CN106802418A - A kind of method for designing of the high-effect sparse dictionary in synthetic aperture compressed sensing ultrasonic imaging - Google Patents
A kind of method for designing of the high-effect sparse dictionary in synthetic aperture compressed sensing ultrasonic imaging Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The invention discloses a kind of method for designing of the high-effect sparse dictionary in synthetic aperture compressed sensing ultrasonic imaging, belong to ultrasonic imaging technique field.The method comprises the following steps：The continuous echo-signal that supersonic array is received is amplified treatment and A/D conversions, the echo-signal x required for obtaining ultrasonic imaging；Delta matrixes are chosen as the calculation matrix of synthetic aperture compressed sensing ultrasonic imaging, non-homogeneous compression sampling is carried out to echo-signal x, obtain measurement signal y；By the use of pulse is launched high-effect sparse dictionary Ψ is constructed as basic function；The Mathematical Modeling of synthetic aperture compressed sensing ultrasonic imaging is built according to delta matrixes, measurement signal y and high-effect sparse dictionary Ψ, obtains rebuilding original echoed signals by the model and restructing algorithmUsing rebuilding original echoed signalsCarry out Beam synthesis and be ultimately imaged；The present invention can allow echo-signal to realize identical recovery effects with compression ratio higher, further reduce the data volume of storage needed for synthetic aperture imaging, reduce the complexity of ultrasonic image-forming system.
Description
Technical field
The invention belongs to ultrasonic imaging technique field, and in particular to the height in a kind of synthetic aperture compressed sensing ultrasonic imaging
The method for designing of efficiency sparse dictionary.
Background technology
Synthetic aperture imaging is one kind side for improving image resolution ratio, improving image quality in current ultrasonic imaging
Method.This technology proposed that its basic thought was that single array element launches pulse signal successively, entirely by Passman in 1996 earliest
Portion's array element receives the scattered signal from detection zone simultaneously, then all array element data process and obtains final medical science
Image, it is therefore desirable to which the echo data amount of storage is very huge, increased hard-wired complexity.Compressed sensing is in recent years
A kind of solution proposed for high-speed data acquisition and mass data storage, the theory think when signal in itself or
It is sparse on certain transform domain, then just can rebuilds original with high precision from a small amount of sampled data by restructing algorithm
Signal, reduces the data volume for needing storage, reduces hardware implementation complexity.
In first having to for signal to be restored to transform to some sparse domain due to compressed sensing, common restructing algorithm is first
Reconstruct the rarefaction representation coefficient in sparse domain and then recover primary signal, thus sparse coefficient of the signal in sparse domain is straight
Connect the effect for determining reconstruct.Under identical reconstruction condition, sparse coefficient is more sparse, i.e., nonzero element is fewer, and reconstruction accuracy is got over
Height, the efficiency of the sparse dictionary is better.Conventional sparse transformation matrix has at present：DFT (Discrete
Fourier Transform, DFT) matrix, Discrete Orthogonal cosine transform (Discrete cosine transform, DCT) square
Battle array, wavelet transformation (Discrete Wavelet Transform, DWT) matrix, discrete Walsh-Ha Er Hadamard transforms
(Discrete Walsh-Hadamard Transform, DWHT) matrix etc..It is special and common sparse matrix lacks specific aim
Be not when be applied to ultrasound echo signal it is this with repeat superimposed characteristics signal when, signal itself is not made full use of
Feature, therefore its rarefaction representation is limited in one's ability, reconstruction accuracy is low, and the effect of reconstructed image is difficult to ensure that under little compressible.Therefore
Design the focus that dynamical sparse dictionary is synthetic aperture compressed sensing ultrasonic imaging application study.
The content of the invention
In view of this, it is an object of the invention to provide a kind of synthetic aperture compressed sensing ultrasonic imaging in it is high-effect dilute
Dredge the method for designing of dictionary.
