1. Ultrasonic Imaging using Resolution Enhancement Compression and GPU- Accelerated Synthetic Aperture Techniques Presenter: Anthony Podkowa May 2, 2013 Advisor: Dr José R. Sánchez Department of Electrical and Computer Engineering

2. Outline I. Motivation & project summary II.Block diagram A.REC B.GSAU III.Results IV.Areas of Expansion 2

3. Outline I.Motivation & project summary II.Block diagram A.REC B.GSAU III.Results IV.Areas of Expansion 3

4. Motivation Key medical imaging technique Key medical imaging technique Tumor detection Tumor detection Seek to improve Seek to improve Spatial resolution Spatial resolution Signal-to-noise ratio (SNR) Signal-to-noise ratio (SNR) 4

5. Project Summary Resolution enhancement compression (REC) Resolution enhancement compression (REC) Coded excitation and pulse compression technique Coded excitation and pulse compression technique Improved axial resolution Improved axial resolution Improved SNR Improved SNR Generic synthetic aperture ultrasound (GSAU) Generic synthetic aperture ultrasound (GSAU) Synthetic aperture technique Synthetic aperture technique Improves lateral resolution Improves lateral resolution Improves SNR Improves SNR Computationally expensive, but parallelizable Computationally expensive, but parallelizable 5

6. Goals: 1. To investigate the combination of both REC and GSAU in an ultrasound system using MATLAB and Field II. 2. To accelerate the GSAU algorithm using a graphics processing unit (GPU) to achieve real-time processing of the images. 6

7. Outline I. Motivation & project summary II.Block diagram A.REC B.GSAU III.Results IV.Areas of Expansion 7

8. System Block Diagram 8 Encoder TransducerGSAU V in (t) V pc (t) Image Recon. Image Output V lc (t) Received Echo Signals Beamformed Signals 256 Wiener Filter Compressed Signals 256

9. Outline I. Motivation & project summary II.Block diagram A.REC B.GSAU III.Results IV.Areas of Expansion 9

10. Resolution Enhancement Compression Based on the convolution equivalence principle Based on the convolution equivalence principle Encoder shapes excitation signal Encoder shapes excitation signal Wiener Filter: Wiener Filter: Compresses the received signals Compresses the received signals Removes corrupting noise Removes corrupting noise 10 Encoder Transducer V in (t) V pc (t) V lc (t) Received Echo Signals 256 Wiener Filter Compressed Signals 256

11. Convolution Equivalence Principle Make h t (t) act like h d (t) by shaping v 1 (t) Make h t (t) act like h d (t) by shaping v 1 (t) Wiener deconvolution. Wiener deconvolution. 11 Desired Response Desired system Transducer Some other input Some input

12. Encoder Subsystem V ulc (f) V pc (f) Tukey Window V lc (f) Wiener Deconvolution Filter Inverse Filter V upc (f) 12

13. Encoder Subsystem V ulc (f) V pc (f) Tukey Window V lc (f) Wiener Deconvolution Filter Inverse Filter V upc (f) 13

14. Encoder Subsystem V ulc (f) V pc (f) Tukey Window V lc (f) Wiener Deconvolution Filter Inverse Filter V upc (f) 14

15. Encoder Subsystem V ulc (f) V pc (f) Tukey Window V lc (f) Wiener Deconvolution Filter Inverse Filter V upc (f) 15

16. System Block Diagram 16 Encoder TransducerGSAU V in (t) V pc (t) Image Recon. Image Output V lc (t) Received Echo Signals Beamformed Signals 256 Wiener Filter Compressed Signals 256

17. Transducer Specifications 256 elements 256 elements 8 MHz center frequency 8 MHz center frequency 200 MHz sampling frequency 200 MHz sampling frequency 4 mm element height 4 mm element height 0.26 mm element width 0.26 mm element width 0.04 mm element kerf 0.04 mm element kerf 20 mm focus 20 mm focus Height Width Kerf 17

18. System Block Diagram 18 Encoder TransducerGSAU V in (t) V pc (t) Image Recon. Image Output V lc (t) Received Echo Signals Beamformed Signals 256 Wiener Filter Compressed Signals 256

