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Quantifying Cardiac Deformation by strain (-rate) imaging PowerPoint Presentation
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Quantifying Cardiac Deformation by strain (-rate) imaging

Quantifying Cardiac Deformation by strain (-rate) imaging

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Quantifying Cardiac Deformation by strain (-rate) imaging

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  1. 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

  2. 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

  3. Velocity gradient – strain rate V2 – V1 L Velocity gradient: = Rate of deformation = Strain Rate V2 L V1 (Myocardial) velocity gradient is an instantaneous property

  4. Integrated velocity gradientversus strain ”Growth function” Strain = exp{ IVG } - 1

  5. Integrated velocity gradientversus strain

  6. RF Envelope

  7. 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

  8. What is the best velocity estimator? v=c/4piT angle(R) R=x1*x2

  9. Hans Torp NTNU, Norway Estimation error is minimumwhen correlation is maximum Velocity estimate Correlation magnitude

  10. 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]

  11. Simulation experimentStrain rate estimators Linear regression Strain rate Weighted Linear regression (Maximun likelihood) Simulation no

  12. Clutter noise

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. Packet acquisition - continuous acquisition

  19. v1 v2 Hans Tarp NTNU, Norway Myocardial velocity and strain ratewith 300 frames/sec Velocity Strain rate Time

  20. Lateral movement

  21. Angle corrected strain

  22. 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

  23. 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