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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
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Ultrasound Image Degradations
Transmitter Receiver Speckle noise Blurring Radial axis Our Goal: Image reconstruction Attenuation System noise Lateral axis Ultrasound image True object
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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.)
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Outline Theoretical background Deterministic reconstruction
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Outline Theoretical background Image formation Speckle noise
Deterministic reconstruction Frequency compounding
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Image Formation - Transmitting
1D model Probe Acoustic signal Electrical pulse
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Image Formation - Receiving
1D model probe Returning echoes RF line
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Typical velocity of acoustic signal in tissue
Image Formation 1D model Pulse echo technique probe Received signal Typical velocity of acoustic signal in tissue
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Amplitude attenuation
Image Formation Attenuation probe Amplitude attenuation coefficient Frequency of acoustic signal Depth
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Radial Transfer Function
Goal: Estimate the radial transfer function Water tank 1 2 3 4 5 6 7 8 9 10 Temporal frequency (MHz)
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Radial Transfer Function
Water tank 1 2 3 4 5 6 7 8 9 10 Temporal frequency (MHz)
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Radial Transfer Function
Electrical pulse Transfer function of the probe Depth Attenuation
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Image Formation - Transmitting
Phased Array Principle
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Image Formation - Transmitting
Sector Probe Radial axis Transversal axis Sweeping beam Lateral axis Assuming a 2D model
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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)
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Total Transfer Function
1 2 3 4 5 6 7 8 9 10 Spatial frequency (1/mm)
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Lateral Transfer Function
lateral distance (mm) Radial distance (mm) -5 5 30 20 10 40 Goal: Estimate the lateral transfer function
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Lateral Transfer Function
Initial beam width Radial distance from the probe Acoustic frequency High acoustic frequency Low acoustic frequency
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Image Formation - Total
2D model 2D image True object Blur Attenuation Radial blur Lateral blur
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Standard Image Processing
Dynamic range compression RF line Time gain compensation Sampling Envelope detection
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Outline Theoretical background Image formation Speckle noise
Deterministic reconstruction Frequency compounding
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Coherent signal phenomenon
Constructive interference Destructive interference
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Coherent signal phenomenon
Generated image without interference Speckle caused by interference Object Speckle Noise
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Speckle Noise Low acoustic frequency High acoustic frequency
Multiplicative model:
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Outline Theoretical background Image formation Speckle noise
Deterministic reconstruction Frequency compounding
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Frequency Compounding
Compounded image
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Enabling Technologies
Dual frequency transducer (Bouakaz et al. 04, Jadidian et al. 04) MEMS (Ladabaum et al. 98)
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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
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Frequency Compounding
Decreasing weight + - Increasing weight + - Low acoustic frequency High acoustic frequency
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Outline Theoretical background Handling system noise
Handling speckle noise Recovering deep objects No resolution loss Deterministic reconstruction
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Acquired Images True object
Acoustic frequencies range from 1.6MHz to 3.3 MHz
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Acquired Images True object
Acoustic frequencies range from 1.6MHz to 3.3 MHz
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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
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Depth Dependent Averaging
Input: Low acoustic frequency High acoustic frequency Output: Depth dependant Averaging Arithmetic mean
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Depth Dependent Averaging
Input: Low acoustic frequency High acoustic frequency Output: Depth dependant Averaging Arithmetic mean Similar
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Depth Dependent Averaging
Input: Low acoustic frequency High acoustic frequency Output: Depth dependant Averaging Arithmetic mean
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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)
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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
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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]
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Depth Dependent Averaging
5 images 2 images ?
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Depth Dependent Averaging
Conclusions Overcoming attenuation (recovering deep objects) High resolution maintained Computationally efficient Disadvantage Noise statistics are not considered Goal: consider noise statistics
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Thank you!
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