To reach above-mentioned purpose, the present invention provides following technical scheme：
A kind of method for designing of the high-effect sparse dictionary in synthetic aperture compressed sensing ultrasonic imaging, the method include with
Lower step：
1) continuous echo-signal x (t) that acoustic array is received is amplified treatment and A/D conversions, needed for acquisition ultrasonic imaging
The echo-signal x for wanting；
2) delta matrixes are chosen as the calculation matrix of synthetic aperture compressed sensing ultrasonic imaging, echo-signal x is carried out
A certain proportion of non-homogeneous compression sampling, obtains measurement signal y；
3) high-effect sparse dictionary Ψ is constructed as basic function by the use of launching pulse s (t)；
4) synthetic aperture compressed sensing ultrasound is built according to delta matrixes, measurement signal y and high-effect sparse dictionary Ψ
The Mathematical Modeling of imaging；
5) obtain rebuilding original echo letter by the Mathematical Modeling and restructing algorithm of synthetic aperture compressed sensing ultrasonic imaging
Number
6) utilize and rebuild original echoed signalsCarry out Beam synthesis and be ultimately imaged；
Further, in step 2) in, specifically include：
21) length N and selected compression sampling rate p, the length M=of computation and measurement signal y according to echo-signal x
p·N；
22) the delta matrixes Φ of M × N-dimensional is randomly selected as the calculation matrix of synthetic aperture compressed sensing ultrasonic imaging,
Element " 1 " correspondence sampled signal is stored in wherein delta matrixes Φ, and the corresponding sampled signal of element " 0 " is not stored
Give up.
23) sampling is compressed to echo-signal x with delta matrixes Φ, obtains measurement signal y=Φ x.
Further, in step 3) in, specifically include：
31) it is superimposed characteristics of transmitting pulse s (t) after different delayed time decay using continuous echo-signal x (t),
The mathematic(al) representation of echo-signal x can be expressed as：
Wherein T is the exomonental cycle, and n is the pulse signal number of reflection receivable, and t is to send first from supersonic array
The time that individual pulse starts, tmAnd αmThe time delay of respectively m-th reflection echo and amplitude.If the sample frequency of system is
fs, then sampling period TS=1/fs, continuous echo-signal x (t) can be expressed as again：
Wherein nm=tm/Ts。
32) sparse basic function and sparse dictionary are constructed using launching pulse signal s (t)：
ψi(t)=s (t-iTs)
Ψ={ ψi(t)|ψi(t)=s (t-iTs) i=1,2 ..., N
Using frequency fsDiscrete sampling is carried out to sparse basic function and obtains vector：
ψi=[0 ..., 0, s (Ts),s(2Ts),s(3Ts),…,s(kTs),0,…,0]
=[0 ... 0, ψ, 0 ..., 0]
Wherein k=T/TS, ψ=[s (Ts),s(2Ts),s(3Ts),…,s(kTs)]。
By ψiSubstitute into Ψ and obtain high-effect sparse dictionary Ψ ∈ CN×N：
33) sparse dictionary Ψ is chosen as sparse matrix, and sparse transformations of the echo-signal x on Ψ is：
Wherein α is sparse coefficients of the echo-signal x on Ψ.
Further, in step 4) in, built according to delta matrixes Φ, measurement signal y and high-effect sparse dictionary Ψ and closed
Into the Mathematical Modeling of aperture compressed sensing ultrasonic imaging：
Y=Φ x=Φ Ψ α
Further, in step 5) in, specifically include：
51) by solving optimization problem arg min | | α | |1S.t. Φ Ψ α=Φ x=y, obtain the reality of echo-signal x
Border rarefaction representation
52) by high-effect sparse dictionary Ψ and actual rarefaction representationRebuild original echoed signalsWherein
Further, in step 6) in, wave beam composition algorithm is superimposed to rebuilding original echoed signals by traditional time delayEnter
Row weighted sum, is calculated Beam synthesis signal：
Wherein, sDASThe Beam synthesis signal that expression is calculated,Represent the reconstruction original echo letter in i-th array element
Number, N1To represent the sum of supersonic array.
By adopting the above-described technical solution, the present invention has the advantage that：
The invention discloses a kind of method for designing of the high-effect sparse dictionary in synthetic aperture compressed sensing ultrasonic imaging；
The method according to the decay superimposed characteristics of ultrasound echo signal, using launching a kind of dynamical sparse dictionary of Pulse Design.
