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Characterization 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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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:

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אמצע מצגת עד תכנית

29.5-5.6- מ תמונה לייצוג סטנדרטיות שיטות של beamformed dataלימוד

5.6-12.6) ואמפליטודות ) השיות קיימות מתוצאות תמונה של אלטרנטיבי ייצוג

12.6-19.6 , שיטות בשתי שמתקבלות תוצאות בין אלטרנטיבית fine tuningהשוואה שיטה של

19.6-26.6- ה אלגוריתם של תיאורטי נועם, OMPלימוד אצל שמומש האלגוריתם של ניתוח

בחינות 27.7 – 28.6 תקופת

אמצע 1.8 – 27.7 למצגת הכנה