CN114795288A - Human body osteoporosis detection method based on ultrasonic guided wave frequency dispersion curve - Google Patents
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Abstract
The invention provides a human body osteoporosis detection method based on an ultrasonic guided wave frequency dispersion curve, which is used for processing and analyzing acquired ultrasonic signals of human bones to obtain a corresponding bone density judgment basis and is characterized by comprising the following steps of: carrying out Fourier transform on the ultrasonic signal to obtain an initial frequency dispersion curve; processing the initial dispersion curve by using a preset power spectrum estimation algorithm to obtain a maximum energy point track; and performing fitting point matching in a pre-established simulation model database by using a preset search algorithm according to the maximum energy point track to obtain a parameter of a maximum fitting coefficient corresponding to the maximum energy point track, and taking the transverse wave speed in the parameter as a bone density judgment basis so as to enable a user to distinguish non-osteoporosis patients from osteoporosis patients according to the bone density judgment basis. The invention has no radiation hazard to patients, and has the advantages of low cost, high accuracy, small calculated amount and high real-time property.
Description
Technical Field
The invention belongs to the field of medical diagnosis, and particularly relates to a human osteoporosis detection method based on an ultrasonic guided wave frequency dispersion curve.
Background
Osteoporosis is a common degenerative disease. Once osteoporosis occurs, a plurality of hazards are brought to patients, such as low back pain, height shortening, humpback, dyspnea, osteoporotic fracture and the like. Therefore, early screening and prevention of osteoporosis are the most economic and effective means for preventing and treating the occurrence of osteoporosis and osteoporotic fracture.
Currently, a Dual Energy X-ray absorption method (DEXA) is selected by most medical institutions at home and abroad as a method for evaluating bone density, so as to evaluate the health degree of bones. However, the dual-energy X-ray absorption method is complicated in operation, long in measurement time, high in test cost, expensive in test equipment, and large in occupied space, and thus is difficult to popularize in community medical institutions, middle and small hospitals or physical examination centers. This also results in a significant proportion of people with osteoporosis not being diagnosed and prevented effectively, leading to a high incidence of osteoporosis in the elderly. In addition, special people such as premature infants or pregnant women often need to monitor the bone health condition regularly, and the low radiation hazard of DEXA is not suitable for long-term monitoring of pregnant women and infants.
Disclosure of Invention
In order to solve the problems, the invention provides a human body osteoporosis detection algorithm which is free of radiation hazard, simple and feasible, low in cost and high in accuracy, and the invention adopts the following technical scheme:
the invention provides a human body osteoporosis detection method based on an ultrasonic guided wave frequency dispersion curve, which is used for processing and analyzing collected ultrasonic signals of human bones to obtain a corresponding bone density judgment basis and is characterized by comprising the following steps of: step S1-1, carrying out Fourier transform on the ultrasonic signal to obtain an initial dispersion curve; step S1-2, processing the initial dispersion curve by using a preset power spectrum estimation algorithm to obtain a maximum energy point track; and step S1-3, performing fitting point matching in a pre-established simulation model database by utilizing a preset search algorithm according to the maximum energy point track to obtain a parameter of a maximum fitting coefficient corresponding to the maximum energy point track, and taking the transverse wave velocity in the parameter as a bone density judgment basis so as to enable a user to distinguish non-osteoporosis and osteoporosis patients according to the bone density judgment basis.
The method for detecting the osteoporosis of the human body based on the ultrasonic guided wave frequency dispersion curve can also have the technical characteristics that the simulation model database is a frequency dispersion curve point of skin-cortical bone-marrow three-layer simulation constructed by guided wave signals collected by a waveguide.