The dictionary can make the degree of rarefication of echo-signal sparse coefficient equal to the reflection echo number that array element is received in theory, believe echo
Number possess good openness.The reconstruction accuracy of this patent sparse dictionary is higher under identical reconstruction condition, reconstructed error is smaller, enters
One step reduces the data volume of storage needed for synthetic aperture ultrasonic imaging, reduces the complexity of system.
Brief description of the drawings
In order that the purpose of the present invention, technical scheme and beneficial effect are clearer, the present invention provides drawings described below and carries out
Explanation：
Fig. 1 is the techniqueflow chart of the method for the invention；
Fig. 2 is original single-row echo-signal；
Fig. 3 is rarefaction representation of the single-row echo-signal under 4 kinds of sparse dictionaries；
Reconstruction signal of the single-row echo-signal under 4 kinds of sparse dictionaries when Fig. 4 is 50% compression ratio；
Reconstructed image when Fig. 5 is 50% compression ratio under different sparse transformations；
Fig. 6 is imaged on cross-section analysis at 60mm for different sparse transformations；
Reconstructed image when Fig. 7 is original image and 50% compression ratio under 4 kinds of different sparse transformations；
Fig. 8 is the reconstructed image of the different compression ratios of lower 4 kinds of sparse dictionary；
Fig. 9 is the mean absolute error analysis of the different lower 4 kinds of sparse transformations of compression ratio；
Figure 10 be original phantom image with 30% compression ratio when 4 kinds of different sparse transformations under reconstructed image.
Specific embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.
Fig. 1 is techniqueflow chart of the invention, as illustrated, the present invention provide a kind of synthetic aperture compressed sensing ultrasound into
The method for designing of the high-effect sparse dictionary as in, comprises the following steps：
1) continuous echo-signal x (t) that acoustic array is received is amplified treatment and A/D conversions, needed for acquisition ultrasonic imaging
The echo-signal x for wanting；
2) delta matrixes are chosen as the calculation matrix of synthetic aperture compressed sensing ultrasonic imaging, echo-signal x is carried out
A certain proportion of non-homogeneous compression sampling, obtains measurement signal y.Specifically include step：
21) length N and selected compression sampling rate p, the length M=of computation and measurement signal y according to echo-signal x
p·N；
22) the delta matrixes Φ of M × N-dimensional is randomly selected as the calculation matrix of synthetic aperture compressed sensing ultrasonic imaging,
Element " 1 " correspondence sampled signal is stored in wherein delta matrixes Φ, and the corresponding sampled signal of element " 0 " is not stored
Give up.
23) sampling is compressed to echo-signal x with delta matrixes Φ, obtains measurement signal y=Φ x.
3) high-effect sparse dictionary Ψ is constructed as basic function by the use of launching pulse s (t).Specifically include following steps：
31) it is superimposed characteristics of transmitting pulse s (t) after different delayed time decay using continuous echo-signal x (t),
The mathematic(al) representation of echo-signal x can be expressed as：
Wherein T is the exomonental cycle, and n is the pulse signal number of reflection receivable, and t is to send first from supersonic array
The time that individual pulse starts, tmAnd αmThe time delay of respectively m-th reflection echo and amplitude.If the sample frequency of system is
fs, then sampling period TS=1/fs, continuous echo-signal x (t) can be expressed as again：
Wherein nm=tm/Ts。
32) sparse basic function and sparse dictionary are constructed using launching pulse signal s (t)：
ψi(t)=s (t-iTs)
Ψ={ ψi(t)|ψi(t)=s (t-iTs) i=1,2 ..., N
Using frequency fsDiscrete sampling is carried out to sparse basic function and obtains vector：
ψi=[0 ..., 0, s (Ts),s(2Ts),s(3Ts),…,s(kTs),0,…,0]
=[0 ... 0, ψ, 0 ..., 0]
Wherein k=T/TS, ψ=[s (Ts),s(2Ts),s(3Ts),…,s(kTs)]。
By ψiSubstitute into Ψ and obtain high-effect sparse dictionary Ψ ∈ CN×N：
33) sparse dictionary Ψ is chosen as sparse matrix, and sparse transformations of the echo-signal x on Ψ is：
Wherein α is sparse coefficients of the echo-signal x on Ψ.
