CN104873221A - Ultrasonic-based liver fat quantification method and system - Google Patents

Abstract

The invention provides an ultrasonic-based liver fat quantification method and system. According to recommendations, the system comprises an ultrasonic transmitter, an ultrasonic receiver, an ultrasonic signal processing module, a liver near-field positioning module, a liver far-field positioning module and an ultrasonic attenuation coefficient calculating module; the ultrasonic transmitter is used for transmitting pulse ultrasonic to liver tissues; the ultrasonic receiver is used for receiving ultrasonic echo signals of the liver tissues; the ultrasonic signal processing module is used for compensating, sampling, demodulating, envelope detecting or filtering of the ultrasonic echo signals of the liver tissues, so as to obtain processed ultrasonic echo corrected signals; the liver near-field positioning module is used for positioning a near-field region RN of a liver; the liver far-field positioning module is used for positioning a far-region of the liver; the ultrasonic attenuation coefficient calculating module is used for calculating an ultrasonic attenuation coefficient Epsilon of the liver.

Description

Based on hyperacoustic liver fat quantitative approach and system

Technical field

The present invention relates to a kind of based on hyperacoustic liver fat quantitative approach and system.

Background technology

At present, fatty liver is a kind of common DHD.Diagnosis morning of fatty liver, early treatment can effectively stop the progress of chronic hepatopathy and improve its prognosis.

Clinically, doctor can use ultrasonoscopy to carry out etiologic diagnosis to fatty liver, the method has that sensitivity is high, simple operation, low cost advantage, but its specificity is poor, comparatively large by doctor's subjective impact, therefore can only as a kind of etiologic diagnosis method.

CT (Computed Tomography, computed tomography) measures liver density and accurately can detect Liver fatty deposition, if be aided with correcting CT testing tube, then can accurate quantification liver fat, but there is the risk of radiation in it.

Nuclear magnetic resonance (Magnetic Resonance Imaging, and Magnetic Resonance Spectrum (MagneticResonance Spectrum MRI), MRS) a small amount of lipidosis in hepatocyte can be found, but it is the same with CT, it is expensive and the review time is long, not easily extensively promotes the use of.

Summary of the invention

In order to solve at least one problem mentioned in background technology, the invention provides a kind of based on hyperacoustic liver fat quantitative approach and system.

The present invention for achieving the above object, adopts following technical scheme: a kind of based on hyperacoustic liver fat quantitative approach, it to liver organization emission pulse ultrasonic, utilizes ultrasonic receiver to receive liver near-field region R respectively by ultrasonic transmitter _nultrasound echo signal S ₁with liver far-field region R _fultrasound echo signal S ₂, to ultrasound echo signal S ₁and S ₂carry out signal processing respectively and obtain ultrasonic echo corrected signal S _p1and S _p2, according to ultrasonic echo corrected signal S _p1and S _p2calculate the ultrasonic wave attenuation coefficient ε of liver, its computing formula is:

$\mathrm{\ϵ}=\frac{\ln \left(I_{R_N}\right)-\ln \left(I_{R_F}\right)}{k*\mathrm{\Δd}*f};$

Wherein, for near-field region R _nultrasonic echo corrected signal S _p1the value of intensity or meansigma methods, for far-field region R _fultrasonic echo corrected signal S _p2the value of intensity or meansigma methods, Δ d is near-field region R _nwith far-field region R _fbetween distance, f is the mid frequency of ultrasonic echo corrected signal, and k is distance coefficient.

Further, the near-field region R of described liver _n, and far-field region R _fautomatically identify location, its localization method is as follows:

1), the ultrasonic echo corrected signal after process is divided into multiple detection subregion;

2), the eigenvalue of the ultrasonic echo corrected signal of liver in each detection subregion is calculated, and according to the near-field region R of this eigenvalue determination liver _n, and far-field region R _f.

