1. Spatially Varying Frequency Compounding of Ultrasound Images
Yael Erez,
Department of Electrical Engineering, Technion
Supervisors :
Dr. Yoav Y. Schechner, Prof. Dan Adam
Ack. GE medical Systems

2. Ultrasound Image Degradations
Transmitter
Receiver
Speckle noise
Blurring
Radial axis
Our Goal: Image reconstruction
Attenuation
System noise
Lateral axis
Ultrasound image
True object

3. Previous Work 70s Wiener filter Space invariant
Not using noise statistics
80s
Compounding (frequency & spatial) 86
Weighted median filter (Mcdicken et al.) 89
Local frequency diversity (Forsberg et al.)
Smoothing
Not handling attenuation 90
Anisotropic diffusion (Perona and Malik) 95
Non-linear Gaussian filters (Aurich)
Late 90s
Harmonic imaging
Low signal
01,04
Wavelets (Insana et al, Loi et al.)

4. Outline
Theoretical background
Deterministic reconstruction

5. Outline Theoretical background Image formation Speckle noise
Deterministic reconstruction
Frequency compounding

6. Image Formation - Transmitting
1D model
Probe
Acoustic signal
Electrical
pulse

7. Image Formation - Receiving
1D model
probe
Returning echoes
RF line

8. Typical velocity of acoustic signal in tissue
Image Formation
1D model
Pulse echo technique
probe
Received signal
Typical velocity of acoustic signal in tissue

9. Amplitude attenuation
Image Formation
Attenuation
probe
Amplitude attenuation
coefficient
Frequency of
acoustic signal
Depth

10. Radial Transfer Function
Goal: Estimate the radial transfer function
Water tank 1 2 3 4 5 6 7 8 9 10
Temporal frequency (MHz)

11. Radial Transfer Function
Water tank 1 2 3 4 5 6 7 8 9 10
Temporal frequency (MHz)

12. Radial Transfer Function
Electrical pulse
Transfer function of the probe
Depth
Attenuation

13. Image Formation - Transmitting
Phased Array Principle

14. Image Formation - Transmitting
Sector
Probe
Radial axis
Transversal axis
Sweeping beam
Lateral axis
Assuming a 2D model

15. Frame Rate Each scanned sector is a frame
The Frame rate is determined by:
Frame processing time
Echo Fading time
Desired sector angle
Desired sector radius (not really a limitation)

16. Total Transfer Function1 2 3 4 5 6 7 8 9 10
Spatial frequency (1/mm)

17. Lateral Transfer Function
lateral distance (mm)
Radial distance (mm) -5 5 30 20 10 40
Goal: Estimate the lateral transfer function

18. Lateral Transfer Function
Initial beam width
Radial distance from the probe
Acoustic frequency
High acoustic
frequency
Low acoustic
frequency

19. Image Formation - Total
2D model
2D image
True object
Blur
Attenuation
Radial blur
Lateral blur

20. Standard Image Processing
Dynamic range compression
RF line
Time gain compensation
Sampling
Envelope detection

21. Outline Theoretical background Image formation Speckle noise
Deterministic reconstruction
Frequency compounding

22. Coherent signal phenomenon
Constructive
interference
Destructive
interference

23. Coherent signal phenomenon
Generated image
without interference
Speckle caused
by interference
Object
Speckle Noise

24. Speckle Noise Low acoustic frequency High acoustic frequency
Multiplicative model:

25. Outline Theoretical background Image formation Speckle noise
Deterministic reconstruction
Frequency compounding

26. Frequency Compounding
Compounded image

27. Enabling Technologies
Dual frequency transducer (Bouakaz et al. 04, Jadidian et al. 04)
MEMS (Ladabaum et al. 98)

28. Frequency Compounding
Common compounding techniques
(Bilgutai et al. 86)
An arithmetic mean
Arithmetic mean of the squared signals
Minimum of the squared signals
Disadvantages
Not handling attenuation
Space invariant
Goal: Space variant reconstruction

29. Frequency Compounding
Decreasing weight + -
Increasing weight + -
Low acoustic frequency
High acoustic frequency

30. Outline Theoretical background Handling system noise
Handling speckle noise
Recovering deep objects
No resolution loss
Deterministic reconstruction

31. Acquired Images True object
Acoustic frequencies range from 1.6MHz to 3.3 MHz

32. Acquired Images True object
Acoustic frequencies range from 1.6MHz to 3.3 MHz

33. Depth Dependent Averaging
Compounding two images 1
Low acoustic frequency
High acoustic frequency
weight
High
resolution
Control parameter
Noise
averaging
Recovering
deep objects
Example:
Space variant
distance from the probe

34. Depth Dependent Averaging
Input:
Low acoustic frequency
High acoustic frequency
Output:
Depth dependant Averaging
Arithmetic mean

35. Depth Dependent Averaging
Input:
Low acoustic frequency
High acoustic frequency
Output:
Depth dependant Averaging
Arithmetic mean
Similar

36. Depth Dependent Averaging
Input:
Low acoustic frequency
High acoustic frequency
Output:
Depth dependant Averaging
Arithmetic mean

37. Depth Dependent Averaging
Compounding K images
MHz
Typical parameters
2.2,2.3 MHz
2.4,2.5 MHz
2.6 MHz
>3 MHz 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Distance from the probe (cm)

38. Depth Dependent Averaging
Compounding K images
Image 1 : highest acoustic frequency …
Image K : lowest acoustic frequency needed
Control parameters
Image 1
Image 2 1
weight
Image 2
Image 3
Image K-1
Image K
Block 1
Block 2
Block K-1 . .
distance from the probe

39. Depth Dependent Averaging
5 images
2 images 4 4 6 6 8 8
Radial distance [cm]
Radial distance [cm] 10 10 12 12 14 14 16 16 -8 -6 -4 -2 2 4 6 8 -8 -6 -4 -2 2 4 6 8
Lateral distance [cm]
Lateral distance [cm]

40. Depth Dependent Averaging
5 images
2 images ?

41. Depth Dependent Averaging
Conclusions
Overcoming attenuation (recovering deep objects)
High resolution maintained
Computationally efficient
Disadvantage
Noise statistics are not considered
Goal: consider noise statistics

42. Thank you!