4) synthetic aperture compressed sensing ultrasound is built according to delta matrixes, measurement signal y and high-effect sparse dictionary Ψ
The Mathematical Modeling of imaging.Specifically mathematic(al) representation is：
Y=Φ x=Φ Ψ α
5) obtain rebuilding original echo letter by the Mathematical Modeling and restructing algorithm of synthetic aperture compressed sensing ultrasonic imaging
NumberSpecifically include following steps：
51) by solving optimization problem arg min | | α | |1S.t. Φ Ψ α=Φ x=y, obtain the reality of echo-signal x
Border rarefaction representation
52) by high-effect sparse dictionary Ψ and actual rarefaction representationRebuild original echoed signalsWherein
6) utilize and rebuild original echoed signalsCarry out Beam synthesis and be ultimately imaged.Wave beam is superimposed with traditional time delay to close
Original echoed signals are built into algorithm counterweightSummation is weighted, beam signal is calculated：
Wherein, sDASThe beam signal that expression is calculated,Represent the reconstruction original echoed signals in i-th array element, N1
To represent the sum of supersonic array.
In order to verify effectiveness of the invention, in the present embodiment, using Field II to point conventional in medical imaging
Scattering Targets and spot Scattering Targets are imaged and are carried out real data collection to body film.Emulation is provided with 10 scattering point mesh altogether
Mark, is evenly distributed in the region of 30~120mm, and spacing is 10mm.Emulation sets 10 scattering spots simultaneously, and 5 scattering points dissipate
Penetrate spot to be evenly distributed between 30~90mm in two separate ranks, spacing is 10mm.Entered with 4 kinds of sparse dictionaries under different compression ratios respectively
Line reconstruction, and compare resolution ratio, contrast and the mean absolute error of various sparse dictionaries.Body film data acquisition center frequency is
f0=3.5MHz, sample frequency is fs=25MHz.Element number of array N=16, array element spacing is 0.78mm, and institute is into dynamic range of images
It is 50dB, is reconstructed using 4 kinds of sparse dictionaries and comparative effectiveness.
The single-row echo-signal received in array element when Fig. 2 is synthetic aperture imaging, it is also seen that echo is believed from Fig. 2
Number it is the superposition of the reflection attenuation signal for launching pulse signal at different target point.With the increase of depth, reflected signal
Attenuation amplitude is increasing, but signal shape is held essentially constant.Fig. 3 is single-row echo-signal in 4 kinds of different sparse transformations
Under rarefaction representation, the rarefaction representation ability of sparse dictionary that this patent that be can visually see from Fig. 3 is proposed is significantly stronger than
Other 3 kinds of sparse transformations, the degree of rarefication of its sparse coefficient is approximately equal to the impact point number 10 that emulation is set.Fig. 4 is use
Recovery signal of the single-row echo-signal under 4 kinds of different sparse transformations during YALL1_group restructing algorithms, reconstruct employs 50%
Original data volume.4 width reconstructed images are contrasted, the quality reconstruction of the sparse dictionary that this patent is proposed is optimal, and does not produce volume
Outer clutter, although dct transform can accurately recover the signal at impact point, it is miscellaneous at non-targeted point to introduce some
Ripple, therefore quality reconstruction, slightly poorer to sparse dictionary, DWT becomes the recovery signal changed at far field objects point and occurs in that some distortions, its
Quality reconstruction is slightly better than DFT.Quality reconstruction in Fig. 4 is consistent with the rarefaction representation effect in Fig. 3, and the degree of rarefication of sparse coefficient is got over
Quality reconstruction under height, the same terms is better.