19. Outline I. Motivation & project summary II.Block diagram A.REC B.GSAU III.Results IV.Areas of Expansion 19

20. GSAU Technique Transmit and receive with one element at a time. Transmit and receive with one element at a time. Calculate delays associated with the distances from element to each pixel: Calculate delays associated with the distances from element to each pixel: 256 x 30000 pixels 256 x 30000 pixels Parallel processing Parallel processing 20

21. GPU Programming (CUDA) 21 HostDevice Up to 8 coresHundreds of cores Memory Transfer

22. CUDA C 22 Allocate data memory on device Allocate data memory on device Copy data from the host memory to the device Copy data from the host memory to the device Spawn several threads to process the data Spawn several threads to process the data Each thread runs the same chunk of code (kernel) Each thread runs the same chunk of code (kernel) Each thread processes the pixel corresponding to its thread index. Each thread processes the pixel corresponding to its thread index. Copy data back from device memory Copy data back from device memory Free device memory Free device memory

23. Test Hardware Specifications CPU:Intel Core i7-2600K CPU:Intel Core i7-2600K 4 Cores 4 Cores Processor Clock: 3.4 GHz Processor Clock: 3.4 GHz RAM:16 GB RAM:16 GB GPU:NVIDIA Quadro 5000 GPU:NVIDIA Quadro 5000 352 CUDA cores 352 CUDA cores Processor Clock:1026MHz Processor Clock:1026MHz RAM:2560 MB GDDR5 RAM:2560 MB GDDR5 Memory Bandwidth: 120 GB/s Memory Bandwidth: 120 GB/s 23

24. System Block Diagram 24 Encoder TransducerGSAU V in (t) V pc (t) Image Recon. Image Output V lc (t) Received Echo Signals Beamformed Signals 256 Wiener Filter Compressed Signals 256

25. Image Reconstruction Subsystem Envelope Detection Logarithmic Compression Limiter Beamformed Signal Image Scan Line 25

26. Outline I. Motivation & project summary II.Block diagram A.REC B.GSAU III.Results IV.Areas of Expansion 26

27. Simulation Settings Point imaged at 20mm Point imaged at 20mm Tukey window taper: α = 0.08 Tukey window taper: α = 0.08 γ = 1 (Wiener filter) γ = 1 (Wiener filter) Additive noise injected (σ n = 0.1 σ s ) Additive noise injected (σ n = 0.1 σ s ) Excitation schemes studied: Excitation schemes studied: REC REC Conventional pulsing (Delta function) Conventional pulsing (Delta function) 27

28. Encoding 28 Linear chirp: Linear chirp: 0 – 17.12 MHz 0 – 17.12 MHz 12.5 μs 12.5 μs Desired Response: Desired Response: 200% BW 200% BW Transducer Response: Transducer Response: 100% BW 100% BW MSE: 4.46x10 -7 MSE: 4.46x10 -7

29. GPU Acceleration 29 GPUs perform faster using single precision GPUs perform faster using single precision 4.5% round off error 4.5% round off error Computation time decreased from 29.25 s to 0.25 s Computation time decreased from 29.25 s to 0.25 s

30. Wiener Filter 30 Received signals compressed axially Received signals compressed axially 3 dB gain in SNR 3 dB gain in SNR

31. REC + GSAU 31 Received signals compressed laterally Received signals compressed laterally 5 dB gain in SNR 5 dB gain in SNR

32. CP + GSAU 32 Received signals compressed laterally Received signals compressed laterally SNR loss of 0.3 dB SNR loss of 0.3 dB 10 dB less SNR than REC + GSAU, and 5 dB less than REC alone 10 dB less SNR than REC + GSAU, and 5 dB less than REC alone

33. Resolution Analysis 33 Resolution computed from the modulation transfer function (MTF) Resolution computed from the modulation transfer function (MTF) MTF is the spatial Fourier transform of the point spread function (PSF). MTF is the spatial Fourier transform of the point spread function (PSF). Critical wavenumber k 0 computed by determining the point where normalized MTF crosses 0.1 Critical wavenumber k 0 computed by determining the point where normalized MTF crosses 0.1 Resolution given by: Resolution given by:

