CN102247170A - 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 the blood flow movement velocity, but be subjected to factor affecting such as check point, the degree of depth and imaging system Scanning speed, velocity scale of doppler imaging (Scale) or pulse recurrence frequency (PRF), baseline position imaging parameters such as (Baseline) may not be best, need the doctor manually to adjust according to practical situation, and this in the ordinary course of things adjustment is difficult to once finish, and needs to adjust repeatedly to find best imaging parameters gear.This patent provides a kind of method and apparatus that automatically one or more imaging parameters such as velocity scale, baseline position, gain, spectrogram direction is optimized according to Doppler's spectrogram, with regard to entering at once, greatly facilitate doctor's use by one-touch to optimize the pattern that good parameter is carried out imaging.

The Doppler imaging parameters Automatic Optimal is to improve a kind of important technology of doppler imaging ease for use, this is studied and invents more.General thinking is after the user starts the optimization button, adopt bigger velocity scale to carry out doppler imaging, wait for then and finishing after 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 optimizations is used for follow-up doppler imaging.The purpose that atlas technology is analysed is in order to obtain information such as the blood flow signal power on the spectrogram, shared bandwidth, direction, for passing through of obtaining that these information have calculated the noise characteristic of the electronic device on the imaging path, and compare with the noise situations of spectrogram and to judge blood flow signal or noise; The technology that has is by some statistical methods, waits spectrogram analysis as average, the variance of local spectrogram, 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 that has, mean power spectral line that passes through a period of time that has and peak power spectral line carry out determining of border; Judging that the method that has is judged by the template that prestores on the blood flow direction; The energy that utilizes the different directions on the spectral line that has and judge etc.

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

Prior art is after the user presses the optimization button, general elder generation carries out the imaging of at least one cardiac cycle with bigger velocity scale, imaging parameters after estimating optimization after the imaging of finishing this stage, imaging parameters after system will obtain to optimize then is set in the module of doppler imaging, and system will carry out normal imaging by the parameter after optimizing.The process of the optimum imaging parameters of above-mentioned estimation is looked the efficient faster or slower of algorithm, but generally all has a more tangible 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 the monitoring of spectrogram and the estimation of parameters optimization in real time, in case starting, the user optimizes scanning, system can the real-time response user demand, the parameters optimization of estimating in real time is set in the 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, promptly need not user key-press and start optimizing process, but system starts optimizing process automatically, such as the imaging parameters of finishing after a screen spectrogram is provided with new optimization after refreshing automatically, perhaps differ above the imaging parameters after promptly new optimization being set automatically after certain certain threshold level when the parameters optimization of estimation in real time and the parameter of current imaging.

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

A kind of Doppler imaging parameters automatic optimization method, it may further comprise the steps:

A. carry out doppler imaging and obtain Doppler signal with the pulse recurrence frequency or the sample rate of the speed stage correspondence set greater than the active user;

B. the described Doppler signal of Huo Deing divides two-way to handle, the Doppler signal of the speed stage that the down-sampled acquisition respective user of one road Doppler signal process is regulated, and described down-sampled Doppler signal is carried out traditional Doppler signal handle acquisition spectrogram, voice output; Another road Doppler signal is used for monitoring in real time the frequency range of Doppler signal, estimates optimum Doppler imaging parameters in real time;

C. start the processing that the optimum Doppler imaging parameters that will estimate after optimizing is used to control 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 the impulse wave doppler imaging is following maximum impulse repetition rate that can reach of the current degree of depth.

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

Real-time monitoring Doppler signal in the described C step is the signal before the wall filtering processing, estimates in real time carrying out the wall filtering processing behind the optimum Doppler imaging parameters.

Real-time monitoring Doppler signal in the described C step is the signal after handling through wall filtering.

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

The method of monitoring the Doppler signal frequency range described in the described step B in real time further comprises following steps:

B1. described Doppler signal being carried out power spectrum calculates;

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

B3. described peak power spectral curve is carried out the judgement of signal and noise, judgment processing adopted the threshold decision method, the noise average power that needs before judging

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, greater than

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

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

B5. the described signal noise vector after handling 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 take place in decision signal, estimates optimum Doppler imaging parameters according to the statistical result and the distribution of signal vector.

The Doppler imaging parameters that is used for the follow-up signal processing that described step B5 estimates comprises optimum down-sampled rate DSR _Prf, its computational methods are:

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

Wherein N is described signal noise vector length, SumSNV be described signal noise vector add up and, k is a predefined correction factor.

The Doppler imaging parameters that is used for the follow-up signal processing that described step B5 estimates comprises optimal base line position parameter, before estimating optimal base line position parameter, at first carry out the judgement of blood flow direction, and estimate the bandwidth L of reverse blood flow doppler signal, the baseline position after determining to optimize with this bandwidth L and the down-sampled rate of PRF, the down-sampled rate correction factor of PRF k, blood flow direction:

Judge that blood flow direction is minus optimum baseline

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

Judge that blood flow direction is positive optimum baseline

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

Direction vector PDV (power spectum direction vector) is at first multiply by the signal noise vector in described blood flow direction judgement, direction vector is N/2 individual-1 and N/2 1 composition, the described vector that obtains the multiplying each other summation that adds up, when accumulation result was nonnegative number, the judgement blood flow was a forward; When accumulation result was negative, the judgement blood flow was a negative sense.

