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Midterm Presentation
Performed by:Ron Amit
Supervisor: Tanya ChernyakovaSemester: Spring 2012
Sub-Nyquist Sampling in Ultrasound Imaging
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Ultrasound Device:
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Problem:Modern devices require large number of receivers
Acoustic pulses are of high bandwidthTypical Nyquist rate is 20 MHz * Number of receivers
Large amount of data must be processedHigh computational cost
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Solution:
Reduce sample rate, while still extracting the same required information for image reconstruction
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FRI Model:
• Lower bound of sample rate:
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Single receiver solution:
Unknown parameters are extracted from low rate samples.
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Multichannel Sampling Scheme:
Different sampling scheme for a single receiver, using bank of integrators
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Problem :Low SNR of received signal at a single
receiver.
Solution : Use array of receivers and combine the received
signals – Beamforming process.Beamformed signal has improved SNRRepresents reflections from a single angle –
forming an image line
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Beamforming:
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Compressed Beamforming:
• Combines Beamforming and sampling process.• Received signals are sampled at Sub-Nyquist rate• The scheme’s output is a group of Beamformed signal ‘s
Fourier coefficients• Digital processing extracts the Beamformed signal
parameters
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Using modulation with analog kernels and integration
First Scheme :
Problem : Analog kernels are complicated for hardware implementation
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Simplified Scheme :
• Based on approximating each received signal by only Ki Fourier coefficients
• Each received signal is filtered by a simple analog filter
• Linear transformation on the samples provides the Beamformed signal Fourier coefficients
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Analog Processing
Sub – Nyquist
Sampling
Receiver Elements
Low Rate Samples
Digital Processin
gAmplitudes and delays of reflections
Image Reconstructi
on
Block Diagram:
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Image Construction:
Standard Image Construction:• Delays and amplitudes are translated to a stream of modulated
pulses• Hilbert transform is used for un-modulation• The data points in 120 image lines (angles) are interpolated to a
2-D Cartesian Image Problem:
The standard process is complicated and slow• 2-D interpolation is very slow• Doesn't use the fact that Xampled Images are
mostly zero • Modulation and Un-modulation is unnecessary
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Alternative Image Construction:
• Build signals with un-modulated pulse shape• Only one dimensional interpolation: in angle axis• Finds nearest Cartesian coordinates for every data point (which
is in Polar coordinates ) and place the amplitude (nearest neighbor method)
• Computation is done only for non-zero data points
Goal: Faster image construction from Xampled data
Solution:
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Alternative Image Construction:
Xampled Image - Standard Image Construction
-80 -60 -40 -20 0 20 40 60 80
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Xampled Image - Alternative image Construction
-80 -60 -40 -20 0 20 40 60 80
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Average runtime: 4 seconds
Average runtime: 0.5 seconds
Standard Image Construction:
Almost identical image!Reduced computation complexity!
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Project Goals:Main goal: Prove the preferability of the Xampling method for Ultrasound devices
Sub goals:• Alternative image reconstruction • Optimize algorithm and improve runtime• Explore hardware implementation
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Semester 1:Understand and run current code
Improvement:• Image construction from pulses• Lighter OMP algorithm
Semester 2:Algorithm optimization:• Flow graph algorithm• Complexity analysis of subroutines• Runtime optimization
System analysis :• How to implement on processer platform for
maximal performance
Mission Plan: