CN102247170B - Doppler imaging automatic optimization method - Google Patents

Abstract

The invention provides an automatic optimization method for Doppler imaging, which monitors a spectrogram and estimates optimized parameters in real time in the Doppler imaging process, once a user starts optimized scanning, a system can respond to the requirements of the user in real time and set the optimized parameters estimated in real time into the current Doppler imaging module, thereby avoiding inconvenience brought to the user due to long transient process. In addition, the invention can also easily realize real-time spectrogram optimization, namely, a user does not need to press a key to start the optimization process, but the system automatically starts the optimization process, for example, a new optimized imaging parameter is automatically set after one-screen spectrogram is refreshed, or the new optimized imaging parameter is automatically set when the difference between the real-time estimated optimization parameter and the current imaging parameter exceeds a certain threshold value.

Description

A kind of doppler imaging automatic optimization method

Technical field

The invention provides a kind of doppler imaging optimization method, relate in particular to a kind of doppler imaging automatic optimization method.

Background technology

Doppler imaging is a kind of important imaging mode that detects blood flow movement velocity, but affected by the factors such as check point, the degree of depth and imaging system Scanning speed, the imaging parameters such as the velocity scale of doppler imaging (Scale) or pulse recurrence frequency (PRF), baseline position (Baseline) may not be best, need doctor manually to adjust according to practical situation, and this adjustment is difficult to once complete in the ordinary course of things, needs to adjust and repeatedly can find best imaging parameters gear.This patent provides a kind of method and apparatus automatically one or more imaging parameters such as velocity scale, baseline position, gain, spectrogram direction being optimized according to Doppler's spectrogram, by one-touch, with regard to the parameter that enters at once to have optimized, carry out the pattern of imaging, greatly facilitate doctor's use.

Doppler imaging parameters Automatic Optimal is to improve a kind of important technology of doppler imaging ease for use, this is studied and invent more.General thinking is to start and optimize after button user, adopt larger velocity scale to carry out doppler imaging, then after having waited for the imaging of one or several cardiac cycle, spectrogram state to one or several cardiac cycle of described acquisition carries out technical Analysis, thereby obtain best imaging parameters, and the parameter after these are optimized is for follow-up doppler imaging.The object that atlas technology is analysed is in order to obtain the information such as the blood flow signal power on spectrogram, shared bandwidth, direction, for obtaining passing through that these information have, the noise characteristic of the electronic device on imaging path is calculated, and to carry out recently judgement with the noise situations of spectrogram be blood flow signal or noise; Some technology are by some statistical methods, as the average of local spectrogram, variance etc. are carried out spectrogram analysis, to distinguish blood flow and noise; In the technology of the shared bandwidth of signal calculated, the positive and negative border of passing through search signal on the mean power spectral line of a period of time having, the mean power spectral line that passes through a period of time having and peak power spectral line carry out determining of border; On judgement blood flow direction, some methods judge by the template prestoring; The energy that utilizes the different directions on spectral line having and judge etc.

The optimization method of Doppler imaging parameters is a lot, but the complexity of algorithm, robustness and response speed etc. are still research emphasis.

Prior art is pressed and is optimized after button user, generally first with larger velocity scale, carry out the imaging of at least one cardiac cycle, after the imaging that completes this stage, estimate the imaging parameters after optimization, then system is set to the imaging parameters obtaining after optimizing in the module of doppler imaging, and system will be carried out normal imaging by the parameter after optimizing.The process of the optimum imaging parameters of above-mentioned estimation is looked the efficiency faster or slower of algorithm, but generally all has an obvious transient process.