34. Axial Resolution 34 CP: 0.52022 mm CP: 0.52022 mm REC: 0.44062 mm REC: 0.44062 mm CP+GSAU: 0.54117 mm CP+GSAU: 0.54117 mm REC+GSAU: 0.64507 mm REC+GSAU: 0.64507 mm

35. Lateral Resolution 35 CP: 0.28149 mm CP: 0.28149 mm REC: 0.29489 mm REC: 0.29489 mm CP+GSAU: 0.10321 mm CP+GSAU: 0.10321 mm REC+GSAU: 0.10321 mm REC+GSAU: 0.10321 mm

36. Outline I. Motivation & project summary II.Block diagram A.REC B.GSAU III.Results IV.Areas of Expansion 36

37. Potential Areas of Expansion GSAU GSAU Improved interpolation (linear, polynomial) Improved interpolation (linear, polynomial) Alternative reweighting schemes Alternative reweighting schemes Other SA techniques: Other SA techniques: Synthetic transmit aperture ultrasound (STAU) Synthetic transmit aperture ultrasound (STAU) Synthetic receive aperture ultrasound (SRAU) Synthetic receive aperture ultrasound (SRAU) GPU speedup GPU speedup Use of optimized libraries (CUBLAS, MAGMA) Use of optimized libraries (CUBLAS, MAGMA) Reduce thread overhead Reduce thread overhead 37

38. Conclusions 38 REC + GSAU exhibit the best performance in SNR. REC + GSAU exhibit the best performance in SNR. CP + GSAU exhibit the best performance in spatial resolution. CP + GSAU exhibit the best performance in spatial resolution. GPU acceleration results in a speedup by a factor of 116. GPU acceleration results in a speedup by a factor of 116.

39. References 39 [1] M. Oelze, “Bandwidth and resolution enhancement through pulse compression,” IEEE Trans. Ultrason., Ferroelec., and Freq. Contr., vol. 54, no. 4, pp. 768-781, Apr. 2007. [2] J. Sanchez and M. Oelze, “An ultrasonic imaging speckle-suppression and contrast-enhancement technique by means of frequency compounding and coded excitation,” IEEE Trans. Ultrason., Ferroelec., and Freq. Contr., vol. 56, no. 7, pp. 1327-1339, Jul. 2009. [3] S. Nikolov, “Synthetic aperture tissue and flow ultrasound imaging,” Ph.D. dissertation, Technical University of Denmark, 2001. [Online]. Available: https://svetoslavnikolov.wordpress.com/synthetic-aperture-ultrasound-imaging/ [4]J. Jensen, “Field: A program for simulating ultrasound systems,” in Medical & Biological Engineering & Computing, vol. 34, 1996, pp 351-353 [5]J. Jensen, and N. Svendsen, “Calculation of pressure fields from arbitrary shaped, apodized, and excited ultrasound transducers,” IEEE Trans. Ultrason., Ferroelec. and Freq. Contr.

40. Ultrasonic Imaging using Resolution Enhancement Compression and GPU- Accelerated Synthetic Aperture Techniques Presenter: Anthony Podkowa May 2, 2013 Advisor: Dr José R. Sánchez Department of Electrical and Computer Engineering

41. Importing into MATLAB 41 Generate PTX file from CUDA code Generate PTX file from CUDA code Initialize kernel object using PTX file Initialize kernel object using PTX file Convert input data to a gpuArray Convert input data to a gpuArray Evaluate kernel Evaluate kernel Bring the output data back using the gather() function Bring the output data back using the gather() function

42. Derivation of Envelope Detection 42

43. Apodization Spatial Windowing Spatial Windowing Used to shape the beam profile Used to shape the beam profile Reweighting by apodization coefficients Reweighting by apodization coefficients a1a1 a2a2 aNaN 43

44. Generic Synthetic Aperture Ultrasound Electrically focus signals to create an artificial aperture. Electrically focus signals to create an artificial aperture. Pros: Pros: Improved lateral resolution. Improved lateral resolution. Improved SNR. Improved SNR. Cons: Cons: Computationally expensive. Computationally expensive. 44