The method for detecting the osteoporosis of the human body based on the ultrasonic guided wave frequency dispersion curve, provided by the invention, also has the technical characteristics that the establishment process of the simulation model database comprises the following steps: step S2-1, establishing an initial cortical bone thickness h 1 mm, thickness increment of delta h, and initial longitudinal wave velocity of p 1 m/s, increment of longitudinal wave velocity delta p and initial transverse wave velocity s 1 m/s and initial database with transverse wave velocity increment of deltas, wherein the initial database comprises h z *p z *s z Group initial dispersion data K hps (ii) a Step S2-2, recombining all initial frequency dispersion data into corresponding frequency dispersion point matrix K' hps To obtain a matrix K 'of all frequency dispersion points' hps Constructed simulation model database, initial dispersion data K hps Composed of multiple frequency dispersion points, the initial frequency dispersion data K hps Including multiple modes
Where n is the number of modes corresponding to each set of initial dispersion data, m ═ 1,2 is the frequency and phase velocity of the dispersion points in each mode, and l is the arrangement order of the dispersion points in each mode.
According to the human osteoporosis detection method based on the ultrasonic guided wave frequency dispersion curve provided by the invention, the human osteoporosis detection method can also be used for detecting the osteoporosis of a human bodyTo have the technical feature that the step S2-2 includes the following sub-steps: step S2-2-1, searching out the corresponding phase velocity position b in each mode based on the frequency point i ：
b i ＝i
In the formula, df is the longitudinal interval of the maximum energy point track A, l is the arrangement sequence of the frequency dispersion points in each mode, and c is an array formed by all phase velocity positions corresponding to the frequency points in each arrangement sequence l; step S2-2-2, setting the arrangement sequence l corresponding to the minimum value in the array c as a known arrangement sequence l'; step S2-2-3, calculating wave number position b of wave number according to known permutation sequence l j ：
Where dk is the lateral spacing of the maximum energy point trajectory a,
the frequency of the i' th dispersion point of the nth mode,
phase velocity of the l' th dispersion point of the nth mode; step S2-2-4, according to the phase velocity position b i And wave number position b j Obtaining recombined initial frequency dispersion data as frequency dispersion point matrix K' hps And the initial dispersion data K is added hps Marking the corresponding position to obtain a marked position; step S2-2-5, repeating the steps S2-2-1 to S2-2-4 until all the initial dispersion data are completely recombined to obtain a matrix K 'of all dispersion points' hps And forming a simulation model database.
The method for detecting the osteoporosis of the human body based on the ultrasonic guided wave frequency dispersion curve can also be usedAs such, wherein the step S1-3 includes the following sub-steps: step S1-3-1, judging whether the energy E of the grid points in the maximum energy point track is larger than a preset energy threshold value T or not; step S1-3-2, recording the position corresponding to the grid point as the position to be matched when the judgment of the step S1-3-1 is positive; step S1-3-3, searching whether a mark position corresponding to the position to be matched exists in the simulation model database through a search algorithm; step S1-3-4, when the judgment of step S1-3-3 is yes, according to the energy E of the grid point corresponding to the mark position and the fitting point P hps Calculating to obtain a fitting coefficient Q hps And a pseudo-coincidence mean value P' hps ：
P hps ＝P hps +1
(ii) a Step S1-3-5, repeating the step S1-3-1 to the step S1-3-4 until a fitting coefficient Q corresponding to each grid point in the maximum energy point track is obtained hps And a pseudo-coincidence mean value P' hps (ii) a Step S1-3-6, according to all fitting coefficients Q hps And all the fitted point mean values P' hps Selecting a fitting coefficient Q hps The maximum value is used as a maximum fitting coefficient, and the transverse wave speed in the parameter in the maximum fitting coefficient is used as a bone density judgment basis.
The method for detecting the osteoporosis of the human body based on the ultrasonic guided wave frequency dispersion curve provided by the invention can also have the technical characteristics that the power spectrum estimation algorithm in the step S1-2 is a Burg algorithm.
The method for detecting the osteoporosis of the human body based on the ultrasonic guided wave frequency dispersion curve, provided by the invention, can also have the technical characteristics that the search algorithm in the step S1-3 is a grid search algorithm.