Summary of the invention

The invention provides a kind of doppler imaging automatic optimization method, it is in the process of doppler imaging, carry out in real time the monitoring of spectrogram and the estimation of parameters optimization, once starting, user optimizes scanning, system can real-time response user demand, the parameters optimization of estimating is in real time set in current doppler imaging module, thereby has avoided the long inconvenience that user's use is brought of transient process.In addition, the present invention can also be very easy to realize real-time spectrogram optimization, without user key-press, start optimizing process, but system starts optimizing process automatically, such as completing a screen spectrogram, refresh the imaging parameters after the optimization that rear Lookup protocol is new, or differ over the imaging parameters after after certain specific threshold being the optimization that Lookup protocol is new when the parameters optimization of estimating in real time and the parameter of current imaging.

The present invention solves the problems of the technologies described above adopted technical scheme to be:

An automatic optimization method, it comprises the following steps:

A. to be greater than corresponding pulse recurrence frequency or the sample rate of speed stage of active user's setting, to carry out doppler imaging and obtain Doppler signal;

B. the described Doppler signal obtaining divides two-way to process, the Doppler signal of the speed stage that one road Doppler signal regulates through down-sampled acquisition respective user, and described down-sampled Doppler signal is carried out to traditional Doppler signal and process acquisition spectrogram, voice output; Another road Doppler signal, for the frequency range of Real-Time Monitoring Doppler signal, is estimated optimum Doppler imaging parameters in real time;

C. after start optimizing by the optimum Doppler imaging parameters estimating for controlling the processing of follow-up link, realize the Automatic Optimal of Doppler imaging parameters.

The described pulse recurrence frequency of using in the imaging of A step described in Pulsed-Wave Doppler imaging is the maximum impulse repetition rate that can reach under the current degree of depth.

The maximum sample rate that the described sample rate of using in the imaging of A step described in continuous wave Doppler imaging is supported for system.

Real-Time Monitoring Doppler signal in described C step is the signal before wall filtering is processed, and estimates in real time carrying out wall filtering processing after optimum Doppler imaging parameters.

Real-Time Monitoring Doppler signal in described C step is the signal after processing through wall filtering.

Described in described step C, starting the mode of optimizing is that user manually boots or imaging system starts automatically.

The method of the frequency range of Real-Time Monitoring Doppler signal described in described step B further comprises following steps:

B1. described Doppler signal is carried out to spectra calculation;

B2. detect in real time the peak power on each Frequency point in a period of time, obtain peak power spectral curve;

B3. described peak power spectral curve is carried out to the judgement of signal and noise, the threshold decision method that adopted is processed in judgement, the noise average power needing before judgement adopt online real-time estimation method or in system, record in advance, according to setting in advance parameter k ₁determine noise threshold

power to all frequencies of peak power spectral curve judges, is greater than

be judged as signal and be designated as 1, otherwise be judged as noise, be designated as 0, obtain a signal noise vector;

B4. described signal noise vector is carried out to statistical disposition, add up the length of each segment signal within the scope of whole frequency domain, signal length is less than given threshold k ₂, the glitch for noise causes, sets to 0 this section of glitch to deserved array section;

B5. the described signal noise vector after processing is sued for peace, summed result is N, be judged to be signal and have serious spectral aliasing, and higher pulse recurrence frequency or the sample rate of startup carried out doppler imaging, otherwise serious spectral aliasing does not occur decision signal, according to the statistical result of signal vector and distribution, estimate optimum Doppler imaging parameters.

The Doppler imaging parameters of processing for follow-up signal that described step B5 estimates comprises optimum reduce sampling frequency DSR _prf, its computational methods are:

${\mathrm{DSR}}_{\mathrm{prf}}=\frac{k*N}{\mathrm{SumSNV}}$

Wherein N is described signal noise vector length, and SumSNV is described signal noise vector cumulative sum, and k is a predefined correction factor.