Further, to the near-field region R of liver _n, and far-field region R _fwhen carrying out automatically identifying location, define a ultrasound echo signal and comprise n sampled point, the corresponding hepatic scan degree of depth is d, then every 1mm deep packet is containing n/d point, take z as spacing, n sampled point is divided into { d/z} section, { d/z}, for rounding up, is expressed as S _i, i is the integer from 1 to t, t={d/z}-1, and the 1st section comprises { zn/d} point respectively to t segment signal;

Calculate each segment signal S respectively _inakagami to distribute m value m _i, the probability density function of ultrasound echo signal R can be expressed as:

$f\left(r\right)=\frac{{2m}^m{r}^{2m-1}}{\mathrm{\Γ}\left(m\right){\mathrm{\Ω}}^m}\exp (-\frac{m}{\mathrm{\Ω}}{r}^2)U\left(r\right),$

Wherein, Γ (m) and U (r) represents gamma function and unit-step function respectively; M is Nakagami distribution m value, can be calculated by following formula:

$m=\frac{{\left[E\left(R^2\right)\right]}^2}{E{[R^2-E\left(R^2\right)]}^2},$

Wherein, E (R ²), E [R ²-E (R ²)] ²for mean value function,

When m value is in 0 to 1 scope, ultrasound echo signal obeys pre-Rayleigh distribution; When m value equals 1, ultrasound echo signal obeys Rayleigh distribution; When m value is greater than 1, ultrasound echo signal obeys post-Rayleigh distribution,

Calculate each segment signal S respectively _iaverage M _iwith standard deviation SD _i,

Ergodic signals S _i, i=[1, t], if a certain segment signal S _jmeet M _j∈ [M _n1, M _n2], SD _j∈ [SD _n1, SD _n2], d _j∈ [d _nP1, d _nP2] and m _j∈ [m _n1, m _n2], then by signal S _jcorresponding region is set to liver near-field region R _n, wherein, M _n1and M _n2be respectively the upper and lower threshold value of liver near-field region average, SD _n1and SD _n2be respectively the upper and lower threshold value of liver near-field region standard deviation, d _nP1and d _nP2be respectively the upper and lower threshold value of the liver near-field region degree of depth, m _n1and m _n2be respectively the upper and lower threshold value of liver near-field region Nakagami distribution m value;

Ergodic signals S _i, i=[1, t], if a certain segment signal S _kmeet M _k∈ [M _f1, M _f2], SD _k∈ [SD _f1, SD _f2], d _k∈ [d _fP1, d _fP2] and m _k∈ [m _f1, m _f2], then by S _kcorresponding region is set to liver far-field region R _f, wherein, M _f1and M _f2be respectively the upper and lower threshold value of liver far-field region average, SD _f1and SD _f2be respectively the upper and lower threshold value of liver far-field region standard deviation, d _fP1and d _fP2be respectively the upper and lower threshold value of the liver far-field region degree of depth, m _f1and m _f2be respectively the upper and lower threshold value of liver far-field region Nakagami distribution m value.

Further, the near-field region R of described liver _n, and far-field region R _falso constant depth can be adopted to locate, and its localization method is as follows:

The degree of depth is set at [d _n1, d _n2] region in scope is the near-field region R of liver _n, the degree of depth is set at [d _f1, d _f2] region in scope is the far-field region R of liver _f; Wherein, d _n1and d _n2be respectively the upper and lower depth threshold of near-field region; d _f1and d _f2be respectively the upper and lower depth threshold of far-field region.

Further, when constant depth is located, if those who are investigated need the near-field region R more accurately locating liver _nwith far-field region R _ftime, can according to the described near-field region R of body-mass index BMI correspondence setting of those who are investigated _nupper and lower depth threshold d _n1, d _n2, and far-field region R _fupper and lower depth threshold d _f1, d _f2.

Except above two kinds of locate modes, the near-field region R of described liver _n, and far-field region R _falso can carry out manual depth adjustment location, its localization method is as follows: user arranges liver near-field region R voluntarily _n, and far-field region R _f.