Action and Effect of the invention
According to the human body osteoporosis detection method based on the ultrasonic guided wave frequency dispersion curve, the initial frequency dispersion curve is obtained by performing Fourier transform on ultrasonic signals, then the initial frequency dispersion curve is processed by using a preset power spectrum estimation algorithm to obtain the maximum energy point track, finally fitting point matching is performed on the maximum energy point track in a preset simulation model database by using a preset search algorithm to obtain the parameter of the maximum fitting coefficient corresponding to the maximum energy point track, the transverse wave velocity in the parameter is used as the bone density judgment basis, so that the cortical bone transverse wave velocity can be obtained by inversion through the steps under the condition that only the human body bone ultrasonic signals of a patient exist, the transverse wave velocity can reflect the porosity condition of cortical bone, and non-osteoporosis and osteoporosis patients can be effectively distinguished according to the transverse wave velocity, the method has high accuracy and high reliability. In addition, only the human bone ultrasonic signals of the patient are needed, so compared with a dual-energy X-ray absorption method, the method is low in cost, does not generate radiation to the patient, has smaller calculation amount and higher real-time performance, and is suitable for long-term and periodic detection of the health degree of the human bone.
Drawings
FIG. 1 is a flowchart of a method for detecting osteoporosis in a human body based on an ultrasonic guided wave frequency dispersion curve according to an embodiment of the present invention;
FIG. 2 is a grid diagram of a maximum energy point trajectory according to an embodiment of the present invention;
FIG. 3 is a schematic grid diagram of initial dispersion data according to an embodiment of the present invention;
FIG. 4 is a grid diagram of a frequency dispersion point matrix according to an embodiment of the present invention;
FIG. 5 is a flowchart of the substep of step S1-3 of an embodiment of the present invention;
fig. 6 is a schematic diagram of grid points corresponding to effective performance metrics in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of matching a mark position with a position to be matched according to an embodiment of the present invention;
FIG. 8 is a graph of a dispersion curve obtained by fitting point coefficient based inversion according to an embodiment of the present invention;
FIG. 9 is a graph of a dispersion curve obtained based on a fitting point mean inversion according to an embodiment of the present invention; and
fig. 10 is a dispersion curve diagram obtained by inversion based on the fitting point mean and the fitting coefficient according to the embodiment of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the following embodiment and the accompanying drawings are combined to specifically describe the human osteoporosis detection method based on the ultrasonic guided wave dispersion curve.
< example >
The human body osteoporosis detection method based on the ultrasonic guided wave frequency dispersion curve in the embodiment of the invention is to process and analyze ultrasonic signals collected from the same side of the human body radial bone so as to obtain a corresponding bone density judgment basis. Wherein, the ultrasonic signal is obtained by a single-transmitting and multi-receiving mode.
Fig. 1 is a flowchart of a method for detecting osteoporosis in a human body based on an ultrasonic guided wave dispersion curve according to an embodiment of the present invention.
As shown in fig. 1, a method for detecting osteoporosis of a human body based on an ultrasonic guided wave dispersion curve includes the following steps:
and step S1-1, carrying out Fourier transform on the ultrasonic signal to obtain an initial dispersion curve.
The ultrasonic signals related to human bones are data in a multi-channel time-displacement format, the Fourier transform is particularly two-dimensional Fourier transform, and the initial frequency dispersion curve is data in a frequency-wavenumber domain format.
In this embodiment, the human bone related ultrasound signal is a 60 channel time-shifted signal.
And step S1-2, processing the initial dispersion curve by using a preset power spectrum estimation algorithm to obtain a maximum energy point track.
Wherein, the power spectrum estimation algorithm is a Burg algorithm.
Fig. 2 is a mesh diagram of a maximum energy point trajectory according to an embodiment of the present invention.
