CN110720951A - Ultrasonic axial transmission bone density measurement method based on wavelet transformation - Google Patents
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Abstract
An ultrasonic axial transmission bone density measuring method based on wavelet transformation is a method improvement for solving the practical problems of low measuring precision and the like in the existing bone density measuring technology; the core scheme of the design method is that original echo signals are mapped to a time-scale relation through wavelet transformation with Mexican hat as a wavelet base, and a time-scale graph is observed to obtain the initial position of a first arrival wave. In order to further optimize the method of the first arrival time difference, the scale is transferred to the corresponding frequency, the scale of the center frequency of the original signal is calculated, and the correlation coefficient of the scale is extracted; the maximum value and the time of each correlation coefficient of the data are extracted, the maximum value and the time of the correlation coefficient of the first arrival wave are extracted according to the approximate position of the first arrival wave in the time-scale graph, the time difference can be obtained by making a difference between the times corresponding to the maximum values of the correlation coefficients of the first arrival waves of the two channel signals, and the ultrasonic wave conduction velocity is obtained, so that the bone density is represented.
Description
Technical Field
The invention mainly relates to the field of medical ultrasonic diagnosis, and designs an ultrasonic axial transmission bone density measuring method based on wavelet transformation by utilizing a quantitative ultrasonic technology aiming at the actual conditions of low measuring precision and the like existing in the current bone density measuring technology.
Background
Osteoporosis is a disease frequently occurring in the middle-aged and the elderly, and in recent years, it gradually threatens the health of people. In recent years, people find that some young people gradually suffer from osteoporosis due to poor work and rest and eating habits, and the condition may be called as the 'senile disease' and the young people tend to be younger. The harm of osteoporosis to human body is manifold and not easy to be found.
With the increasing attention on osteoporosis, relevant patients have higher requirements on bone density measurement technology, and the following methods are roughly used for measuring the bone density of people in the current medical technology: the single photon absorption measurement (SPA) determines the intensity of human bone density by the different absorption rates of bone tissues with different densities to gamma rays. However, the measurement precision of the technology is not high, and the equipment is relatively cheap, so that the technology is more suitable for application scenes of large-area mass census than families and communities. Two-photon absorption measurement (DPA), which is derived from single-photon absorption, has improved accuracy and precision of measurement results compared to the former, but is considerably more expensive. The dual-energy X-ray Determination (DEXA) technology can be researched for a long time in the seventies of the last century, but is only applied to clinical determination for more than ten years, has the characteristic of low radiation amount, is a gold standard accepted by the international health organization, has obvious defects, is large in size and high in price, and is not suitable for being applied to families, communities and other scenes. In bone biopsy, in short, human bone tissue is sliced and observed by a microscope, and the method is obviously only performed in a laboratory and cannot be applied to clinic. The quantitative CT technique has higher accuracy than the method described above, but has a large drawback in that the equipment is expensive, it can be configured only by large hospitals, and the radiation dose is large. With the widespread use of CT technology and various patients, it is not necessary to make the patients suffer from the harmful effects of radiation after receiving the radiation for a long time, so that the reduction of the radiation dose is also a concern.
The invention designs an ultrasonic axial transmission bone density measuring method based on wavelet transformation, which aims to solve the problems of low measuring precision, high price, large volume, large radiation and the like.
Disclosure of Invention
The invention aims to solve the problems that: because the existing bone density measurement technology has a plurality of defects, people have urgent need for realizing bone density measurement by using a quantitative ultrasonic technology, so the invention designs an ultrasonic axial transmission bone density measurement method based on wavelet transformation aiming at improving the current bone density measurement technology.
The technical scheme of the invention is as follows: the critical angle side wave transmitted axially is used as a first arrival wave, and the osteoporosis condition is measured by using the ratio of the distance traveled by the first arrival wave to the time.
Firstly, the transmitting end and the receiving end of the ultrasonic probe are placed at a certain angle and height, so that the side wave becomes a first arrival wave and reaches the receiving end. When the four ultrasonic probes are positioned on the same horizontal line, the time required for the ultrasonic waves to enter the bone from the soft tissue is the same as the time required for the ultrasonic waves to penetrate out of the bone from the soft tissue, the difference value of the first arriving wave paths received by the two receiving ends is twice the probe distance d, and the sound velocity of the ultrasonic waves in the bone can be obtained by taking the path difference and the time difference as quotients.