Single-row echo-signal can not embody influence of 4 kinds of sparse transformations to integrative reconstruction image, when Fig. 5 is identical compression ratio
4 kinds of reconstructed images of different sparse transformations.Compare for the ease of calculating, all of raw radar data is normalized in text
Treatment.As can be seen from Figure 5 the recovery image of sparse dictionary and DCT is essentially the same with original image, and picture quality does not occur
Distortion, and the artifact not having in some original images is occurred in that in the recovery image of DFT and DWT, picture quality has declined.Figure
6 is the cross section comparing result that 4 kinds of sparse transformations are imaged at 60mm, as can be seen from Figure 6 the cross section curve of sparse dictionary
Be completely superposed with initial data, cross section curve and the initial data of DCT are essentially coincided, the quality reconstruction of DWT slightly poorer to DCT, and
There are some distortion, resolution ratio and the original image of the reconstructed image from the point of view of contrast under sparse dictionary at two ends in the curve of DFT
Resolution ratio is the most close, and does not have any influence substantially on secondary lobe, and its recovery effects is optimal.
In order to more accurately weigh 4 kinds of recovery effects of sparse transformation, this patent uses mean absolute error (the
Mean absolute error, MAE) as evaluation criterion.Table 1 is reconstruct data under 4 kinds of different sparse transformations and original time
The mean absolute error of wave number evidence, because this patent is that each column echo-signal is individually reconstructed in reconstruct, and synthesizes hair
The columns of echo-signal has thousands of row in perforation footpath, therefore its mean absolute error can exclude the result of contingency.From table 1
Data can more simple and clearly find out 4 kinds of effects of sparse transformation, between the reconstruct data and initial data of sparse dictionary
Mean absolute error be far below other 3 kinds of sparse transformations, DCT secondly, DWT is slightly better than DFT, illustrates the reconstruct under sparse dictionary
Precision highest, quality reconstruction is optimal.
The mean absolute error of reconstruct data and raw radar data under the different sparse transformations of 14 kinds of table
| Sparse transformation | DFT | DWT | DCT | Sparse dictionary |
| Mean absolute error | 0.0153 | 0.0049 | 0.0015 | 2.71e-06 |
To the reconstructed image of complex target, our 4 kinds of different sparse transformations can from Fig. 7 when Fig. 7 is 50% compression ratio
DFT and DWT is substantially better than with the recovery effects for finding out the sparse dictionary that this patent is proposed, contrast sparse dictionary and DCT recover figure
The scattering spot of picture finds that the Quality of recovery of spot is scattered under sparse dictionary more preferably, and this illustrates the extensive of the sparse dictionary that this patent is proposed
Multiple effect is slightly better than DCT, while proving that the dictionary can also be perfectly suitable for the rarefaction representation of complex target echo-signal.
It is same to use mean absolute error as judgment criteria in order to more intuitively distinguish 4 kinds of effects of sparse transformation,
The mean absolute error that table 2 is reconstructed for 4 kinds of different sparse transformations in Fig. 7 under 50% compression ratio, as can be seen from the table
The reconstructed error of the sparse dictionary that this patent is proposed is minimum under identical compression ratio, and far below other three kinds of sparse transformations.It is right
The region of black box and white circular collimation mark note carries out Analysis of Contrast in Fig. 7, as a result as shown in table 3.Reconstructed under sparse dictionary
The center mean power and background mean power in image tagged region are all closest to original image, thus it recovers the contrast of image
Degree highest, the reduction effect to original image is optimal, and image quality is more excellent with respect to DFT, DWT and DCT.
