extracted.

) 또는 제2 변수(

)를 독립변수로 하여, 각 독립변수와 종속변수를 회귀 분석한 것이며, 다중회귀모델은 골밀도 및 골구조 중 어느 하나를 종속변수로 하고, 제1 변수(

) 및 제2 변수(

)를 독립변수로 하여, 각 종속변수와 독립변수를 회귀 분석한 것이다. In this case, the simple regression model uses any one of bone density and bone structure as a dependent variable, and the first variable (

) or the second variable (

) as an independent variable, regression analysis of each independent variable and dependent variable is carried out.

) and the second variable (

) as an independent variable, regression analysis of each dependent variable and independent variable is performed.

Referring to FIG. 5 , it was found that the coefficient of determination of the multiple regression model was larger than that of the simple regression model. In this way, compared to predicting the bone density and bone structure of cancellous bone by using each of the two ultrasound variables obtained through nonlinear regression analysis of the attenuation coefficient and phase velocity of cancellous bone as independent variables, both ultrasound variables are used as independent variables. It can be confirmed that using a multiple regression model can more accurately predict the bone density and bone structure of cancellous bone.

As described above, the detailed description of the present invention has been made by the embodiments with reference to the accompanying drawings, but since the above-described embodiments have only been described with preferred examples of the present invention, the present invention is limited only to the above embodiments It should not be understood as being, and the scope of the present invention should be understood as the following claims and their equivalents.

100: Bone density and bone structure prediction device using ultrasonic damping coefficient and phase velocity
110: a pair of ultrasonic transducers
111: transmitter
112: receiver
120: waveform generator
130: analysis unit
140: prediction unit
150: output unit
C: cortical bone
T: spongy bone
Tb: bone shochu
Tb.Sp: trabecular distance
Tb.Th: thickness of bone trabeculae

Claims (5)

A measuring step of irradiating a transmitting ultrasound wave to the cancellous bone positioned between a pair of ultrasound transducers, and receiving the receiving ultrasound wave passing through the spongy bone by the other transducer;
a first analysis step of analyzing the electrical signal of the received ultrasonic wave received in the measuring step to calculate an attenuation coefficient and a phase speed for a frequency region in the range of 0.2 MHz to 0.6 MHz;
a second analysis step of deriving a regression equation by performing nonlinear regression analysis between each of the attenuation coefficient and phase velocity calculated in the first analysis step and the ultrasonic frequency, and extracting the coefficient of the derived regression equation as a variable; and
Any one of the bone density and bone structure of the cancellous bone is a dependent variable, and the variable extracted in the second analysis step is applied as an independent variable to a multiple regression model in which at least two or more variables are independent variables. A prediction step of predicting the bone structure; includes,
In the first analysis step, the attenuation coefficient is calculated through Equation 1 below, and the phase speed is calculated through Equation 2 below.
Bone density and bone structure prediction method using ultrasonic damping coefficient and phase velocity.

[Equation 1]

(here,

[dB/cm] is the attenuation coefficient of spongy bone, d is the thickness of spongy bone,

and

is the power spectrum level of the received signal when there is no cancellous bone between a pair of ultrasound transducers, respectively)

[Equation 2]

(here,

[m/s] is the phase velocity of cancellous bone,

is the frequency, d is the thickness of the spongy bone,

is the temperature-dependent speed of sound in water (1482 m/s, 20 °C),

is the phase difference of the received signal between a pair of ultrasonic transducers in the absence and presence of spongy bone)
According to claim 1,
The second analysis step is,
a derivation step of deriving the following Equations 3 and 4, which are regression equations, through nonlinear regression analysis between the ultrasonic frequency and any one of the attenuation coefficient and the phase velocity calculated in the first analysis step; and
Through Equations 3 and 4 derived in the derivation step,

(hereinafter referred to as the 'first variable') and

an extraction step of extracting (hereinafter referred to as a 'second variable') as a variable; characterized by comprising
Bone density and bone structure prediction method using ultrasonic damping coefficient and phase velocity.
[Equation 3]

(here,

[dB/cm] is the ultrasonic attenuation coefficient,

is the y-intercept,

[

] is the coefficient, f is the frequency, n is the exponent)

[Equation 4]

(here,

[m/s] is the phase velocity,

is the y-intercept,

[

] is the coefficient, n is the exponent)
3. The method of claim 2,
The bone structure of the cancellous bone is characterized in that it comprises at least one of a bone volume ratio (BT/TV), a trabecular thickness (Tb.Th), and a trabecular distance (Tb.Sp) of the cancellous bone
Bone density and bone structure prediction method using ultrasonic damping coefficient and phase velocity.
4. The method of claim 3,
The first variable and the second variable measured in advance from a plurality of cancellous bone samples are used as independent variables, and any one of bone density and bone structure of the cancellous bone sample is used as a dependent variable. a multiple regression model generation step of performing multiple regression analysis and obtaining an index equation derived therefrom as a multiple regression model; characterized by comprising
Bone density and bone structure prediction method using ultrasonic damping coefficient and phase velocity.
According to claim 1,
An output step of outputting the results predicted in the bone density and bone structure prediction step; characterized in that it further comprises
Bone density and bone structure prediction method using ultrasonic damping coefficient and phase velocity.

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