The Doppler imaging parameters of processing for follow-up signal that described step B5 estimates comprises optimal base line position parameter, before estimating optimal base line position parameter, first carry out the judgement of blood flow direction, and estimate the bandwidth L of reverse blood flow doppler signal, with this bandwidth L and PRF reduce sampling frequency, PRF reduce sampling frequency correction factor k, blood flow direction, determine the baseline position after optimizing:

Judgement blood flow direction is negative optimum baseline

$\mathrm{BaseLine}=\frac{1+k}{2}*N-L*{\mathrm{DSR}}_{\mathrm{prf}}$

Judgement blood flow direction is positive optimum baseline

$\mathrm{BaseLine}=\frac{1-k}{2}*N+L*{\mathrm{DSR}}_{\mathrm{prf}}$

First described blood flow direction judgement is multiplied by signal noise vector direction vector PDV (power spectum direction vector), direction vector is N/2 individual-1 and N/2 1 composition, the described vector obtaining the multiplying each other summation that adds up, when accumulation result is nonnegative number, judge that blood flow is as forward; When accumulation result is negative, judge that blood flow is as negative sense.

The present invention is divided into two-way by Doppler, process for Doppler signal on one tunnel, monitoring and imaging parameters optimization are in real time carried out to Doppler's spectrogram in one tunnel, when starting optimization, the parameter of having optimized is used for to imaging, thereby realizes real-time response, in addition by peak power spectral line being carried out to the processing of frequency-domain and time-domain, and noise signal vector is carried out to conditionality restriction, make algorithm there is stronger robustness.In a word, the invention provides Doppler parameter optimization method and the device of a kind of real-time response, simple efficient, strong robustness.

Accompanying drawing explanation

Fig. 1 is that embodiment of the present invention PW Doppler signal is processed block diagram;

Fig. 2 is parameter optimization embodiment 1 of the present invention;

Fig. 3 is embodiment of the present invention spectrogram pattern diagram.

The specific embodiment

According to drawings and embodiments the present invention is described in further detail below:

The present invention is all applicable to PW and CW doppler imaging, the sample rate of CW signal can with the pulse recurrence frequency equivalent process of PW doppler imaging, the present embodiment just be take PW doppler imaging as example, and scheme of the present invention is described in detail.In the present embodiment, key point is: PW Doppler scanning scans with the maximum impulse repetition rate under the current degree of depth, according to speed stage, the Doppler signal of the higher pulse repetition frequencies obtaining is carried out down-sampledly, then carries out follow-up signal processing; The Doppler signal of the higher pulse repetition frequencies obtaining is carried out to real-time monitoring simultaneously, calculate in real time the Doppler imaging parameters such as optimum baseline position and pulse recurrence frequency.

Adopt a kind of Doppler signal of the present embodiment to process block diagram as shown in Figure 1.As seen from the figure, after wall filtering, (also can before wall filtering) reserves a road for carrying out the processing of Doppler parameter Automatic Optimal, in this processing links, Doppler signal carried out calculating in real time the Doppler imaging parameters such as optimum baseline position and pulse recurrence frequency.When pressing Doppler, user optimizes (or the optimization automatically being triggered by system is moved) after button in real-time spectrogram optimization situation, directly the parameters such as the good baseline of upper suboptimization and pulse recurrence frequency are used for controlling the links such as baseline translation, down-sampled and signal processing, thereby realize the Automatic Optimal of Doppler imaging parameters.And first traditional doppler imaging optimization method need to change the pulse recurrence frequency of scanning, after waiting for that optimum results out, again change scanning impulse repetition rate value and scan, so the method for the present embodiment proposition can be accomplished complete real-time response user's optimization demand.

The flow process of a kind of concrete optimum embodiment that Auto processes is as Fig. 2, described embodiment has carried out automatic estimation to baseline and two parameters of pulse recurrence frequency, the embodiment that Auto processes is not limited to this two parameters, such as providing the Gain Automatic estimation of optimum imaging according to the estimation of signal to noise ratio, according to the form of spectrum, judge the direction of blood flow etc.

First wall filtering result is carried out to spectra calculation and processing, then the signal on spectrogram and noise situations are judged, judged result is kept in a vector, noise signal vector is processed, on the basis of signal noise situation vector, carried out repeatedly the processing such as aliasing judgement, an aliasing pattern confirmation and parameter optimization.

