Mobile Wireless Ultrasound with GPU Beamforming Jesper Mosegaard , - - PowerPoint PPT Presentation

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Mobile Wireless Ultrasound with GPU Beamforming Jesper Mosegaard , - - PowerPoint PPT Presentation

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Click to edit Master title style Mobile Wireless Ultrasound with GPU Beamforming Jesper Mosegaard , PhD Head of Computer Graphics Lab Alexandra Institute Denmark The Alexandra Institute Click to edit Master title style Private

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Jesper Mosegaard, PhD Head of Computer Graphics Lab Alexandra Institute Denmark

Mobile Wireless Ultrasound with GPU Beamforming

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  • Private not-for-profit company within IT

– Technology transfer from University research through GTS institutes (Danish model)

  • Application oriented research
  • Consultancy for companies

The Alexandra Institute

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FutureSonic

  • ” A new platform and

business model for

  • n-demand diagnostic

ultrasound imaging“

  • 2013-2018
  • Budget: 23 mio US$

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  • Center for Fast Ultrasound Imaging, Technical

University of Denmark

– Martin Christian Hemmsen – Borislav G. Tomov – Jørgen Arendt Jensen

  • BK Medical

– Carsten Kjær

  • Computer Graphics Lab, Alexandra Institute

– Thomas Kim Kjeldsen – Lee Lassen – Jesper Mosegaard

Joint work

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Presentation based on publications

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  • M. C. Hemmsen, T. Kjeldsen, L. Larsen, C. Kjaer, B. G. Tomov, J. Mosegaard,

and J. A. Jensen, “Implementation of synthetic aperture imaging on a hand- held device,” in Proceedings of 2014 ieee international ultrasonics symposium, 2014, pp. 2177-2180.

  • T. Kjeldsen, L. Lassen, M. C. Hemmsen, C. Kjaer, B. G. Tomov, J. Mosegaard, and J. A. Jensen,

“Synthetic aperture sequential beamforming implemented on multi-core platforms,” in Proceedings of 2014 ieee international ultrasonics symposium, 2014, pp. 2181-2184.

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Medical Ultrasound

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Mobile transducer

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Leveraging disruptive technology

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Ultrasound

  • From acoustic (pressure) waves to images

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Medium Velocity (m/ sec) Fat 1450 Water 1480 Soft tissue 1540 Kidney 1560 Blood 1570 Muscle 1580 Bone 4080

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Beamforming to reconstruct images

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Shooting for a number of scanlines

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Dynamic Receive beamforming

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Scanline Dynamic focus point Multiple transducer elements

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  • Traditional beamforming requires a high data bandwidth.
  • A typical system could have 128 channels and use a 12-

bit 40 MHz sampling system.

  • This generates 128 × 40 × 106 Hz × 2B = 9.54 GB/s

Beamforming

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  • Simple first stage

– Single focal point for both transmit and receive

  • Advanced second stage

– combining information from multiple first stage focused scan lines

  • Reduction in data-transfer requirement

– Reduced by a factor of receive elements (192)

SASB – dual beamforming

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  • M. C. Hemmsen, J. M. Hansen, and J. A. Jensen, “Synthetic Aperture

Sequential Beamformation applied to medical imaging using a multi element convex array transducer,” in EUSAR , Apr. 2012, pp. 34–37.

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Algorithmic engineering

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  • 1. stage: Fixed focus transmit and receive

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First stage line Fixed focus point Receiver r receives at time a+2*b +c a b c

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  • 2. stage: reconstruct focus

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  • 1. Find index

scanline entries

  • 2. Add contribution
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The math behind it

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for all image samples p with polar coordinates (i,j) for all first stage scanlines k a = calculateWeight(i,k) d = calculateDelay(i,k) s = getScanline(d,k) l(i,j) += a*s

Pseudo code

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Three implementations

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AVX Multithreaded

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Benchmark, simple implementation

