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Spatially Varying Frequency Compounding of Ultrasound Images

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Presentation on theme: "Spatially Varying Frequency Compounding of Ultrasound Images"— Presentation transcript:

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 Function
1 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!


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