Specifically, the initial dispersion curve is processed by the Burg algorithm to obtain a high-resolution and high-accuracy maximum energy point trajectory a (as shown in fig. 2), which is an information matrix in a frequency-wavenumber domain format. Wherein, the abscissa is the frequency of the collected ultrasonic signals, and the number of the rows is marked as a i (ii) a The ordinate is the wave number of the collected ultrasonic signal, and the number of lines is recorded as a j 。
The maximum energy point track A is a reference matrix and is composed of grid points, and the position of each grid point is expressed as [ a ] j ，a i ]Each grid point has a corresponding energy value E (E in fig. 2) 11 、E 22 、E 33 、E 44 ...E ji ...)。
And step S1-3, performing fitting point matching in a pre-established simulation model database by utilizing a preset search algorithm according to the maximum energy point track to obtain a parameter of a maximum fitting coefficient corresponding to the maximum energy point track, and taking the transverse wave velocity in the parameter as a bone density judgment basis so as to enable a user to distinguish non-osteoporosis and osteoporosis patients according to the bone density judgment basis.
Wherein, the search algorithm in the step S1-3 is a grid search algorithm.
The simulation model database is a frequency dispersion curve point of skin-cortical bone-marrow three-layer simulation constructed by guided wave signals collected by a waveguide, and the establishing process of the database comprises the following steps:
step S2-1, establishing an initial cortical bone thickness h 1 mm, thickness increment of delta h, and initial longitudinal wave velocity of p 1 m/s, increment of longitudinal wave velocity delta p and initial transverse wave velocity s 1 m/s and initial database with transverse wave velocity increment of deltas, wherein the initial database comprises h z *p z *s z Group initial dispersion data K hps 。
Wherein h is 1 、Δh、p 1 、Δp、s 1 And Δ s are positive numbers. Frequency dispersion point matrix K hps Expressed as the dispersion data of thickness h, longitudinal wave velocity p and transverse wave velocity s。
Each group of initial frequency dispersion data K hps Consists of a plurality of dispersion points, and has a plurality of modes (A0, A1, A2.. S0, S1, S2..) and all modes of the initial dispersion data are marked as
Where n is the number of modes in each set of data, m ═ 1,2 is the frequency and phase velocity of the dispersion point of each mode, respectively, and l is the arrangement order of the dispersion points of each mode.
Fig. 3 is a schematic grid diagram of initial dispersion data according to an embodiment of the present invention.
As shown in FIG. 3, the initial dispersion data is represented as a matrix with the abscissa of the matrix representing the frequency of the ultrasonic signals in the database and the number of columns denoted b i (ii) a The ordinate is the wave number of the ultrasonic signal of the database, and the number of lines is recorded as b j 。
Step S2-2, recombining all initial frequency dispersion data into corresponding frequency dispersion point matrix K' hps To obtain a matrix K 'of all frequency dispersion points' hps And forming a simulation model database.
Wherein, the step S2-2 comprises the following sub-steps:
step S2-2-1, searching out the corresponding phase velocity position b in each mode based on the frequency point i ：
In the formula, df is the longitudinal interval of the maximum energy point track A, l is the arrangement sequence of the dispersion points in each mode, and c is an array formed by all phase velocity positions corresponding to the frequency points in each arrangement sequence l.
Step S2-2-2, the permutation sequence l corresponding to the minimum value in the array c is set as the known permutation sequence l'.
Step S2-2-3, calculating wave number position b of wave number according to known permutation sequence l j ：
Where dk is the lateral spacing of the maximum energy point trajectory a,
the frequency of the i' th dispersion point of the nth mode,
the phase velocity of the i' th dispersion point of the nth mode.
Fig. 4 is a grid diagram of a frequency dispersion point matrix according to an embodiment of the invention.
Step S2-2-4, according to the phase velocity position b i And wave number position b j Obtaining recombined initial frequency dispersion data as frequency dispersion point matrix K' hps (as shown in FIG. 4), and the initial dispersion data K hps The corresponding position in the image is marked to obtain a marked position R i ：
R i ＝K(b j ，b i )＝1 (3)
Step S2-2-5, repeating the steps S2-2-1 to S2-2-4 until all the initial dispersion data are completely recombined to obtain a matrix K 'of all dispersion points' hps And forming a simulation model database.