The present invention is divided into two-way with Doppler, one the tunnel is used for Doppler signal handles, one the tunnel monitors and imaging parameters optimization in real time to Doppler's spectrogram, when starting optimization, be used for imaging with optimizing good parameter, thereby realize real-time response, in addition by the peak power spectral line being carried out the processing of frequency domain and time domain, and the noise signal vector is carried out conditionality limit, make algorithm have stronger robustness.In a word, the invention provides the Doppler parameter optimization method and the device of a kind of real-time response, simple efficient, strong robustness.

Description of drawings

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

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

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

The specific embodiment

With embodiment the present invention is described in further detail with reference to the accompanying drawings below:

The present invention all is suitable for PW and CW doppler imaging, CW signals sampling rate can with the pulse recurrence frequency equivalent process of PW doppler imaging, present embodiment is an example with the PW doppler imaging just, and scheme of the present invention is described in detail.In the present embodiment, key point is: the 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 that obtains is carried out down-sampledly, carries out follow-up signal then and handles; Doppler signal to the higher pulse repetition frequencies that obtains carries out real-time monitoring simultaneously, calculates Doppler imaging parameters such as optimum baseline position and pulse recurrence frequency in real time.

Adopt a kind of Doppler signal of present embodiment to handle block diagram as shown in Figure 1.As seen from the figure, reserve one the tunnel (also can before wall filtering) behind the wall filtering and be used to carry out the Doppler parameter Automatic Optimal and handle, Doppler signal is calculated in real time Doppler imaging parameters such as optimum baseline position and pulse recurrence frequency in this processing links.When the user press Doppler optimize button after (the optimization action that perhaps under real-time spectrogram optimization situation, automatically triggers) by system, directly will go up parameters such as good baseline of suboptimization and pulse recurrence frequency and be used to control links such as baseline translation, down-sampled and signal processing, thereby realize the Automatic Optimal of Doppler imaging parameters.And traditional doppler imaging optimization method needs at first to change the pulse recurrence frequency of scanning, after coming out etc. result to be optimized, change scanning impulse repetition rate value once more and scan, so the method for present embodiment proposition can be accomplished complete real-time response user's optimization demand.

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

At first the wall filtering result being carried out power spectrum calculates and handles, then signal on the spectrogram and noise situations are judged, judged result is kept in the vector, the noise signal vector is handled, on the basis of signal noise situation vector, carried out repeatedly processing such as aliasing judgement, an aliasing pattern affirmation and parameter optimization.

The several links that auto is handled describes below:

(1) power spectrum calculates and the estimation of peak power spectrum

The wall filtering result is carried out power spectrum calculate, the power spectrum computational methods can adopt with traditional Doppler's power spectrum and calculate same procedure, i.e. fast Fourier transform method (FFT).

The peak power spectrum estimates to refer to the different maximum power values constantly of estimation each Frequency point in predefined a period of time.The estimation of this peak power spectrum can spectral line of every input, promptly all spectral lines in up-to-date a period of time is carried out the estimation of peak power spectrum, though do like this can real-time tracking peak power spectrum variation, amount of calculation can be bigger.Because blood flow rate is to have metastable velocity interval to specific vessel position, therefore also can adopt the method for simplification, promptly carry out the estimation of a peak power spectrum at set intervals.First spectral line is saved in the peak power spectral line memory block in the period at this section, calculate the peak power spectral line since second power spectral line, promptly on each Frequency point, current power spectral line and the preceding peak power spectral line that once calculates are compared, choose bigger performance number as the performance number under the frequency.After obtaining current peak power spectral line, the peak power spectral line in the updated stored district.After finishing the peak power spectrum estimation of scheduled time length, restart the peak power spectrum of a new round and estimate.

(2) the peak power spectrum is handled

Current peak power spectral line is carried out linear or nonlinear level and smooth (such as FIR/IIR Filtering Processing commonly used) in frequency domain and time domain reduce and be subjected to effect of noise, improve the robustness of algorithm.

(3) signal noise vector and processing

To the peak power spectral line after handling

Carry out the judgement of signal and noise, judgment processing can adopt the threshold decision method of simply crossing.The mean power that before judging, needs to know noise, noise average power Online real-time estimation method of the prior art can be adopted, also noise average power can be recorded in advance.Parameter k is set then ₁Determine noise threshold Peak power after handling is composed the power of all frequencies and judge, greater than

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

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

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

(4) repeatedly aliasing is judged

Signal noise vector after handling is sued for peace,, illustrate that signal is full of whole frequency band if summed result is N, repeatedly aliasing has taken place probably, 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 then signal is full of whole spectrogram, repeatedly aliasing takes place, start higher pulse recurrence frequency or sample rate imaging;

If SumSNV＜N then carries out the optimization of baseline and the down-sampled rate of pulse recurrence frequency.

