Presenter: 詹承洲 Student ID : D96943004 Date : 2009/12/10

1. Time Frequency Analysis and Wavelet Transforms Spectral Doppler in the Medical Ultrasound System Presenter: 詹承洲 Student ID : D96943004 Date : 2009/12/10

2. Outline • Introduction to the Ultrasound System • Main Functions in the Medical Ultrasound Systems • Introduction to the Doppler Ultrasound • Conclusion • References

3. Traditional Applications of the Ultrasound System • National defense • Fisheries • Applications • Sonar systems • Radar systems Sea

4. Applications on the Medical Ultrasound Systems • Modern medical imaging systems • X-rays • Computer tomography (CT) • Magnetic resonance imaging (MRI) • Ultrasound imaging (US) • Medical ultrasound has well-established imaging modality • Real-time monitoring • Non-invasiveness • Portability [1][2][3][4]

5. Conventional Medical Ultrasound System • Overall architecture of an ultrasonic imaging system Front-end Back-end All digital Color Doppler Processing Transducer Beamformer Display Spectral Doppler Processing

6. Outline • Introduction to the Ultrasound System • Main Functions in the Medical Ultrasound Systems • Beamformer • Doppler Processing • Introduction to the Doppler Ultrasound • Conclusion • References

7. Beamformer • Overall architecture of an ultrasonic imaging system Front-end Back-end All digital Color Doppler Processing Transducer Beamformer Display Spectral Doppler Processing

8. The Concept of the Beamformer • Two categories of the beamformer designs • Mechanical movement • Static transducers with digital processing Mechanical movement Ultrasound wave emitting and receiving Digital processing

9. Beamformer Design • Traditional design • Use all transducers at the same time • Delay and sum

10. Beaformer Imaging in the Ultrasound system • Real-time imaging • 3D beamformer imaging

11. Outline • Introduction to the Ultrasound System • Main Functions in the Medical Ultrasound Systems • Beamformer • Doppler Processing • Introduction to the Doppler Ultrasound • Conclusion • References

12. Spectral Doppler Processing • Overall architecture of an ultrasonic imaging system Front-end Back-end All digital Color Doppler Processing Transducer Beamformer Display Spectral Doppler Processing

13. Doppler Ultrasound • Velocity profile is significant in clinical applications. • Doppler ultrasound provides a non-invasive way for velocity measurement. Source Receiver [3]

14. -f0-fd -f0 f0 f0+fd -2f0-fd fd fd Continuous Wave (CW) Doppler Received Signal time Moving-window FFT • No range resolution [1]

15. Pulsed Wave (PW) Doppler High range resolution transmitted time received PRI (Pulse Repetition Interval)

16. State of the Art – Doppler Processing • Doppler processing for flow estimation • Noise may be 20~50 dB greater than the flow signals • Noise elimination with various filters[8] [9] • Velocity estimation [10] [11]

17. Outline • Introduction to the Ultrasound System • Main Functions in the Medical Ultrasound Systems • Introduction to the Doppler Ultrasound • Clutter Filters in the Spectral Doppler • Velocity Estimation in the Spectral Doppler with HHT • Process and Results of HHT • Conclusion • References

18. Clutter Filter • The clutter filter is usually a high-pass filter • Non-moving signals • Low-velocity signals • The noise is generally much stronger than the signal. • The clutter characteristics vary in both frequency and time

19. Clutter Filters in the Spectral Doppler (1/5) • The real-time imaging of the heart • Tissue • Valves • Blood flow

20. Clutter Filters in the Spectral Doppler (2/5) • Original un-filtered signals

21. Clutter Filters in the Spectral Doppler (3/5) • Fixed coefficients of the high-pass filter • Higher cut-off frequency

22. Clutter Filters in the Spectral Doppler (4/5) • Fixed coefficients of the high-pass filter • Lower cut-off frequency

23. Clutter Filters in the Spectral Doppler (5/5) • Characteristics extraction with Hamming window then deciding coefficients

24. Spectral Doppler for Observing the Tissue (1/2) • Observing the tissue with fixed-coefficient low-pass filter • Higher cut-off frequency