Fig. 5 is a flowchart of the substep of step S1-3 of an embodiment of the present invention.
As shown in fig. 5, step S1-3 includes the following sub-steps:
and S1-3-1, judging whether the energy E of the grid point in the maximum energy point track is larger than a preset energy threshold value T, if so, entering S1-3-2, and if not, repeating the step S1-3-1 to judge the next grid point.
Fig. 6 is a schematic diagram of grid points corresponding to effective metric values according to an embodiment of the present invention.
Due to the complexity of the acquired ultrasonic signals of the human skeleton, when extracting the maximum energy point trajectory of the lamb wave in the ultrasonic dispersion curve of the human radial bone, an energy threshold T is preset for the energy matrix value of the dispersion curve, and when the energy E is greater than the energy threshold T, the energy E is taken as an effective magnitude value (as shown in fig. 6).
Step S1-3-2, recording the position corresponding to the grid point as the position delta a to be matched when the judgment of the step S1-3-1 is positive i 。
And S1-3-3, searching whether a mark position corresponding to the position to be matched exists in the simulation model database through a search algorithm, if so, entering S1-3-4, and if not, entering S1-3-1.
Fig. 7 is a schematic diagram of matching a mark position with a position to be matched according to an embodiment of the present invention.
As shown in FIG. 7, in frequency dispersion point matrix K' hps To-be-matched position Δ a in (1) i Go out and search all b i At the position to be matched, Δ a i Whether or not the marked grid point is searched for (i.e., the mark position R in step S2-2-4) i )。
Step S1-3-4, when the judgment of step S1-3-3 is yes, according to the energy E of the grid point corresponding to the mark position and the fitting point P hps Calculating to obtain a fitting coefficient Q hps And a pseudo-coincidence point mean value P' hps ：
Specifically, when judged yes at step S1-3-3, each mark position R is set i Energy value E of the grid point i All b are accumulated i Corresponding energy as effective energy E':
E′＝E+E i (5)
at the same time, for the mark position R i To a fitting point P of hps The accumulation is performed. For all b i After the energy value and the fitting point are accumulated, a fitting coefficient Q is obtained by calculation according to a formula 5 hps And calculating to obtain a fitting point mean value P 'according to formula 5' hps ：
Step S1-3-5, repeating the step S1-3-1 to the step S1-3-4 until each grid in the maximum energy point track is obtainedFitting coefficient Q of point correspondences hps And a pseudo-coincidence mean value P' hps 。
Step S1-3-6, according to all fitting coefficients Q hps And all the fitted point mean values P' hps Selecting a fitting coefficient Q hps The maximum value is used as a maximum fitting coefficient, and the transverse wave speed in the parameter in the maximum fitting coefficient is used as a bone density judgment basis.
Fig. 8 is a dispersion curve diagram obtained by fitting point coefficient inversion according to an embodiment of the present invention.
As shown in FIG. 8, the fitting coefficient Q is selected hps In the process of maximum value, if only fitting point coefficient Q is considered hps Without considering the fitted point mean value P' hps The maximum fitting point coefficient Q hps Relatively large but fitting point mean value P' hps Is smaller.
Fig. 9 is a dispersion curve diagram obtained based on the fitting point-mean inversion according to the embodiment of the present invention.
If only the fitted point mean value P 'is considered' hps Without taking into account the fitting point coefficient Q hps Then fitted to point mean P' hps Is relatively large, but has a maximum fitting point coefficient Q hps Smaller (as shown in fig. 9).
Therefore, based only on the fitting coefficient Q hps Or is based only on the fitted point mean value P' hps The condition that the matching result is inaccurate is caused by selecting the maximum fitting coefficient, so that the fitting coefficient Q needs to be comprehensively considered hps And a pseudo-coincidence mean value P' hps 。
Fig. 10 is a dispersion curve diagram obtained by inversion based on the fitting point mean and the fitting coefficient according to the embodiment of the present invention.