Secondly, under the condition of determining the propagation distance, the core lies in solving the time delay of the first arrival wave of the two echo paths. Echo data are processed by a wavelet transform method with Mexican Hat wavelets as wavelet bases, a time-scale graph is drawn by combining MATLAB, and the initial position of the first arrival wave is obtained according to graphic information. In addition, compared with a method for directly reading graphic information of a time-scale diagram in MATLAB, the method transfers the scale to corresponding frequency, extracts relevant parameters of the scale by calculating the scale of the center frequency of an original signal, and converts the problem into the research of the relevant parameters of the scale; relationship between scale and frequency: setting a as a scale, fs as a sampling frequency and Fc as a wavelet center frequency, and then setting the actual frequency Fa corresponding to a as Fc multiplied by fs/a; the absolute value of the processed data is taken, the maximum value and the time of each correlation coefficient of the absolute value-taken data after 200 points are extracted, the maximum value and the time of the correlation coefficient of the first arrival wave are extracted according to the approximate position of the first arrival wave in the time-scale graph, and the time difference can be obtained by making the difference between the times corresponding to the maximum values of the correlation coefficients of the first arrival wave obtained by the two channel signals.
Drawings
FIG. 1 is a system design block diagram.
FIG. 2 is a time-frequency analysis diagram of Mexican Hat wavelet transform.
FIG. 3 is a graph of the echo and the scale-dependent coefficient of the AD channel of a glass test block.
Fig. 4 is a graph of the echo and the scale-dependent coefficient of the BC channel of the glass block.
FIG. 5 is a graph of the echo and the scale dependent coefficient of the AD channel of the brass test block.
Fig. 6 is a plot of the echo and the scale-dependent coefficient of the brass test block BC channel.
Detailed Description
A method for measuring bone density through ultrasonic axial transmission based on wavelet transformation comprises the steps of selecting a wavelet base, processing echoes through wavelet transformation, determining the position of a first arrival wave through an optimization method, and calculating the time delay of the first arrival wave of two channels and the propagation speed of ultrasonic waves in cortical bone.
The wavelet basis is selected by comparing the characteristics of common wavelet basis functions: the Daubechies wavelet has no symmetry and its support zone is 2N-1, so the Daubechies wavelet has limited length. The Meyer wavelet has no tight support characteristic, and the convergence speed is fast. The Morlet wavelet is a non-orthogonal complex wavelet, so that the amplitude and phase information of the signal can be analyzed. The function envelope of the method is a Gaussian envelope, has symmetry, has no scale function, is not good in tight support, but is widely applied in engineering, but the Morlet wavelet cannot be used for orthogonal wavelet transformation because the Morlet wavelet is non-orthogonal. The Mexican hat wavelet is a second-order derivative expression of a Gaussian function, has better localization characteristics in both time domain and frequency domain, and is suitable for the research of some weak signal positioning. Through research and experiments on the above conventional wavelet bases, specifically, radius test data of a human arm is imported into a PC (personal computer), simulation test is performed by combining MATLAB (matrix laboratory), a time-scale graph and a correlation coefficient graph are drawn for comparison, and Mexican Hat wavelets are selected, and the time-frequency analysis of the Mexican Hat wavelets is shown in FIG. 2.
In signal processing, a proper signal processing method is selected according to the characteristics of a signal, if the signal contains different frequencies, Fourier transform is used, and then a filter is used, but if the frequency of the signal is concentrated in a certain frequency, and a corresponding time point needs to be found in a frequency domain, the limitation of the Fourier transform is very obvious, and the wavelet transform is more suitable obviously. The time information can be obtained by the translation transformation of the mother wavelet, and the frequency domain information can be obtained by scaling the mother wavelet. In general, a high-scale transform (i.e., the wavelet is stretched) corresponds to a low-frequency signal, and a low-scale transform (i.e., the wavelet is shrunk) corresponds to a high-frequency signal. Wavelet coefficients can be obtained by scaling and shifting the mother wavelet. These wavelet coefficients represent the similarity to the local signal. In the experiment, a correlation coefficient with an original signal is obtained by wavelet transformation and taking a Mexican Hat wavelet as a wavelet basis, a time scale graph is drawn in Matlab, and the magnitude of the correlation coefficient is represented by different chromaticities, so that a value with a larger correlation number in the original signal is well mapped on the chromaticity graph, and the starting moment of a first arrival wave can be clearly observed.
