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Ultrasound Image Denoising by Spatially Varying Frequency Compounding

1. Dept. Elect. Eng. Technion – Israel Institute of Technology. Ultrasound Image Denoising by Spatially Varying Frequency Compounding. Yael Erez , Yoav Y. Schechner , and Dan Adam. Ultrasound Problems. 7. Transmitter Receiver. Speckle noise. Blurring. Radial axis. Attenuation.

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Ultrasound Image Denoising by Spatially Varying Frequency Compounding

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  1. 1 Dept. Elect. Eng. Technion – Israel Institute of Technology Ultrasound Image Denoising by Spatially Varying Frequency Compounding Yael Erez , Yoav Y. Schechner , and Dan Adam

  2. Ultrasound Problems 7 Transmitter Receiver Speckle noise Blurring Radial axis Attenuation System noise Lateral axis Erez, Schechner & Adam, Proc. DAGM 2006

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

  4. 8 Image Formation probe Received signal Velocity of acoustic wave in tissue Erez, Schechner & Adam, Proc. DAGM 2006

  5. Image Formation 9 Sector Probe Radial axis Sweeping beam Lateral axis Erez, Schechner & Adam, Proc. DAGM 2006

  6. 10 Lateral PSF D D High freq. = better (?) Low acoustic freq High acoustic freq Erez, Schechner & Adam, Proc. DAGM 2006

  7. Attenuation 11 r a object distance Low freq. = better (?) probe Erez, Schechner & Adam, Proc. DAGM 2006

  8. 15 Speckle Noise Low acoustic freq High acoustic freq Erez, Schechner & Adam, Proc. DAGM 2006

  9. Wave phenomenon 16 Wave interference Object blur: as if no interference Object Speckle Noise Erez, Schechner & Adam, Proc. DAGM 2006

  10. 17 PSF D D Depends on: • Radial distance • Acoustic frequency Low acoustic freq High acoustic freq Erez, Schechner & Adam, Proc. DAGM 2006

  11. 18 r = 7cm r = 11cm 1 White noise r = 15cm 0.8 0.6 0.4 0.2 0 -2 -1 0 1 2 Radial lag (mm) Measuring Noise Statistics Erez, Schechner & Adam , Proc. DAGM 2006

  12. Standard Pre-Processing 19 Time gain compensation Envelope detection Dynamic range compression RF line Sampling

  13. Speckle Noise 20 = log operation Iinear noise Erez, Schechner & Adam, Proc. DAGM 2006

  14. Model 21 … … … correlated noise !!! Erez, Schechner & Adam, Proc. DAGM 2006

  15. 22 y = Hx + n … … … Stochastic Reconstruction Erez, Schechner & Adam, Proc. DAGM 2006

  16. Best Linear Unbiased Estimator 23 Considering noise statistics Erez, Schechner & Adam, Proc. DAGM 2006

  17. Input: Dual Acoustic Frequency 24 Low acoustic freq High acoustic freq 5 6 7 Radial distance [cm] 8 9 10 11 Erez, Schechner & Adam, Proc. DAGM 2006

  18. Stochastic Freq. Compounding 25 Arithmetic mean Stochastic reconstruction 5 6 7 Radial distance [cm] 8 9 10 11 Erez, Schechner & Adam, Proc. DAGM 2006

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