1. Characterization Presentation Performed by: Ron Amit Supervisor: Tanya Chernyakova Semester: Spring 2012 1 Sub-Nyquist Sampling in Ultrasound Imaging

2. Ultrasound Device: 2

3. Problem : Modern devices require large number of receivers Acoustic pulses are of high bandwidth Typical Nyquist rate is 20 MHz * Number of receivers Large amount of data must be processed High computational cost 3

4. 4 Solution : Reduce sample rate, while still extracting the same required information for image reconstruction

5. FRI Model: 5

6. Single receiver solution : Unknown parameters are extracted from low rate samples. 6

7. Multichannel Sampling Scheme : Different sampling scheme for a single receiver, using bank of integrators 7

8. 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 SNR Represents reflections from a single angle – forming an image line 8

9. Beamforming : 9

10. 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 10

11. Using modulation with analog kernels and integration First Scheme : Problem : Analog kernels are complicated for hardware implementation 11

12. 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 12

13. 13 Analog Processing Sub – Nyquist Sampling Receiver Elements Low Rate Samples Digital Processing Amplitudes and delays of reflections Image Reconstruction Block Diagram :

14. 14 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

15. 15 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:

16. 16 תכנית עד מצגת אמצע 29.5-5.6 לימוד של שיטות סטנדרטיות לייצוג תמונה מ -beamformed data 5.6-12.6 ייצוג אלטרנטיבי של תמונה מתוצאות קיימות ( השיות ואמפליטודות ) 12.6-19.6 השוואה בין תוצאות שמתקבלות בשתי שיטות, fine tuning של שיטה אלטרנטיבית 19.6-26.6 לימוד תיאורטי של אלגוריתם ה -OMP, ניתוח של האלגוריתם שמומש אצל נועם 28.6 – 27.7 תקופת בחינות 27.7 – 1.8 הכנה למצגת אמצע