Further, the mode of described signal processing comprises following at least one or multiple: compensation, sampling, demodulation, envelope detected or Filtering Processing.

Further, the fat content C of liver is calculated by the ultrasonic wave attenuation coefficient ε of liver _fAT, its computing formula is: C _fAT=μ * ε+η, wherein, μ is the coefficient of ε, and η is constant.

Further, utilize above-mentioned ultrasound source, receive the ultrasound echo signal of kidney simultaneously, calculated the fat content C of liver by the ratio of the ultrasonic echo intensity of liver and kidney and the ultrasonic wave attenuation coefficient ε of liver _fAT, its computing formula is: C _fAT=α * r _hR+ β * ε+γ, wherein, r _hRfor the ratio of the ultrasonic echo intensity of liver and kidney, α is r _hRcoefficient, β is the coefficient of ε, and γ is constant.

To achieve these goals, the present invention also provides a kind of liver fat quantitative system based on said method, comprising:

Ultrasonic transmitter, it is for liver organization emission pulse ultrasonic;

Ultrasonic receiver, it is for receiving the ultrasound echo signal of liver organization;

Ultrasonic signal processing module, it is for carrying out signal processing to the ultrasound echo signal of liver organization, to obtain the ultrasonic echo corrected signal after processing;

Liver near field locating module, it is for locating the near-field region R of liver _n;

Liver far field locating module, it is for locating the far-field region R of liver _f;

Ultrasonic attenuation coefficient computing module, it is according to described near-field region R _nultrasonic echo corrected signal S _p1with far-field region R _fultrasonic echo corrected signal S _p2calculate ultrasonic attenuation coefficient ε;

Display module, it is for showing ultrasonic attenuation coefficient ε;

Input module, it is for receiving the input information of user;

Main control module, it is for controlling and coordinating the work between above-mentioned modules.

Beneficial effect of the present invention: the present invention is based on hyperacoustic liver fat quantitative approach and system adds liver near field locating module and liver far field locating module, the ultrasonic attenuation coefficient of liver region can be obtained more accurately, thus improve the accuracy of liver fat detection; The present invention allows operator automatically and/or manually to set liver near-field region and liver far-field region, and setting and manual setting can operate alternately automatically, improve the convenience of operation under the prerequisite ensureing accuracy; The present invention compensates ultrasonic signal, sample, demodulation, at least one in envelope detected and Filtering Processing, to improve the accuracy and reliability that liver fat detects.

Accompanying drawing explanation

Fig. 1 is the system architecture diagram that the present invention is based on hyperacoustic liver fat quantitative approach and system.

Fig. 2 is the method flow diagram that the present invention is based on hyperacoustic liver fat quantitative approach and system.

Detailed description of the invention

Shown in Fig. 1, relate to a kind of based on hyperacoustic liver fat quantitative system,

Comprise ultrasonic transmitter, it is for liver organization emission pulse ultrasonic;

Ultrasonic receiver, it is for receiving the ultrasound echo signal of liver organization;

Ultrasonic signal processing module, it is for carrying out signal processing to the ultrasound echo signal of liver organization, to obtain the ultrasonic echo corrected signal after processing;

Liver near field locating module, it is for locating the near-field region R of liver _n;

Liver far field locating module, it is for locating the far-field region R of liver _f;

Ultrasonic attenuation coefficient computing module, it is according to described near-field region R _nultrasonic echo corrected signal S _p1with far-field region R _fultrasonic echo corrected signal S _p2calculate ultrasonic attenuation coefficient ε.

Wherein, described ultrasonic signal processing module to the signal processing mode that the ultrasound echo signal of liver organization carries out at least comprise following one or more: compensation, sampling, demodulation, envelope detected or Filtering Processing.Wherein, the computing formula of described ultrasonic attenuation coefficient ε is: wherein, for near-field region R _nultrasonic echo corrected signal S _p1the value of intensity, for far-field region R _fultrasonic echo corrected signal S _p2the value of intensity, Δ d is near-field region R _nwith far-field region R _fbetween distance, f is the mid frequency of ultrasonic echo corrected signal, and k is distance coefficient.