The mean absolute error that 4 kinds of different sparse transformations are reconstructed under 50% compression ratio when the complex target of table 2 is emulated
| Sparse transformation | DFT | DWT | DCT | Sparse dictionary |
| Mean absolute error | 0.0754 | 0.0347 | 0.0039 | 9.15e-04 |
4 kinds of Analysis of Contrast of different sparse transformation reconstructed images under 50% compression ratio when the complex target of table 3 is emulated
| Sparse transformation | Original image | DFT | DWT | DCT | Sparse dictionary |
| Center mean power/dB | 51.72 | 35.18 | 34.04 | 50.61 | 51.29 |
| Background mean power/dB | 26.7 | 25.86 | 26.18 | 26.53 | 26.69 |
| Contrast/dB | 25.02 | 9.32 | 7.86 | 24.08 | 24.60 |
Application purpose of the compressed sensing in synthetic aperture ultrasonic imaging is just to try to reduce the amount of storage of data, reduces system
The complexity of system.In order to verify reconstruction situation of this patent sparse dictionary under little compressible, 30%, 20%, 10% and is chosen
5% compression ratio carries out signal reconstruction.Fig. 8 is this 4 kinds of reconstructed images of compression ratio under sparse dictionary.As can be seen from the figure with
The reduction of compression ratio, although the Quality of recovery of image declines therewith, but reconstruct of this patent sparse dictionary under little compressible
Effect can still match in excellence or beauty effect of other sparse transformations under high compression rate.Table 4 is that sparse dictionary is reconstructed in 4 kinds of different compression ratios
Mean absolute error, according to data in table 4 it is also seen that under sparse dictionary the mean absolute error of 30% data recovery with
The mean absolute error of 50% data recovery is close under dct transform, and mean absolute error and the DFT and DWT of 5% data recovery become
The mean absolute error for changing 50% data recovery is close.Compression ratio is exhausted with average under Fig. 9 gives different sparse representation methods
To the relation curve between error, the tendency of each bar curve also indicates that quality reconstruction of the sparse dictionary under each compression ratio is all remote
Better than other sparse transformations.
The mean absolute error reconstructed during lower 4 kinds different compression ratios of the sparse dictionary of table 4
| Compression ratio | 30% | 20% | 10% | 5% |
| Mean absolute error | 0.0027 | 0.0093 | 0.0284 | 0.0422 |
Experiment randomly selects the 30% of real data, is reconstructed using 4 kinds of different sparse transformations, when the dynamic model of imaging
Enclose when being set to 60dB, the DAS imaging results for reconstructing data are as shown in Figure 10.Each width image in comparison diagram 10 can be seen that 30%
The whole structure of reconstructed image is optimal under sparse dictionary during compression ratio, and its image resolution ratio is substantially better than DFT and DWT, and DCT
Substantially quite, than more completely remaining the image resolution ratio that initial data DAS is imaged.From the point of view of contrast angle, although dilute
The contrast of dictionary reconstructed image is dredged less than reconstruct contrast highest DWT, but is influenceed by reconstruct clutter, DWT reconstruct images
The noise of picture is big, and image quality is low.Therefore from the point of view of comprehensive each side under sparse dictionary reconstructed image optimal quality, in low pressure contracting
Reconstruction quality is still ensured that under rate.It is same to use mean absolute error conduct in order to more intuitively characterize the efficiency of sparse dictionary
Validation criteria, 4 kinds of reconstruct data of different sparse transformations are average exhausted with initial body mould experimental data when table 5 is 30% compression ratio
To error.The mean absolute error that can be seen that sparse dictionary according to the data of table 5 is minimum, only about the half of DCT, the 1/ of DWT
The 1/16 of 7, DFT, this also demonstrates the superiority that this patent carries sparse dictionary.Therefore, this patent proposes sparse dictionary reconstruct
Error is smaller, and reconstructed image resolution ratio and contrast closer to original image.
The mean absolute error that the different sparse transformations of 54 kinds of table are reconstructed under 30% compression ratio
| Sparse transformation | DFT | DWT | DCT | Sparse dictionary |
| Mean absolute error | 0.0516 | 0.0229 | 0.0058 | 0.0032 |
Finally illustrate, preferred embodiment above is merely illustrative of the technical solution of the present invention and unrestricted, although logical
Cross above preferred embodiment to be described in detail the present invention, it is to be understood by those skilled in the art that can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (6)
1. the method for designing of the high-effect sparse dictionary in a kind of synthetic aperture compressed sensing ultrasonic imaging, it is characterised in that should
Method comprises the following steps：
1) continuous echo-signal x (t) that supersonic array is received is amplified treatment and A/D conversions, required for acquisition ultrasonic imaging
Echo-signal x；
2) calculation matrix of the delta matrixes as synthetic aperture compressed sensing ultrasonic imaging is chosen, echo-signal x is carried out non-equal
Even compression sampling, obtains measurement signal y；
3) high-effect sparse dictionary Ψ is constructed as basic function by the use of launching pulse s (t)；
4) synthetic aperture compressed sensing ultrasonic imaging is built according to delta matrixes, measurement signal y and high-effect sparse dictionary Ψ
Mathematical Modeling；
5) obtain rebuilding original echoed signals by the Mathematical Modeling and restructing algorithm of synthetic aperture compressed sensing ultrasonic imaging
6) utilize and rebuild original echoed signalsCarry out Beam synthesis and be ultimately imaged.