As shown in FIG. 10, all fitting point mean values P 'in the simulation model database' hps Making a comparison, only keeping
Fitting coefficient Q when listening to corresponding thickness h, longitudinal wave velocity p and transverse wave velocity s hps . Then, these fitting coefficients Q are again used hps In selecting Q hps，max (i.e. the maximum fitting coefficient),at this time Q hps，max The corresponding thickness h, longitudinal wave velocity p and transverse wave velocity s are the optimal inversion parameters. And when the wave speed of the transverse wave is lower than the reference value, the patient can be judged to be osteoporosis.
Examples effects and effects
According to the method for detecting osteoporosis of human body based on ultrasonic guided wave frequency dispersion curve provided by the embodiment, the ultrasonic signal is firstly subjected to Fourier transform to obtain an initial frequency dispersion curve, then the initial frequency dispersion curve is processed by utilizing a preset power spectrum estimation algorithm to obtain a maximum energy point track, finally fitting point matching is carried out on the maximum energy point track in a preset simulation model database by utilizing a preset search algorithm to obtain a parameter of a maximum fitting coefficient corresponding to the maximum energy point track, the transverse wave velocity in the parameter is taken as a bone density judgment basis, so that the cortical bone transverse wave velocity can be obtained by inversion through the steps under the condition that only the human bone ultrasonic signal of a patient exists, the transverse wave velocity can reflect the porosity condition of cortical bone, and non-osteoporosis and osteoporosis patients can be effectively distinguished according to the transverse wave velocity, the method has high accuracy and high reliability. In addition, only the human bone ultrasonic signals of the patient are needed, so compared with a dual-energy X-ray absorption method, the method is low in cost, does not generate radiation to the patient, has smaller calculation amount and higher real-time performance, and is suitable for long-term and periodic detection of the health degree of the human bone.
Additionally, in the embodiment, all initial dispersion data are recombined into the corresponding dispersion point matrix K' hps Thus at dispersive point matrix K' hps Under the condition that the format of the maximum energy point track is the same as that of the maximum energy point track, the parameter of the maximum fitting coefficient corresponding to the maximum energy point track is obtained in the simulation model database more quickly through a search algorithm.
The above-described embodiments are merely illustrative of specific embodiments of the present invention, and the present invention is not limited to the description of the above-described embodiments.
Claims (7)
1. A human osteoporosis detection method based on ultrasonic guided wave frequency dispersion curve is used for processing and analyzing collected ultrasonic signals of human bones to obtain corresponding bone density judgment basis, and is characterized by comprising the following steps:
step S1-1, carrying out Fourier transform on the ultrasonic signal to obtain an initial dispersion curve;
step S1-2, processing the initial dispersion curve by using a preset power spectrum estimation algorithm to obtain a maximum energy point track;
and step S1-3, performing fitting point matching in a pre-established simulation model database by using a preset search algorithm according to the maximum energy point track to obtain a parameter of a maximum fitting coefficient corresponding to the maximum energy point track, and taking the transverse wave velocity in the parameter as the bone density judgment basis so as to enable a user to distinguish non-osteoporosis and osteoporosis patients according to the bone density judgment basis.
2. The method for detecting the osteoporosis of the human body based on the ultrasonic guided wave frequency dispersion curve according to claim 1, characterized in that:
the simulation model database is a frequency dispersion curve point of skin-cortical bone-marrow three-layer simulation constructed by guided wave signals collected by a waveguide.
3. The method for detecting the osteoporosis of the human body based on the ultrasonic guided wave frequency dispersion curve according to claim 2, characterized in that:
the establishing process of the simulation model database comprises the following steps:
step S2-1, establishing an initial cortical bone thickness h 1 mm, thickness increment of delta h, and initial longitudinal wave velocity of p 1 m/s, increment of longitudinal wave velocity delta p and initial transverse wave velocity s 1 m/s and initial database with transverse wave velocity increment of deltas, wherein the initial database comprises h z *p z *s z Group initial dispersion data K hps ；
Step S2-2, recombining all the initial frequency dispersion data into corresponding frequency dispersion point matrixes K' hps To obtain a matrix K 'of all the frequency dispersion points' hps The simulation model database is constructed by the following steps,
the initial dispersion data K hps Composed of multiple frequency dispersion points, the initial frequency dispersion data K hps Including multiple modes
Where n is the number of the modes corresponding to each set of the initial dispersion data, m ═ 1,2 is the frequency and phase velocity of the dispersion points in each of the modes, and l is the arrangement order of the dispersion points in each of the modes.