In addition, can also be near-field region R _nultrasonic echo corrected signal S _p1the meansigma methods of intensity. can also be far-field region R _fultrasonic echo corrected signal S _p2the meansigma methods of intensity.

After the ultrasonic wave attenuation coefficient obtaining liver, calculate hepatic fat content.Calculating hepatic fat content by ultrasonic wave attenuation coefficient is common practise, in this no longer superfluous words.

Ultrasonic attenuation coefficient ε in the present invention is also by ultrasonic echo corrected signal S _pcarry out Autoregressive Spectrum Analysis (Girault J, Ossant F, Ouahabi A, et al.Time-varying autoregressive spectralestimation for ultrasound attenuation in tissue characterization.IEEE Transactionson Ultrasonics, Ferroelectrics and Frequency Control, 1998,45 (3): 650-659) obtain.

In native system, as k=1, two groups are adopted at the independently ultrasonic transducer organizing heteropleural to be measured for ultrasonic transmitter and ultrasonic receiver; As k=2, same group of ultrasonic transducer is adopted for ultrasonic transmitter and ultrasonic receiver.Visible, the supersonic generator that the present invention comprises and receptor can adopt same group of ultrasonic transducer, also two groups of independently ultrasonic transducers can be adopted, the advantage of same group of ultrasonic transducer is adopted to be cost-saving, reduce operation complexity, adopt the advantage of two groups of independently ultrasonic transducers to be to provide mode of operation more flexibly.

Also comprise fat content computing module in native system, it is for calculating the fat content C of liver _fAT;

Display module, it is for showing ultrasonic attenuation coefficient ε;

Input module, it is for receiving the input information of user;

Main control module, it is for controlling and coordinating the work between above-mentioned modules.

In addition, in the present system, described fat content C _fATcomputing formula be: C _fAT=μ * ε+η, wherein, μ is the coefficient of ε, and η is constant.

In addition, in native system, the fat content C of liver _fATalso by Liver and kidney echo when ultrasonic attenuation coefficient ε to the fat content C of liver _fATcalculate, its computing formula is as follows:

Described fat content C _fATcomputing formula be: C _fAT=α * r _hR+ β * ε+γ, wherein, r _hRfor the ratio of the ultrasonic echo intensity of liver and kidney, α is r _hRcoefficient, β is the coefficient of ε, and γ is constant.

Wherein, in above-mentioned parameter, μ, η, α, beta, gamma is demarcated by fatty liver calibration body mould.Can also be obtained by a certain amount of clinical sample statistics.Such as: α, beta, gamma can be set as α=62.6, β=168.1, γ=-27.9.

At setting μ, η, α, during beta, gamma, can set adaptively according to the BMI of those who are investigated.

In native system, liver near field locating module and liver far field locating module are by cooperatively interacting with to liver near-field region R _n, and far-field region R _fautomatically identify location, its localization method is as follows:

1), the ultrasonic echo corrected signal after process is divided into multiple detection subregion;

2), the eigenvalue of the ultrasonic echo corrected signal of liver in each detection subregion is calculated, and according to the near-field region R of this eigenvalue determination liver _n, and far-field region R _f.

In native system, liver near field locating module, liver far field locating module are also by cooperatively interacting with to liver near-field region R _n, and far-field region R _fbe fixed depth localization, its localization method is as follows:

The degree of depth is set at [d _n1, d _n2] region in scope is liver near-field region R _n, the degree of depth is set at [d _f1, d _f2] region in scope is liver far-field region R _f; Wherein, d _n1and d _n2be respectively the upper and lower depth threshold of near-field region; d _f1and d _f2be respectively the upper and lower depth threshold of far-field region.During use, can according to the upper and lower depth threshold d of the described near-field region of body-mass index BMI correspondence setting of those who are investigated _n1, d _n2, and the upper and lower depth threshold d of far-field region _f1, d _f2.

