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MEDT8007
Simulering av ultralydsignal fra spredere i bevegelse
Hans Torp
Institutt for sirkulasjon og medisinsk bildediagnostikk
Hans TorpNTNU, Norway
Signal processing Signal processing for CW Dopplerfor CW Doppler
fofrequency
fo+fd0 frequencyfd0
Hans TorpNTNU, Norway
Matlab: cwdoppler.m
Blood velocity calculated from Blood velocity calculated from measured Doppler-shiftmeasured Doppler-shift
fd = 2 fo v cos() / c
v = c/2fo/cos() fd
fd : Dopplershiftfo : Transmitted frequencyv : blood velocity
: beam angle c : speed of sound (1540 m/s )Hans Torp
NTNU, Norway
Continous Wave DopplerContinous Wave Doppler
ø
Single transducerPW
Double transducerCW
ø
transmit
recieve
Velocity profile, v
Artery
Range cell
Observation region in overlap of beams
Signal from all scattererswithin the ultrasound beam
Pulsed Wave DopplerPulsed Wave Doppler
Signal from a limited sample volume
ø
Single transducerPW
Double transducerCW
ø
transmit
recieve
Velocity profile, v
Artery
Range cell
Observation region in overlap of beams
Hans TorpNTNU, Norway
Matlab: pwdoppler.m
a)
b)
10-08/12pt
Hans TorpNTNU, Norway
Signal from a large number of red blood cellsSignal from a large number of red blood cellsadd up to a Gaussian random processadd up to a Gaussian random process
G ()e
10-11/12pt
Hans TorpNTNU, Norway
Power spectrum of the Doppler signal Power spectrum of the Doppler signal represents the distribution of velocitiesrepresents the distribution of velocities
Definition of Complex Gaussian process Definition of Complex Gaussian process
0)()(:
average)(ensemblevalueexpectedmeans
)(*)(zmatrixCovariance
21 vector signal
)(
,
H
1 1H
nzkzthatNote
nzkzz
),...,z(N)),z(z(z
ep
nk
N
zzz
Stationary Complex Gaussian processStationary Complex Gaussian process
;)()(m
miemRGPower spectrum
Autocorrelation function ,......2,1,0)(*)()( mmnznzmR
mieGdmR )(2
1)(
Autocorrelation function = coeficients in Fourier series of G
Power spectrum estimatePower spectrum estimateStatistical propertiesStatistical properties
)()(2
1)(
2
GWdGN
m
miN emwW )()(
Expected value:
m
miNN emzmwZ )()()(
2)(
1)( NN Z
NG Power spectrum estimate:
Power spectrum estimatePower spectrum estimateStatistical propertiesStatistical properties
N
G
GWWdN
GG
N
NN
/1 when 0
0 when )(
)()()(2
1)(),(cov
2
2
*
Covariance:
m
miNN emzmwZ )()()(
2)(
1)( NN Z
NG Power spectrum estimate:
Computer simulation of Computer simulation of Complex Gaussian processComplex Gaussian process
1.Complex Gaussian white noise Zn(0),..,Zn(N-1)2. Shape with requested power spectrum: Z(k)=G(2k/N) Zn(k); k=0,..,N-13. Inverse FFT: z(n) = ifft(Z)
< |Z(w)|^2 >= G(w)|Zn(w)|^2 = G(w); for w= 2k/N
)()(2
1)(
2
GWdGZPower spectrum for z(n):(smoothed version of G(w) )
1
0
)(N
m
mieW Autocorrelation function: )()()( mRmTrimRz
• The power spectrum of the simulated signal is The power spectrum of the simulated signal is smoothed with a window given by the number of smoothed with a window given by the number of samples Nsamples N
• The autocorrelation function of the simulated signal The autocorrelation function of the simulated signal Rz(m)= 0 for m>|N| Rz(m)= 0 for m>|N|
Computer simulation of Computer simulation of Complex Gaussian processComplex Gaussian process
Matlab: CsignalDemo.m
Properties of power spectrum estimateProperties of power spectrum estimate
• Fractional variance = 1 independent of the window form and size
• GN(ω1) and GN(ω2) are uncorrelated when |ω1-ω2| > 1/N
• Increasing window length N gives better frequency resolution, but no decrease in variance
• Smooth window functions give lower side lobe level, but wider main lobe than the rectangular window
• Decrease in variance can be obtained by averaging spectral estimates from different data segments.
Doppler spektrumDoppler spektrum
frequ
ency
time
spec
tral
am
plit
ude
Hans TorpNTNU, Norway
Thermal noise
Signal from moving scattererSignal from moving scattererPulse no1 2 .. … N
2D Fouriertransform
Hans TorpNTNU, Norway
Doppler shift frequency [kHz]
Ult
raso
und
puls
e fr
eque
ncy
[MH
z]
Doppler shift frequency [kHz]
Pow
erSignal from one range
Fas
t ti
me
Slow time
Blood
Clutter
RF versus basebandRF versus baseband
Doppler shift frequency [kHz]
Blood signalClutter
Ultrasound frequency [MHz]
Doppler frequency [kHz]
Remove negative ultrasoundFrequencies by Hilbert transformor complex demodulation
• Skewed clutter filter (signal adaptive Skewed clutter filter (signal adaptive filter) can be implemented with 1D filter) can be implemented with 1D filteringfiltering
• Axial sampling frequency Axial sampling frequency reduced by a factor > 4reduced by a factor > 4
2D Spectrum Subclavian artery2D Spectrum Subclavian artery
Summary spectral DopplerSummary spectral Doppler
• Complex demodulation give direction information of blood Complex demodulation give direction information of blood flowflow
• Smooth window function removes sidelobes from clutter-Smooth window function removes sidelobes from clutter-signalsignal
• PW Doppler suffers from aliasing in many cardiac PW Doppler suffers from aliasing in many cardiac applicationsapplications
• SNR increases by the square of pulse length in PW Doppler SNR increases by the square of pulse length in PW Doppler