In the process of processing the original signal, the following four steps are needed:
the first step is as follows: the scale range of the wavelet change is set, the range is selected appropriately, and how to select the scale range is to find the corresponding scale value under the central frequency of the signal. Otherwise, the correlation coefficient obtained after the wavelet change is small, which is not favorable for the later data analysis.
The second step is that: and under each scale, the mother wavelet is translated to obtain the discrete value of the wavelet. And convolved with the original signal.
The third step: and repeating the second step to obtain multi-scale wavelet discrete values under different scales, and respectively performing convolution with the original signals.
The fourth step: because sequences obtained by convolution can be obtained under different scales, the matrix is a matrix in Matlab, the row number of the matrix represents the scale, and each row of the matrix represents the correlation coefficient of the original signal and the wavelet under each scale. So if this matrix is plotted in the graph, a scale-time graph can be obtained. In this figure, the size of the wavelet coefficients can be mapped into a chromaticity diagram, with brighter chromaticities indicating larger coefficients. Therefore, in order to have a good mapping between the coefficients and the chromaticity, the wavelet coefficients are required to be subjected to modulus, and the modulus wavelet coefficients are mapped into the chromaticity diagram.
Determining the time of the first arrival wave center position by an optimization method:
①, the echo data after passing through the band-pass filter is processed by wavelet transform with specified scale.
② taking the absolute value of the data obtained in step ①.
③ extracting each maximum and time after 200 points of the data obtained in step ②.
④ time to extract the first maximum of the data obtained in step ③.
And writing a program according to the steps, wherein the time obtained by calculation is the time of the center position of the first arrival wave, and the time difference can be obtained by making the difference between the arrival times of the first arrival waves obtained by the two channel signals. It is supplementary how to determine the scaling coefficients for the wavelet transform. Let the scale be a and the sampling frequency be fsWavelet center frequency of fcAnd the frequency of the ultrasonic probe is f, there is a formula:
sampling frequency f of the designs60MHz Mexican Hat wavelet center frequency fcThe frequency f of the ultrasound probe is 0.25Hz and 1.25MHz, and the calculated scale is 12.
The design adopts Mexican Hat wavelet as wavelet base, glass test block and copper block as test sample, and the obtained echo data is first simulated and verified with MATLAB. Because the wavelet coefficient obtained after wavelet transformation can reflect the local similarity degree with the echo signal, wavelet transformation with different scales is carried out on the echo signal, and the abscissa is taken as the time ordinate in MATLAB as the correlation coefficient, and the correlation coefficient is normalized, the following graph is a correlation coefficient graph with the scale of 12.
First, a glass test block was tested, as shown in fig. 3 and 4.
The method obtains that the first arrival time of the AD channel is 635, the first arrival time of the BC channel is 379, and the difference between the first arrival time and the first arrival time is 256. The difference of the paths traveled by the AD channel and the BC channel is twice of the distance between the probes, namely
Thus, the sound velocity (SOS) is approximately 2718m/s, which is close to the standard sound velocity 2730m/s of the glass test block at the current temperature of 30 ℃.
The brass test blocks were then tested, as shown in fig. 5 and 6.
The method obtains that the first arrival time of the AD channel is 467 points, the first arrival time of the BC channel is 306 points, and the difference between the two is 161 points. The difference of the paths traveled by the AD channel and the BC channel is twice of the distance between the probes, namely
This resulted in a sound velocity (SOS) of approximately 4322m/s, which is similar to the standard sound velocity of 4300m/s for brass test blocks.
Claims (5)
1. A method for measuring bone density through ultrasonic axial transmission based on wavelet transformation is characterized in that the sound velocity is determined by determining the position of a first arrival wave through wavelet transformation; experiments and MATLAB simulation show that the cross-correlation method has the defect that the result is accurate only when the echo signal only contains the first arrival wave when the echo signal is processed; it is therefore proposed to determine the first arrival position using a wavelet transform which is better able to observe the local characteristics of the signal.