In native system, liver near field locating module, liver far field locating module are also by matching with to liver near-field region R _n, and far-field region R _fcarry out manual depth adjustment location, its localization method is as follows: user arranges liver near-field region R by described input module _n, and far-field region R _fdirty.

In native system, automatically identify liver near-field region R by such as under type _n, far-field region R _f:

If one ultrasound echo signal comprises n sampled point, corresponding scan depths is d (unit: mm), then every 1mm deep packet is containing n/d point.With z (unit: mm) for spacing, n sampled point is divided into d/z} section (d/z} is for rounding up), be expressed as S _i, i is the integer from 1 to t, t={d/z}-1.1st section comprises { zn/d} point respectively to t segment signal.

Calculate each segment signal S respectively _inakagami to distribute m value m _i.Nakagami statistical model is the one of Ultrasonic tissue characterization technology, based on Nakagami statistical model, the probability density function of ultrasound echo signal R can be expressed as (P.M.Shankar, " A general statistical model for ultrasonicbackscattering from tissues; " IEEE Trans.Ultrason.Ferroelec.Freq.Contr., 2000 (47): 727-736):

$f\left(r\right)=\frac{{2m}^m{r}^{2m-1}}{\mathrm{\Γ}\left(m\right){\mathrm{\Ω}}^m}\exp (-\frac{m}{\mathrm{\Ω}}{r}^2)U\left(r\right),$

Wherein, Γ (m) and U (r) represents gamma function and unit-step function respectively; M is Nakagami distribution m value, can be calculated by following formula:

$m=\frac{{\left[E\left(R^2\right)\right]}^2}{E{[R^2-E\left(R^2\right)]}^2},$

Wherein, E (R ²), E [R ²-E (R ²)] ²for mean value function.When m value is in (0,1) scope, ultrasound echo signal obeys pre-Rayleigh distribution; When m value equals 1, ultrasound echo signal obeys Rayleigh distribution; When m value is greater than 1, ultrasound echo signal obeys post-Rayleigh distribution.

Calculate each segment signal S respectively _iaverage M _iwith standard deviation SD _i.

Ergodic signals S _i, i=[1, t], if a certain segment signal S _jmeet M _j∈ [M _n1, M _n2], SD _j∈ [SD _n1, SD _n2], d _j∈ [d _nP1, d _nP2] and m _j∈ [m _n1, m _n2], then by signal S _jcorresponding region is set to liver near-field region R _n, wherein, M _n1and M _n2be respectively the upper and lower threshold value of liver near-field region average, SD _n1and SD _n2be respectively the upper and lower threshold value of liver near-field region standard deviation, d _nP1and d _nP2be respectively the upper and lower threshold value of the liver near-field region degree of depth, m _n1and m _n2be respectively the upper and lower threshold value of liver near-field region Nakagami distribution m value.

Ergodic signals S _i, i=[1, t], if a certain segment signal S _kmeet M _k∈ [M _f1, M _f2], SD _k∈ [SD _f1, SD _f2], d _k∈ [d _fP1, d _fP2] and m _k∈ [m _f1, m _f2], then by S _kcorresponding region is set to liver far-field region R _f, wherein, M _f1and M _f2be respectively the upper and lower threshold value of liver far-field region average, SD _f1and SD _f2be respectively the upper and lower threshold value of liver far-field region standard deviation, d _fP1and d _fP2be respectively the upper and lower threshold value of the liver far-field region degree of depth, m _f1and m _f2be respectively the upper and lower threshold value of liver far-field region Nakagami distribution m value.

It should be noted that, " comprising ", " comprising " of recording in literary composition or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, article or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, article or equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment comprising described key element and also there is other identical element.

To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the present invention.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein can without departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.

