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

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Presentation transcript:

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

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

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

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

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

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

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

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

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

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

Measuring Noise Statistics Radial lag (mm) r = 7cm r = 11cm r = 15cm White noise Erez, Schechner & Adam, Proc. DAGM

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

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

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

…… … y = H x + n Stochastic Reconstruction Erez, Schechner & Adam, Proc. DAGM

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

Radial distance [cm] High acoustic freqLow acoustic freq Input: Dual Acoustic Frequency 24 Erez, Schechner & Adam, Proc. DAGM 2006

Arithmetic meanStochastic reconstruction Stochastic Freq. Compounding Radial distance [cm] Erez, Schechner & Adam, Proc. DAGM 2006