4. The method for detecting the osteoporosis of the human body based on the ultrasonic guided wave frequency dispersion curve according to claim 3, characterized in that:
wherein the step S2-2 includes the following sub-steps:
step S2-2-1, searching out the corresponding phase velocity position b in each mode based on the frequency points i ：
b i ＝i
In the formula, df is the longitudinal interval of the maximum energy point trajectory A, l is the arrangement sequence of the frequency dispersion points in each mode, and c is an array formed by all the phase velocity positions corresponding to the frequency points in each arrangement sequence l;
step S2-2-2, setting the arrangement sequence l corresponding to the minimum value in the array c as a known arrangement sequence l';
step S2-2-3, calculating the wavenumber position b of the wavenumber according to the known permutation sequence l j ：
Where dk is the lateral spacing of the maximum energy point trajectory A,
for the frequency of the i' th of the dispersion point of the nth of the modes,
the phase velocity at the l' th of the dispersion point for the nth of the modes;
step S2-2-4, according to the phase velocity position b i And said wavenumber position b j Obtaining recombined initial frequency dispersion data as the frequency dispersion point matrix K' hps And the initial dispersion data K is processed hps Marking the corresponding position to obtain a marked position;
step S2-2-5, repeating the step S2-2-1 to the step S2-2-4 until all the initial dispersion data are completely recombined to obtain a matrix K 'of all the dispersion points' hps And constructing the simulation model database.
5. The method for detecting osteoporosis of a human body based on the ultrasonic guided wave frequency dispersion curve according to claim 4, characterized in that:
wherein the step S1-3 includes the following substeps:
step S1-3-1, judging whether the energy E of the grid points in the maximum energy point track is larger than a preset energy threshold value T or not;
step S1-3-2, recording the position corresponding to the grid point as the position to be matched when the judgment of the step S1-3-1 is positive;
step S1-3-3, searching whether the mark position corresponding to the position to be matched exists in the simulation model database through the search algorithm;
step S1-3-4, when the judgment of the step S1-3-3 is yes, according to the energy E of the grid point corresponding to the mark position and the fitting point P hps Calculating to obtain a fitting coefficient Q hps And fitting points are allValue P' hps ：
P hps ＝P hps +1
Step S1-3-5, repeating the step S1-3-1 to the step S1-3-4 until obtaining the fitting coefficient Q corresponding to each grid point in the maximum energy point trajectory hps And the synthetic point mean value P' hps ；
Step S1-3-6, according to all the fitting coefficients Q hps And all of the fitted point mean values P' hps Selecting a fitting coefficient Q hps And taking the maximum value as the maximum fitting coefficient, and taking the transverse wave speed in the parameter in the maximum fitting coefficient as the bone density judgment basis.
6. The method for detecting osteoporosis of a human body based on the ultrasonic guided wave frequency dispersion curve according to claim 1, characterized in that:
wherein the power spectrum estimation algorithm in the step S1-2 is a Burg algorithm.
7. The method for detecting the osteoporosis of the human body based on the ultrasonic guided wave frequency dispersion curve according to claim 1, characterized in that:
wherein the search algorithm in the step S1-3 is a grid search algorithm.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116509453A (en) * | 2023-06-29 | 2023-08-01 | 南京科进实业有限公司 | Tibia bone density detection system and method based on ultrasonic waves |
| CN116509453B (en) * | 2023-06-29 | 2023-09-01 | 南京科进实业有限公司 | Tibia bone density detection system and method based on ultrasonic waves |
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