Claims (10)

1., based on a hyperacoustic liver fat quantitative approach, it is characterized in that, comprise the following steps:

To liver organization emission pulse ultrasonic;

Receive liver near-field region R respectively _nultrasound echo signal S ₁with liver far-field region R _fultrasound echo signal S ₂;

To S ₁and S ₂carry out signal processing respectively and obtain ultrasonic echo corrected signal S _p1and S _p2;

Calculate the ultrasonic wave attenuation coefficient ε of liver, its computing formula is:

$\mathrm{\ϵ}=\frac{\ln \left(I_{R_N}\right)-\ln \left(I_{R_F}\right)}{k*\mathrm{\Δd}*f};$

Wherein, for near-field region R _nultrasonic echo corrected signal S _p1the value of intensity or meansigma methods, for far-field region R _fultrasonic echo corrected signal S _p2the value of intensity or meansigma methods, Δ d is near-field region R _nwith far-field region R _fbetween distance, f is the mid frequency of ultrasonic echo corrected signal, and k is distance coefficient.

2. as claimed in claim 1 based on hyperacoustic liver fat quantitative approach, it is characterized in that, described liver near-field region R _nwith described liver far-field region R _fautomatically identify location, its localization method is as follows:

1), by the ultrasonic echo corrected signal S after process _p1and S _p2be divided into multiple detection subregion;

2), calculate the eigenvalue of the ultrasonic echo corrected signal of liver in each detection subregion, and determine described liver near-field region R according to this eigenvalue _nwith described liver far-field region R _f.

3. one as claimed in claim 2 is based on hyperacoustic liver fat quantitative approach, it is characterized in that,

Define a ultrasound echo signal and comprise n sampled point, the corresponding hepatic scan degree of depth is d, then every 1mm deep packet is containing n/d point, take z as spacing, n sampled point is divided into { d/z} section, { d/z}, for rounding up, is expressed as S _i, i is the integer from 1 to t, t={d/z}-1, and the 1st section comprises { zn/d} point respectively to t segment signal;

Calculate each segment signal S respectively _inakagami to distribute m value m _i, the probability density function of ultrasound echo signal R can be expressed as:

$f\left(r\right)=\frac{2m^m{r}^{2m-1}}{\mathrm{\Γ}\left(m\right){\mathrm{\Ω}}^m}\exp (-\frac{m}{\mathrm{\Ω}}{r}^2)U\left(r\right),$

Wherein, Γ (m) and U (r) represents gamma function and unit-step function respectively; M is Nakagami distribution m value, can be calculated by following formula:

$m=\frac{{\left[E\left(R^2\right)\right]}^2}{E{[R^2-E\left(R^2\right)]}^2},$

Wherein, E (R ²), E [R ²-E (R ²)] ²for mean value function,

When m value is in 0 to 1 scope, ultrasound echo signal obeys pre-Rayleigh distribution; When m value equals 1, ultrasound echo signal obeys Rayleigh distribution; When m value is greater than 1, ultrasound echo signal obeys post-Rayleigh distribution,

Calculate each segment signal S respectively _iaverage M _iwith standard deviation SD _i,

Ergodic signals S _i, i=[1, t], if a certain segment signal S _jmeet M _j∈ [M _n1, M _n2], SD _j∈ [SD _n1, SD _n2], d _j∈ [d _nP1, d _nP2] and m _j∈ [m _n1, m _n2], then by signal S _jcorresponding region is set to liver near-field region R _n, wherein, M _n1and M _n2be respectively the upper and lower threshold value of liver near-field region average, SD _n1and SD _n2be respectively the upper and lower threshold value of liver near-field region standard deviation, d _nP1and d _nP2be respectively the upper and lower threshold value of the liver near-field region degree of depth, m _n1and m _n2be respectively the upper and lower threshold value of liver near-field region Nakagami distribution m value;