Several links of below auto being processed describe:

(1) spectra calculation and peak power spectrum are estimated

Wall filtering result is carried out to spectra calculation, and spectra calculation method can adopt and traditional Doppler's spectra calculation same procedure, i.e. fast Fourier transform method (FFT).

Peak power spectrum estimates to refer to not maximum power value in the same time of each Frequency point of estimating within predefined a period of time.The estimation of this peak power spectrum can spectral line of every input, all spectral lines in up-to-date a period of time is carried out the estimation of peak power spectrum, although do like this can real-time tracking peak power spectrum variation, amount of calculation can be larger.Because blood flow rate is to have metastable velocity interval to specific vessel position, therefore also can adopt the method for simplification, carry out at set intervals the estimation of a peak power spectrum.Within this period of time, first spectral line is saved in peak power spectral line memory block, since second power spectral line, calculate peak power spectral line, on each Frequency point, current power spectral line and the front peak power spectral line once calculating are compared, choose larger performance number as the performance number under frequency.After obtaining current peak power spectral line, upgrade the peak power spectral line in memory block.Complete after the peak power spectrum estimation of scheduled time length, restart the peak power spectrum of a new round and estimate.

(2) peak power spectrum is processed

Current peak power spectral line is carried out to linear or nonlinear level and smooth (such as conventional FIR/IIR Filtering Processing) at frequency-domain and time-domain and reduce affected by noisely, improve the robustness of algorithm.

(3) signal noise vector and processing

To the peak power spectral line after processing

carry out the judgement of signal and noise, judgement is processed and can be adopted the threshold decision method of simply crossing.The mean power that needed to know noise before judgement, noise average power online real-time estimation method of the prior art can be adopted, also noise average power can be recorded in advance.Then parameters k1 determines noise threshold

the power of peak power after processing being composed to all frequencies judges, is greater than

for signal, otherwise be noise, the signal noise situation (SNV) that represents current spectral line with the vector that a length is N (frequency that N obtains while calculating for spectrum is counted), vector each is corresponding one by one with Frequency point, current Frequency point be judged as signal respective frequencies point be made as 1, otherwise be set to 0.

Then signal noise vector is carried out to statistical disposition, add up the length of each segment signal within the scope of whole frequency domain, if signal length is less than given threshold k ₂, just think the glitch that noise causes, this section of glitch is set to 0 to deserved SNV section.

If SNV vector is 00011100...0111111111111...100000, signal length threshold value is 10, it is long that first paragraph signal only has 3 cps, thinks the glitch that noise causes, the SNV vector after processing is: 00000000...0111111111111...100000

(4) repeatedly aliasing judgement

Signal noise vector after processing is sued for peace, if summed result is N, illustrate that signal is full of whole frequency band, probably there is repeatedly aliasing, need to start higher pulse recurrence frequency or sample rate imaging, otherwise signal does not have repeatedly aliasing, can carry out follow-up optimization.That is:

$\mathrm{SumSNV}=\underset{N}{\mathrm{\Σ}}{\mathrm{SNV}}_t\left(i\right)$

If SumSNV=N, shows that signal is full of whole spectrogram, there is repeatedly aliasing, start higher pulse recurrence frequency or sample rate imaging;

If SumSNV < is N, carry out the optimization of baseline and pulse recurrence frequency reduce sampling frequency.

(5) one times aliasing pattern is confirmed

In the situation that there is not repeatedly aliasing, the pattern of Doppler's spectrogram mainly contains a shown in Fig. 3, b, and c, tetra-kinds of d, each can be divided into again without aliasing, critical aliasing and three kinds of situations of an aliasing.

1), for the situation without aliasing, compose corresponding SNV form and be: 0..001..110..00, have end to end several 0, middle several 1.

