Quantifying Cardiac Deformation by strain (-rate) imaging

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Hans Torp NTNU, Norway. Quantifying Cardiac Deformation by strain (-rate) imaging. Hans Torp Department of Circulation and Medical Imaging Norwegian University of Science and Technology Norway. Hans Torp NTNU, Norway. Quantifying Cardiac Deformation by strain (-rate) imaging.

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Hans Torp

NTNU, Norway

Quantifying Cardiac Deformation by strain (-rate) imaging

Hans Torp

Department of Circulation and Medical Imaging

Norwegian University of Science and Technology Norway

Hans Torp

NTNU, Norway

Quantifying Cardiac Deformation by strain (-rate) imaging
• Describe deformation by strain and strain rate
• Ultrasound methods for strain rate
• speckle tracking versus Doppler methods
• Clutter noise and thermal noise
• angle dependency
• Frame rate issues
• Visualization of strain and strain-rate

V2 – V1

L

= Rate of deformation

= Strain Rate

V2

L

V1

(Myocardial) velocity gradient is an instantaneous property

”Growth function” Strain = exp{ IVG } - 1

RF

Envelope

What is the best velocity estimator?

velocity ~ angle(R)

R=x1*x2

s2

x2

s1

x1

Autocorrelation method is optimal

(Maximum likelihood estimator)

RF signals

IQ signals

What is the best velocity estimator?

v=c/4piT angle(R)

R=x1*x2

Hans Torp

NTNU, Norway

Estimation error is minimumwhen correlation is maximum

Velocity estimate

Correlation magnitude

Linear

regression

Weighted

Linear

regression

(Maximun

likelihood)

Estimate of velocity gradient (strain rate)

0.03

0.025

0.02

Velocity [m/sec]

0.015

0.01

0

0.005

0.01

0.015

depth range [mm]

Simulation experimentStrain rate estimators

Linear

regression

Strain

rate

Weighted

Linear

regression

(Maximun

likelihood)

Simulation no

Clutter noise
• bias towards zero for velocity measurements
• increased variance for strain rate
• Clutter filter helps when tissue velocity is high
• limited effect in apical region
• Second harmonic (octave) imaging reduces clutter
• independent of tissue velocity

2. harmonic

Hans Torp

NTNU, Norway

Fundamental and second harmonic signal separated by filter

50

100

150

200

250

300

350

400

450

20

40

60

80

100

Fundamental

Signal from septum

Noise from LV cavity

Second Harmonic TDI

Fundamental, f=1.67MHz

• Fundamental and second harmonic calculated from the same data set
• No significant noise difference
• Second harmonic TDI gives more aliasing.

Second harmonic, f=3.33MHz

Fundamental, f=1.67MHz

Second harmonic, f=3.33MHz

Second Harmonic SRI
• Fundamental and second harmonic calculated from the same data set
• Significant noise reduction when using the second harmonic frequency band
• Aliasing is not a problem due to small velocity differences

### Frame rate issues in tissue velocity and strain rate imaging

Packet acquisition

tissue interleaving

100 - 150 frames/sec

Continuous acquisition tissue interleaving

250 - 350 frames/sec

- TVI aliasing

+ Offline spectral Doppler

(Work in progress)

Packet acquisition

30 - 80 frames/sec

Image sector: 70 deg.

Parallell beams : 2

v1

v2

Hans Tarp

NTNU, Norway

Myocardial velocity and strain ratewith 300 frames/sec

Velocity

Strain rate

Time

Summary 1
• Strain rate from Tissue Doppler is possible for motion along the ultrasound beam with high temporal resolution
• Weighted linear regression gives minimum estimation error
• Second harmonic Tissue Doppler reduce clutter noise artefacts in strain rate imaging
Summary 2
• Integrated strain is improved by tracking material points
• 2D speckle-tracking gives angle-independent strain, with reduced temporal resolution
• A combination of high frame rate tissue Doppler and lower frame rate speckle tracking is probably the best solution for strain imaging
• 3D reconstruction of strain (-rate) covering the left ventricle can be obtained from 3 standard apical views