(5) one times the aliasing pattern is confirmed

Under the situation that aliasing does not take place repeatedly, the pattern of Doppler's spectrogram mainly contains a shown in Figure 3, b, and c, four kinds of d, each can be divided into no aliasing, critical aliasing and three kinds of situations of an aliasing again.

1) for the situation of no aliasing, the corresponding SNV form of spectrum is: 0..001..110..00, promptly have end to end several 0, middle several 1.

2) for the situation of critical aliasing, the corresponding SNV form of spectrum is: 1..110..00 or 0..001..11, promptly head or tail have several 0, all the other zones are 1

3) for the situation of aliasing, the corresponding SNV form of spectrum is: 1..110..001..11, promptly have end to end several 1, the centre have several 0

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

(6) the down-sampled rate optimization of pulse recurrence frequency

SNV adds up and SumSNV is the spectral space that signal occupies, then the down-sampled rate DSR of pulse recurrence frequency _PrfFor:

K is an adjustment factor, and value is 0～1, in order to prevent down-sampled back aliasing takes place, and k must satisfy condition:

$k\≥\frac{\mathrm{SumSNV}}{N}$

(7) baseline optimized

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

1. blood flow direction is determined

In the spectral model as shown in Figure 3, a and d are the positive frequency direction, and b and c are the negative frequency direction.Determine frequency direction by following method.

1) SNV being multiply by direction vector PDV (power spectum direction vector) direction vector is N/2 individual-1 and N/2 1 composition, promptly

-1，...，-1，-1，1...，1，1

dSNV＝SNV*PDV

2) dSNV is sued for peace

$\mathrm{SumdSNV}=\underset{N}{\mathrm{\Σ}}\mathrm{dSNV}\left(i\right)$

3) judge according to SumdSNV

SumdSNV＜0, the expression blood flow direction is a negative direction

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

2. baseline optimized

From N/2 beginning, calculate and the shared bandwidth L of the reciprocal signal of blood flow, promptly blood flow direction then begins to the negative frequency direction SNV to be added up from N/2 for just, runs into 0 and just stops to add up, and adds up and is exactly L; Be minus situation to blood flow direction equally, begin SNV to be added up, run into 0 and just stop to add up, add up and be exactly L to the positive frequency direction from N/2.

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

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

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

1) blood flow direction is minus baseline

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

2) blood flow direction is positive baseline

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

The foregoing description has just provided a kind of description of optimum embodiment, peak power spectrum processing links can be omitted, atuo handles can be before wall filtering, also can behind wall filtering, carry out, down-sampled and the order baseline translation also can be exchanged, or the like these change and do not break away from core concept of the present invention.

Those skilled in the art do not break away from essence of the present invention and spirit, can there be the various deformation scheme to realize the present invention, the above only is the preferable feasible embodiment of the present invention, be not so 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, and all is contained within the interest field of the present invention.

Claims (10)

1. Doppler imaging parameters automatic optimization method is characterized in that may further comprise the steps:

A. carry out doppler imaging and obtain Doppler signal with the pulse recurrence frequency or the sample rate of the speed stage correspondence set greater than the active user;

B. the described Doppler signal of Huo Deing divides two-way to handle, the Doppler signal of the speed stage that the down-sampled acquisition respective user of one road Doppler signal process is regulated, and described down-sampled Doppler signal is carried out traditional Doppler signal handle acquisition spectrogram, voice output; Another road Doppler signal is used for monitoring in real time the frequency range of Doppler signal, estimates optimum Doppler imaging parameters in real time;

C. start the processing that the optimum Doppler imaging parameters that will estimate after optimizing is used to control follow-up link, realize the Automatic Optimal of Doppler imaging parameters.

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 the impulse wave doppler imaging is following maximum impulse repetition rate that can reach of 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 the continuous wave Doppler imaging is supported as 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 the described C step is the signal before the wall filtering processing, estimates in real time carrying out the wall filtering processing behind the optimum Doppler imaging parameters.

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 the described C step is the signal after handling through wall filtering.

6. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 1 is characterized in that: starting the mode of optimizing described in the described step C is that the 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 method for monitoring the Doppler signal frequency range described in the described step B in real time further comprises following steps:

B1. described Doppler signal being carried out power spectrum calculates;

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

B3. described peak power spectral curve is carried out the judgement of signal and noise, judgment processing adopted the threshold decision method, the noise average power that needs before judging

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, greater than

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

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

B5. the described signal noise vector after handling 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 take place in decision signal, estimates optimum Doppler imaging parameters according to the statistical result and the distribution of signal vector.

8. a kind of Doppler imaging parameters automatic optimization method as claimed in claim 7 is characterized in that: the Doppler imaging parameters that is used for the follow-up signal processing that described step B5 estimates comprises optimum down-sampled rate DSR _Prf, its computational methods are:

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

Wherein N is described signal noise vector length, SumSNV be described signal noise vector add up and, k is a predefined correction factor.

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

Judge that blood flow direction is minus optimum baseline

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

Judge that blood flow direction is positive optimum baseline

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

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

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