25. Spectral Doppler for Observing the Tissue (2/2) • Observing the tissue with fixed-coefficient low-pass filter • Lower cut-off frequency

26. Results with Different Filter Parameters • Performance comparison with Hamming window Processing with the feature extraction by the Hamming window Fixed-coefficient filter

27. Outline • Introduction to the Ultrasound System • Main Functions in the Medical Ultrasound Systems • Introduction to the Doppler Ultrasound • Clutter Filters in the Spectral Doppler • Velocity Estimation in the Spectral Doppler with HHT • Process and Results of HHT • Conclusion • References

28. Flow Chart of the Hilbert Huang Transform [1] [4]

29. Problem to be Solved • Bio-signals • Non-linear • Non-stationary • Need feature exaction for syndrome identification • HHT • Much better performance in frequency and time resolution • Suitable for non-linear and non-stationary signals • Higher complexity with iterative computation

30. State of the Art – Spectral Doppler • Fast Fourier Transform (FFT) • Wavelet transform • Hilbert-Huang Transform (HHT)

31. Goal – Spectral Doppler Processing • Combination with spectral Doppler processing • Achieve high frequency and time resolution

32. Outline • Introduction to the Ultrasound System • Main Functions in the Medical Ultrasound Systems • Introduction to the Doppler Ultrasound • Clutter Filters in the Spectral Doppler • Velocity Estimation in the Spectral Doppler with HHT • Process and Results of HHT • Conclusion • References

33. Empirical Mode Decomposition (1/4) [1]

34. Empirical Mode Decomposition (2/4) [1]

35. Empirical Mode Decomposition (3/4) • SD<0.1 => IMF [4] [1]

36. Empirical Mode Decomposition (4/4) [4]

37. Performance Comparison with Simulation Results • Comparison between HHT and conventional high-pass filter Less clutter noise Stronger clutter noise

38. Conclusion • Introduction to the medical ultrasound system • The spectral Doppler in the medical ultrasound system • Time-frequency analysis • Windowed clutter filter • Hilbert-Huang transform (HHT)

39. Reference [1] EE-Times [2] http://www.researchinchina.com/Htmls/Report/2009/5723.html [3] M. O'Donnell and L. J. Thomas “Efficient synthetic aperture imaging from a circular aperture with possible application to catheter-based imaging,” IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 39(3):366-380, 1992. [4] Sverre Holm and Hongxia Yao “Improved framerate with synthetic transmit aperture imaging using prefocusedsubapertures,” IEEE Ultrasonics Symposium, Oct. 1997. [5] T.-J. Shan, M. Wax, and T. Kailath, “On spatial smoothing for direction-of-arrival estimation of coherent signals,” IEEE Trans. Acoust. Speech Signal Processing, vol. 33, no. 4, pp. 806–811,Aug. 1985. [6] T.-J. Shan and T. Kailath, “Adaptive Beamforming for Coherent Signals and Interference,” IEEE Trans. Acous.,Speech, Sig. Pro., vol. 33, no. 3, pp. 527–536, June 1985.

40. Reference [7] L. Lovstakken, S. Bjaerum, K. Kristoffersen, R. Haaverstad, and H. Torp,“Real-Time Adaptive Clutter Rejection Filtering in Color Flow Imaging Using Power Method Iterations,” IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.53, pp.1597 - 1608 , Sept. 2006 . [8] Maksimovic, D.; Zane, R.; Erickson, R., "Impact of digital control in power electronics," Power Semiconductor Devices and ICs, 2004. Proceedings. ISPSD '04. The 16th International Symposium on , vol., no., pp. 13-22, 24-27 May 2004 [9] Ylee203 and shanbhag, “Battery-friendly system design, from battery, power electronics to application ICs”.

41. Velocity Constraint in PW Doppler no aliasing vmax aliasing -vmax vmax [1]

42. Aliasing with High PRI

43. Problem in HHT (1/2) • Small wiggle effect [5]

44. Problem in HHT (2/2) • Spline problem [5]