2), for the situation of critical aliasing, compose corresponding SNV form and be: 1..110..00 or 0..001..11, head or tail have several 0, all the other regions are 1

3), for the situation of aliasing, compose corresponding SNV form and be: 1..110..001..11, have end to end several 1, centre have several 0

I.e. 0 and 1 variation only has 1 time or 2 times, otherwise algorithm failure or other reasons are just described, if can not recover within the predetermined time normally, not start optimization for the situation of algorithm failure.

(6) pulse recurrence frequency reduce sampling frequency is optimized

SNV cumulative sum SumSNV is the spectral space that signal occupies, pulse recurrence frequency reduce sampling frequency DSR _prffor:

k is adjustment factor, and value is 0～1, and in order to prevent down-sampled rear generation aliasing, k must satisfy condition:

$k\&GreaterEqual;\frac{\mathrm{SumSNV}}{N}$

(7) baseline optimized

In order to eliminate an aliasing or spectral line to be moved on to suitable position, show, need to carry out baseline optimized according to spectral model.

1. blood flow direction is determined

In spectral model as shown in Figure 3, a and d are positive frequency direction, and b and c are negative frequency direction.By method below, determine frequency direction.

1) SNV is multiplied by direction vector PDV (power spectum direction vector)

Direction vector is N/2 individual-1 and N/2 1 composition,

-1，...，-1，-1，1...，1，1

dSNV＝SNV*PDV

2) dSNV is sued for peace

$\mathrm{SumdSNV}=\underset{N}{\mathrm{\&Sigma;}}\mathrm{dSNV}\left(i\right)$

3) according to SumdSNV, judge

SumdSNV < 0, expression blood flow direction is negative direction

SumdSNV >=0, expression blood flow direction is positive direction

2. baseline optimized

From N/2, start, calculate and the shared bandwidth L of the reciprocal signal of blood flow, blood flow direction, for just, starts to negative frequency direction, SNV to be added up from N/2, runs into 0 and just stops adding up, and cumulative sum is exactly L; Same is negative situation to blood flow direction, from N/2, starts to positive frequency direction, SNV to be added up, and runs into 0 and just stops adding up, and cumulative sum is exactly L.

$L=\underset{i=N/2}{\overset{q}{\mathrm{\&Sigma;}}}\mathrm{SNV}\left(i\right)$

As SumdSNV < 0, q subtracts 1 for search for first call number that is 0 of SNV to positive frequency direction

When SumdSNV >=0, q adds 1 for search for first call number that is 0 of SNV to negative frequency direction

1) blood flow direction is negative baseline

$\mathrm{BaseLine}=\frac{1+k}{2}*N-L*{\mathrm{DSR}}_{\mathrm{prf}}$

2) blood flow direction is positive baseline

$\mathrm{BaseLine}=\frac{1-k}{2}*N+L*{\mathrm{DSR}}_{\mathrm{prf}}$

Above-described embodiment has just provided a kind of description of optimum embodiment, peak power spectrum processing links can be omitted, atuo processes can be before wall filtering, also can after wall filtering, carry out, down-sampled and order baseline translation also can be exchanged, etc. these change and do not depart from core concept of the present invention.

Those skilled in the art do not depart from essence of the present invention and spirit, can there is various deformation scheme to realize the present invention, the foregoing is only the better feasible embodiment of the present invention, not thereby limit to interest field of the present invention, the equivalent structure that all utilizations description of the present invention and accompanying drawing content are done changes, within being all contained in interest field of the present invention.

Claims (9)

1. a Doppler imaging parameters automatic optimization method, is characterized in that comprising the following steps:

A. to be greater than corresponding pulse recurrence frequency or the sample rate of speed stage of active user's setting, to carry out doppler imaging and obtain Doppler signal;

B. the described Doppler signal obtaining divides two-way to process, the Doppler signal of the speed stage that one road Doppler signal regulates through down-sampled acquisition respective user, and described down-sampled Doppler signal is carried out to traditional Doppler signal and process acquisition spectrogram, voice output; Another road Doppler signal, for the frequency range of Real-Time Monitoring Doppler signal, is estimated optimum Doppler imaging parameters in real time;

