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

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

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.)

Outline Theoretical background Deterministic reconstruction

Outline Theoretical background Image formation Speckle noise Deterministic reconstruction Frequency compounding

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

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

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

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

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

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

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

Image Formation - Transmitting Phased Array Principle

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

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)

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

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

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

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

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

Outline Theoretical background Image formation Speckle noise Deterministic reconstruction Frequency compounding

Coherent signal phenomenon Constructive interference Destructive interference

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

Speckle Noise Low acoustic frequency High acoustic frequency Multiplicative model:

Outline Theoretical background Image formation Speckle noise Deterministic reconstruction Frequency compounding

Frequency Compounding Compounded image

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

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

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

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

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

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

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

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

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

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

Depth Dependent Averaging Compounding K images 1.6-1.9 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)

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

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]

Depth Dependent Averaging 5 images 2 images ?

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

Thank you!