Ergodic signals S _i, i=[1, t], if a certain segment signal S _kmeet M _k∈ [M _f1, M _f2], SD _k∈ [SD _f1, SD _f2], d _k∈ [d _fP1, d _fP2] and m _k∈ [m _f1, m _f2], then by S _kcorresponding region is set to liver far-field region R _f, wherein, M _f1and M _f2be respectively the upper and lower threshold value of liver far-field region average, SD _f1and SD _f2be respectively the upper and lower threshold value of liver far-field region standard deviation, d _fP1and d _fP2be respectively the upper and lower threshold value of the liver far-field region degree of depth, m _f1and m _f2be respectively the upper and lower threshold value of liver far-field region Nakagami distribution m value.

4. as claimed in claim 1 a kind of based on hyperacoustic liver fat quantitative approach, it is characterized in that, described liver near-field region R _nwith described liver far-field region R _fbe fixed depth localization, its localization method is as follows:

The degree of depth is set at [d _n1, d _n2] region in scope is described liver near-field region R _n, the degree of depth is set at [d _f1, d _f2] region in scope is described liver far-field region R _f; Wherein, d _n1and d _n2be respectively the upper and lower depth threshold of near-field region; d _f1and d _f2be respectively the upper and lower depth threshold of far-field region.

5. one as claimed in claim 4 is based on hyperacoustic liver fat quantitative approach, it is characterized in that, according to the described liver near-field region R of body-mass index BMI correspondence setting of those who are investigated _nupper and lower depth threshold d _n1, d _n2with described liver far-field region R _fupper and lower depth threshold d _f1, d _f2.

6. as claimed in claim 1 a kind of based on hyperacoustic liver fat quantitative approach, it is characterized in that, described liver near-field region R _nwith described liver far-field region R _fcarry out manual depth adjustment location, its localization method is as follows: user arranges described liver near-field region R voluntarily _nwith described liver far-field region R _f.

7. as claimed in claim 1 based on hyperacoustic liver fat quantitative approach, it is characterized in that, the mode of described signal processing comprises at least one in compensation, sampling, demodulation, envelope detected and Filtering Processing.

8. as claimed in claim 1 based on hyperacoustic liver fat quantitative approach, it is characterized in that, calculated the fat content C of liver by the ultrasonic wave attenuation coefficient ε of liver _fAT, its computing formula is: C _fAT=μ * ε+η, wherein, μ is the coefficient of ε, and η is constant.

9. as claimed in claim 1 based on hyperacoustic liver fat quantitative approach, it is characterized in that, utilize above-mentioned ultrasound source, receive the ultrasound echo signal of kidney simultaneously, calculated the fat content C of liver by the ratio of the ultrasonic echo intensity of liver and kidney and the ultrasonic wave attenuation coefficient ε of liver _fAT, its computing formula is: C _fAT=α * r _hR+ β * ε+γ, wherein, r _hRfor the ratio of the ultrasonic echo intensity of liver and kidney, α is r _hRcoefficient, β is the coefficient of ε, and γ is constant.

10., based on a liver fat quantitative system for method described in the arbitrary claim of claim 1-9, it is characterized in that, comprising:

Ultrasonic transmitter, it is for liver organization emission pulse ultrasonic;

Ultrasonic receiver, it is for receiving the ultrasound echo signal of liver organization;

Ultrasonic signal processing module, it is for carrying out signal processing to the ultrasound echo signal of liver organization, to obtain the ultrasonic echo corrected signal after processing;

Liver near field locating module, it is for locating the near-field region R of liver _n;

Liver far field locating module, it is for locating the far-field region R of liver _f;

Ultrasonic attenuation coefficient computing module, it is according to described near-field region R _nultrasonic echo corrected signal S _p1with far-field region R _fultrasonic echo corrected signal S _p2calculate ultrasonic attenuation coefficient ε;

Display module, it is for showing described ultrasonic attenuation coefficient ε;

Input module, it is for receiving the input information of user;

Main control module, it is for controlling and coordinating the work between above-mentioned modules.