C. after start optimizing by the optimum Doppler imaging parameters estimating for controlling the processing of follow-up link, realize the Automatic Optimal of Doppler imaging parameters;

Wherein, the method for the frequency range of Real-Time Monitoring Doppler signal described in described step B further comprises following steps:

B1. described Doppler signal is carried out to spectra calculation;

B2. detect in real time the peak power on each Frequency point in a period of time, obtain peak power spectral curve;

B3. described peak power spectral curve is carried out to the judgement of signal and noise, the threshold decision method that adopted is processed in judgement, the noise average power needing before judgement

adopt online real-time estimation method or in system, record in advance, according to setting in advance parameter k ₁determine noise threshold

power to all frequencies of peak power spectral curve judges, is greater than

be judged as signal and be designated as 1, otherwise be judged as noise, be designated as 0, obtain a signal noise vector;

B4. described signal noise vector is carried out to statistical disposition, add up the length of each segment signal within the scope of whole frequency domain, signal length is less than given threshold k ₂, the glitch for noise causes, sets to 0 this section of array section corresponding to glitch;

B5. the described signal noise vector after processing is sued for peace, if summed result is N, be judged to be signal and have serious spectral aliasing, and higher pulse recurrence frequency or the sample rate of startup carried out doppler imaging, otherwise serious spectral aliasing does not occur decision signal, according to the statistical result of signal vector and distribution, estimate optimum Doppler imaging parameters;

The frequency obtaining when wherein N calculates for spectrum is counted.

2. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 1, is characterized in that: the described pulse recurrence frequency of using in the imaging of A step described in Pulsed-Wave Doppler imaging is the maximum impulse repetition rate that can reach under the current degree of depth.

3. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 1, is characterized in that: the maximum sample rate that the described sample rate of using in the imaging of A step described in continuous wave Doppler imaging is supported for system.

4. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 1, it is characterized in that: the Real-Time Monitoring Doppler signal in described step B is the signal before wall filtering is processed, and estimates in real time to carry out wall filtering processing after optimum Doppler imaging parameters again.

5. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 1, is characterized in that: the Real-Time Monitoring Doppler signal in described step B is the signal after processing through wall filtering.

6. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 1, is characterized in that: described in described step C, starting the mode of optimizing is that user manually boots or imaging system starts automatically.

7. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 1, is characterized in that: the Doppler imaging parameters of processing for follow-up signal that described step B5 estimates comprises optimum reduce sampling frequency DSR _prf, its computational methods are:

${\mathrm{DSR}}_{\mathrm{prf}}=\frac{k*N}{\mathrm{SumSNV}}$

Wherein SumSNV is described signal noise vector cumulative sum, and k is a predefined correction factor.

8. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 7, it is characterized in that: the Doppler imaging parameters of processing for follow-up signal that described step B5 estimates comprises optimal base line position parameter, before estimating optimal base line position parameter, first carry out the judgement of blood flow direction, and estimate the bandwidth L of reverse blood flow doppler signal, with this bandwidth L and PRF reduce sampling frequency, PRF reduce sampling frequency correction factor k, blood flow direction, determine the baseline position after optimizing:

Judgement blood flow direction is negative optimum baseline

$\mathrm{BaseLine}=\frac{1+k}{2}*N-L*{\mathrm{DSR}}_{\mathrm{prf}}$

Judgement blood flow direction is positive optimum baseline

$\mathrm{BaseLine}=\frac{1-k}{2}*N+L*{\mathrm{DSR}}_{\mathrm{prf}}.$

9. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 8, it is characterized in that: first described blood flow direction judgement is multiplied by direction vector PDV by signal noise vector, direction vector is N/2 individual-1 and N/2 1 composition, the described vector obtaining the multiplying each other summation that adds up, when accumulation result is nonnegative number, judge that blood flow is as forward; When accumulation result is negative, judge that blood flow is as negative sense.

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