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600,000 ¡ 2950 ¡ 699 ¡ 3.05 ¡ 0 ¡ 1 ¡ 10 ¡ 100 ¡ 1,000 ¡ 10,000 ¡ 100,000 ¡ 1,000,000 ¡ Matlab ¡ C ¡(1 ¡thread) ¡ C ¡(8 ¡threads) ¡ OpenCL ¡ ms ¡per ¡frame ¡

Simple ¡

Simple ¡

Intel Core i7 2600 Nvidia GTX 680 GPU

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  • Sampling the beamforming directly in the scan line sample

locations

Algorithmic optimization

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Weights and delays precalculated

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r k Apodization Delays r k

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l = 0 for all image samples p with polar coordinates (i,j) for all first stage scanlines k - up to N(ri) a = getWeight(i,k) if a=0 then break d = getDelay(i,k) s = getScanline(d,j+k) l(i,j) += a*s if (k>0) then s = getScanline(d,j-k) l(i,j) += a*s

Pseudo code

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Benchmark, Optimization

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600,000 ¡ 2950 ¡ 699 ¡ 3.05 ¡ 20.9 ¡ 5.4 ¡ 0.49 ¡ 0 ¡ 1 ¡ 10 ¡ 100 ¡ 1,000 ¡ 10,000 ¡ 100,000 ¡ 1,000,000 ¡ Matlab ¡ C ¡(1 ¡thread) ¡ C ¡(8 ¡threads) ¡ OpenCL ¡ ms ¡per ¡frame ¡ Simple ¡ OpAmizaAon ¡

Intel Core i7 2600 Nvidia GTX 680 GPU

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Resulting image quality

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Matlab SIMD/Multicore OpenGL OpenCL RMSE 0.0044 0.0042 0.0040 PSNR 47.23dB 47.79dB 48.01dB

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Going mobile (OpenGL ES 3.0)

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Mobile hardware

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LG G2 Samsung Galaxy Tab Samsung Nexus 10 Nvidia Jetson TK1 HTC Nexus 9 SoC Snadragon 800 Exynos 5 Exynos 5220 Tegra K1 Tegra K1 GPU Adreno 300 Mali T628 Mali T604 Kepler Kepler Screen 1920x1080 2560x1600 2560x1600 1920x1080 2048x1536 OS Android Android Android Linux4Tegra Android

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  • Need 25.3 MB/s à IEEE 802.11ac

Mobile WIFI capabilities

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28.8 ¡ 18.5 ¡ 11.2 ¡ 43.4 ¡ 35 ¡ 0 ¡ 5 ¡ 10 ¡ 15 ¡ 20 ¡ 25 ¡ 30 ¡ 35 ¡ 40 ¡ 45 ¡ 50 ¡ LG ¡G2 ¡ Galaxy ¡Tab ¡Pro ¡ Nexus ¡10 ¡ ¡ Jetson ¡TK1 ¡+ ¡Intel ¡ 7260HMW ¡ Nexus ¡9 ¡ MB/s ¡

WIFI ¡throughput ¡

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Mobile performance

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0 ¡ 10 ¡ 20 ¡ 30 ¡ 40 ¡ 50 ¡ 60 ¡ 70 ¡ LG ¡G2 ¡(Adreno ¡330) ¡ Galaxy ¡Tab ¡Pro ¡(Mali ¡ T628) ¡ Nexus ¡10 ¡(Mali ¡T604) ¡ Jetson ¡TK1 ¡(Tegra ¡K1) ¡ Nexus ¡9 ¡(Tegra ¡K1) ¡ ms ¡

Timings ¡

Scanconversion ¡ BeamformaAon ¡ IQ ¡demodulaAon ¡ Datatransfer ¡ Total ¡Aming ¡

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Going mobile, Nexus 9

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Digital or wireless?

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Mail: jesper.mosegaard@alexandra.dk Twitter: @mosegaard LinkedIn: linkedin.com/in/mosegaard Please complete the Presenter Evaluation sent to you by email or through the GTC Mobile App. Your feedback is important!

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