2. the design of the high-effect sparse dictionary in a kind of synthetic aperture compressed sensing ultrasonic imaging according to claim 1
Method, it is characterised in that：In step 2) in, specifically include：
21) length N and selected compression sampling rate p, the length M=pN of computation and measurement signal y according to echo-signal x；
22) the delta matrixes Φ of M × N-dimensional is randomly selected as the calculation matrix of synthetic aperture compressed sensing ultrasonic imaging, wherein
Element " 1 " correspondence sampled signal is stored in delta matrixes Φ, and the corresponding sampled signal of element " 0 " is not given up by storage；
23) sampling is compressed to echo-signal x with delta matrixes Φ, obtains measurement signal y=Φ x.
3. the design of the high-effect sparse dictionary in a kind of synthetic aperture compressed sensing ultrasonic imaging according to claim 1
Method, it is characterised in that：In step 3) in, specifically include：
31) continuous echo-signal x (t) is the superposition for launching pulse s (t) after different delayed time decay, the number of echo-signal x
Expression formula is learned to be expressed as：
Wherein T is the exomonental cycle, and n is the pulse signal number of reflection receivable, and t is to send first arteries and veins from supersonic array
Wash the time of beginning, t openmAnd αmThe time delay of respectively m-th reflection echo and amplitude；If the sample frequency of system is fs,
Then sampling period TS=1/fs, continuous echo-signal x (t) can be expressed as again：
Wherein nm=tm/Ts；
32) sparse basic function and sparse dictionary are constructed using launching pulse signal s (t)：
ψi(t)=s (t-iTs)
Ψ={ ψi(t)|ψi(t)=s (t-iTs) i=1,2 ..., N
Using frequency fsDiscrete sampling is carried out to sparse basic function and obtains vector：
ψi=[0 ..., 0, s (Ts),s(2Ts),s(3Ts),…,s(kTs),0,…,0]
=[0 ... 0, ψ, 0 ..., 0]
Wherein k=T/TS, ψ=[s (Ts),s(2Ts),s(3Ts),…,s(kTs)]；
By ψiSubstitute into Ψ and obtain high-effect sparse dictionary Ψ ∈ CN×N：
33) sparse dictionary Ψ is chosen as sparse matrix, and sparse transformations of the echo-signal x on Ψ is：
Wherein α is sparse coefficients of the echo-signal x on Ψ.
4. the design of the high-effect sparse dictionary in a kind of synthetic aperture compressed sensing ultrasonic imaging according to claim 1
Method, it is characterised in that：In step 4) in, specifically include：
41) synthetic aperture compressed sensing ultrasound is built according to delta matrixes Φ, measurement signal y and high-effect sparse dictionary Ψ
The Mathematical Modeling of imaging：
Y=Φ x=Φ Ψ α.
5. the design of the high-effect sparse dictionary in a kind of synthetic aperture compressed sensing ultrasonic imaging according to claim 1
Method, it is characterised in that：In step 5) in, specifically include：
51) by solving optimization problem arg min | | α | |1S.t. Φ Ψ α=Φ x=y, the reality for obtaining echo-signal x is dilute
Dredge and represent
52) by high-effect sparse dictionary Ψ and actual rarefaction representationRebuild original echoed signalsWherein
6. the design of the high-effect sparse dictionary in a kind of synthetic aperture compressed sensing ultrasonic imaging according to claim 1
Method, it is characterised in that：In step 6) in, specifically include：Wave beam composition algorithm is superimposed to rebuilding original time with traditional time delay
Ripple signalSummation is weighted, beam signal is calculated：
Wherein, sDASThe beam signal that expression is calculated,Represent the reconstruction original echoed signals in i-th array element, N1It is table
Show the sum of supersonic array.
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