2. An ultrasonic axial transmission bone density measurement method based on wavelet transformation is characterized in that a Mexican Hat wavelet is selected as a wavelet basis under the method; the whole method leads the radius test data of the human arm into a PC, and through experiments, simulation tests are carried out by combining MATLAB to obtain time-scale graphs of wavelet transformation under different wavelet bases, ultrasonic wave conduction velocities (SOS) corresponding to different wavelet bases are calculated according to the positions of first arrival waves obtained through the time-scale graphs of the wavelet transformation under different wavelet bases, and compared with the actual velocity of ultrasonic waves transmitted in a sample, the Mexican Hat wavelet result is more accurate.
3. A method for measuring bone density by ultrasonic axial transmission based on wavelet transformation is characterized in that ultrasonic waves transmitted along the axial direction of cortical bone are taken as first arrival waves; different from the method for measuring the bone density by ultrasonic transmission type transmission, the method selects that when the ultrasonic waves are transmitted at the position meeting the specific angle and height function, the ultrasonic waves transmitted along the axial direction of the cortical bone can preferentially reach the receiving end of the ultrasonic probe.
4. A method for measuring bone density by ultrasonic axial transmission based on wavelet transformation is characterized by optimizing and calculating a first arrival time difference; compared with a method for directly reading graphic information of a time-scale graph in MATLAB, the method transfers the scale to corresponding frequency, extracts relevant parameters of the scale by calculating the scale of the center frequency of an original signal, and converts a problem into the research of the relevant parameters of the scale; relationship between scale and frequency: setting a as a scale, fs as a sampling frequency and Fc as a wavelet center frequency, and then setting the actual frequency Fa corresponding to a as Fc multiplied by fs/a; the absolute value of the processed data is taken, the maximum value and the time of each correlation coefficient of the absolute value-taken data after 200 points are extracted, the maximum value and the time of the correlation coefficient of the first arrival wave are extracted according to the approximate position of the first arrival wave in the time-scale graph, and the time difference can be obtained by making the difference between the times corresponding to the maximum values of the correlation coefficients of the first arrival wave obtained by the two channel signals.
5. A method for measuring bone density by ultrasonic axial transmission based on wavelet transformation is characterized in that a hardware amplifying circuit is adopted at the front end of a signal and a proper damping resistor is selected to improve the signal-to-noise ratio; reflected waves and refracted waves in echo data are obvious, side waves transmitted axially are large in attenuation and small in amplitude, noise interference exists, and signals are unstable; the hardware amplifying circuit adopts a chip AD8099 to form an in-phase amplifying circuit, so that the signal amplitude is increased, and the signal-to-noise ratio is improved; experiments show that when 200 ohm damping resistors are selected, the noise and the first arrival wave have obvious discrimination.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101336380A (en) * | 2005-11-27 | 2008-12-31 | 奥斯特欧特罗尼克斯公司 | Assessment of structures such as bone using spatial-frequency analysis |
| WO2009110260A1 (en) * | 2008-03-06 | 2009-09-11 | コニカミノルタエムジー株式会社 | Image treating device |
| US8094904B2 (en) * | 2007-09-26 | 2012-01-10 | Siemens Aktiengesellschaft | Method and system for bone suppression based on a single x-ray image |
| CN107854141A (en) * | 2017-11-24 | 2018-03-30 | 南京大学 | A kind of bone trabecula distance measurement system based on ultrasonic backscattering signal |
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- 2019-10-25 CN CN201911027660.8A patent/CN110720951A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101336380A (en) * | 2005-11-27 | 2008-12-31 | 奥斯特欧特罗尼克斯公司 | Assessment of structures such as bone using spatial-frequency analysis |
| US8094904B2 (en) * | 2007-09-26 | 2012-01-10 | Siemens Aktiengesellschaft | Method and system for bone suppression based on a single x-ray image |
| WO2009110260A1 (en) * | 2008-03-06 | 2009-09-11 | コニカミノルタエムジー株式会社 | Image treating device |
| CN107854141A (en) * | 2017-11-24 | 2018-03-30 | 南京大学 | A kind of bone trabecula distance measurement system based on ultrasonic backscattering signal |
Non-Patent Citations (3)
| Title |
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| M. K. ALI HASSAN, K. NAGAMUNE, K. KAKUTANI, K. MAENO, K. NISHIDA: "Wavelet decomposition processing method : Cortical bone thickness measurement using ultrasound sensor", 《2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)》 * |
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