ultrasoundtogo - nano-tera 2016

8/18/2019 UltraSoundtoGO - Nano-Tera 2016

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UltrasoundToGo

Giovanni De Micheli

Luca Benini

Jean-Yves Mewly

Joseph Sifakis

Lothar Thiele

Jean-Philippe Thiran


2/22

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3/22

What is needed?

1. A portable, inexpensive, low-power scanner

2. The scanner must be 3D

Acquires volumes; reduces threshold of operator’s expertise

Only other way to remotely acquire images: robotic arm; complex

3. Good enough imaging quality for the applications

4. A protocol to tag and remotely visualize the scans

ARTIS project by ESA

for space applications

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4/22

Professional 3D scannersexpensive, bulky, >500W

Efforts for mobility are usually 2D (still radiologist…)

First attempt at miniaturized 3D, but full of limitations

Related products

Siemens Acuson, Philips Epiq, Samsung WS80…

BK Ultrasound’s Sonic Window

To locate vessels for dialysis cannulation. Acquires

images 3 cm deep and only computes one cross-section.

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[…] MU’s US-304 portable ultrasound imager,

powered by ST […] aiming to […] diagnostics in

remote rural areas of Africa. 14.04.2016

Philips

Visiq


5/22

2015/16 UltrasoundToGo Progress

Advanced modeling of the acquisition process (Matlab)

Co-designed new 3D probe (transducer and circuit)

in collaboration with Fraunhofer IBMT, Germany

Realized digital beamforming on a new FPGA platform

Developed toolchains for efficient mapping of ultrasound

beamforming onto parallel hardware architectures

Improved algorithms to use compressive sensing allowing

for a reduction in cabling and analog electronics requirements

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6/22

Development of a Matrix Array

Probe is essential to image quality

Collaboration project with Fraunhofer IBMT, DE

Fraunhofer: piezoelectric array(32x32 = 1024 elements),

analog cabling,

custom connector

IIS-ETHZ: miniaturized analog electronics, PCB layout

Final assembly imminent

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Array close-up

(last week)


7/22

Probe Electronics Design

Reduces volume by 2X and cross-section by 3X compared

to previous prototype (30x33 mm – handheld) 9


8/22

Probe and System Design

Sensor Array

(32x32 = 1024

elements)

4:1 analogMultiplexing

and amplification

256-channel I/O

Custom cabling

and connector Analog

front-end

Digital ultrasound

image formation

Fraunhofer imager (DiPhAS)

UltrasoundToGo imager

Offline or

realtime

Next steps:1. Connect to Fraunhofer’s

DiPhAS, acquire echoes, process

them offline on our digital imager

2. (Pending funding application)

Acquire same machine at EPFL as

bridge for realtime imaging 10


9/22

Development Board

Kintex Ultrascale FPGA chip

Performs 1024-channel beamforming(delay, apodization, sum)

Gigabit Ethernet port

Currently used for input

of digitized echoes and

output of images

HDMI portWill be used for direct

video output (requires

on-chip scan conversion)

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10/22

FPGA Beamformer results

32x32=1024-channel in a single XCKU040 chip

Most high-end equipment supports only 256

Configurable volume: 73°x73°x10cm

64x64x600 = 2.5Mvoxels per frame

One insonification per frame

Beamformer operates at 125 MHz

Approximate power consumption: 4W Peak throughput: 50 fps

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11/22

Future View – 2016/17

Concentrates analog processing into the probe to reduce

costs and allow for efficient fully-digital data processing

Enables fully-in-house UltrasoundToGo imager 13


12/22

3D beamforming per-nappe approach

High level of parallelisaton:

46,664 beamformer instances deployed toKalray MPPA-256

3D US deployed on Kalray MPPA-256

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13/22

Offline: 1. Computation of the WCET as a sum of WCCT and delay due to interferences

2. Optimization based on WCET providing real-time guarantiesOnline: Run-time optimizations based on actual execution times (AET)

Task

A

Task

C

ω(eAC)

Task

Bω(eAB)

Cluster 1 Cluster 2b(eAC)

IA TA0 ω(eAB)

FA

0

b(eAB)

IB

TB

0

0

b(eBA)

Online

Partitioning and placement

Mapping, Scheduling,buffer allocation

Run-time optimization

1. Many-core Kalray MPPA-256

2. Application

WCET overapproximation

3. Worst case computation time

(WCCT) in isolation

SMT

solver

SMT

solver

WCET

Updating the schedule

Tightening of the WCET by

pruning out interferences from

tasks not overlapping neither in

space nor in timeOffline

Tighter

WCET

4. Unified system model

Application Deployment

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14/22

Time running the whole application of the host processor:

Time when Beamforming is running on the Kalray MPPA-256 chip:

Probe = 12 x 12 phased array;

Volume depth = 4.5cm

ϕ,ϑ∈ [-38°, 38°];

fc = 4 MHz;

fs = 200 MHz

A blanket of echo signals that need to be stored on each cluster:

Memory required by one blanket is ~ 1MB

Probe = 64 x 64 phased array;

Volume depth = 4.5cm

ϕ,ϑ∈ [-38°, 38°];

fc = 4 MHz;

fs = 12 MHz

Two possible configurations:

~ 30s (1 thread) ~ 13 minutes (1 thread)

~ 0.5s ~ 14s

Possible 3D US configurations

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15/22

Mapping Algorithms to Architecture

Goal: Map application to system architecture towards apredictable and efficient execution.

How to specify the application? Neutral w.r.t. hardware or software.

High expressivity (adaptive, mode change)

CAL Actor Language

RVC-CAL designed and standardizedby MPEG group

Used to specify hardware and software

Conversion to Kahn process networks

for anaysis 17


16/22

Adapteva Parallella board: Zynq dual core ARM A9 CPU and FPGA Epiphany 16-core coprocessor

→ Ideal test platform for HW/SW integration

Case Study

Case study:

2D beamforming

on the

parallella board

Presented at N-T

annual meeting

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17/22

Compressed Beamforming Framework

for 2D Ultrasound Imaging

Reducing memory footprint, data rate and cabling

is crucial for portable ultrasound

Compressed beamforming:

Acquire ultrasound echo signals with fewer sensors Design of new sensing strategies

Reconstruction with compressed-sensing-based algorithms

Classical

acquisition

Delay-and-

Sum

Compressed

beamforming

Compressed

acquisition High quality

image

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18/22

Results on in vivo carotids

Reference image

Standard image

reconstruction

algorithm

128 elements

CTR = -31dB

Proposed image

Compressed image

reconstruction

algorithm

32 elements

CTR = -31dB

Reference image

Standard image

reconstruction

algorithm

32 elements

CTR = -26dB21


19/22

Compressed Beamforming Outlook

Compressed sensing

Acquires ultrasound echo signals with fewer sensors

Performs an iterative reconstruction of the high quality

image based on compressed-sensing algorithms

Results:

Significant data rate reduction (~75%)

Significant decrease of the memory footprint (~75%)

Image quality is preserved

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20/22

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Summary

• Developed a front-end US system in collaboration with

Fraunhofer Institute

• Designed a full 1024 Channel Beamformer on a FPGA

• Power consumption lower than 4W• Developed a tool flow for realizing a beamformer on a

multiprocessor 16x16 core Kalray unit

• Designed compressed sensing algorithms for reducing

system size and power while preserving image quality

• Interacted with medical doctors at CHUV for advice


21/22

UltrasoundToGo Current Staff

Integrated Systems Laboratory (EPFL-LSI)

Aya Ibrahim, Dr. Federico Angiolini, Prof. Giovanni De Micheli

Rigorous System Design Laboratory (EPFL-RiSD) Stefanos Skalistis, Dr. Alena Simalatsar, Prof. Joseph Sifakis

Signal Processing Laboratory (EPFL-LTS5)

A. Besson, Dr. R. Carrillo, Dr. M. Arditi, Prof. J.-Ph. Thiran

Integrated Systems Laboratory (ETHZ-IIS) Pascal Hager, Dr. Andrea Bartolini, Prof. Luca Benini

Computer Engineering and Networks Laboratory (ETHZ-TIK)

Andreas Tretter, Prof. Lothar Thiele

Service de radiodiagnostic et radiologie interventionnelle (CHUV)

Prof. Jean-Yves Meuwly 24


22/22

Thanks for your attention!

Visit us at www.nano-tera.ch

www.nano-tera.ch

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ultrasoundtogo - nano-tera 2016

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