Ultrasound measurements on tissue

Penny Probert SmithInstitute of Biomedical EngineeringDepartment of Engineering Science University of Oxford

(also Professors Alison Noble, Harvey Burd; Dr Fares Mayia, Russ ShannonChris Haw, Emma Crowley, Jon Dennis)

Mechanical model of tissue

Viscoelastic properties

Non-linear Almost incompressible G,E<<K

)1(2

EG

)21(3

EK

Kelvin or Voigt model

Maxwell Model

GE 3;5.0

Why ultrasound?

Possibility of in-vivo measurements Compared with MRI:

Cheaper Faster (so possibility of measurements

during muscle action)

BUT LESS ACCURATE

Propagation of ultrasound in tissueRelevant material properties

Wave propagation velocity depends mainly on elasticity, density:

Independent of frequency

Attenuation (longitudinal and transverse waves) depends on shear viscosity Also frequency dependent BUT also affected by scattering

Multimode operation

sec/mU

c

Spectral response

Stokes-Navier eqn inherently non-linear; normally make linear assumption Reasonable assumption for propagation in water Poor assumption in tissue – exploited in e.g.

harmonic imaging. Non-linearity coefficient: B/A

proportion of second to first harmonic excited Depends on tissue composition, orientation Can measure through taking spectrum of echo

signals

Measurements

Compression, shear velocity measurements – ex vivo Leads to estimation of K,G

Elastography (in-vivo) Strain visualisation

Shear elastography (in- vivo) Leads to estimation of G

Compression measurements on fish muscle

To assess lipid content

22

21

2

)1(1

c

x

c

x

c

Mixture rule: relates volume fraction, x , to changes in material propertiese.g. velocity

Experimental rig

sample

flightoftime

LLvelocity calibratesample TX RX

Correlation with tissue composition

High repeatability in measurement system Good repeatability and correlation with elastic properties

in phantom (normally a gel) or water

Height of water column

Spe

ed o

f so

und

Fat content (from chemical analysis)

Spe

ed o

f so

und

But not so good in tissue ..

Causes of error in samples

Region of muscle Region of fat (myosepta)

Structure

Shape and orientationLoading: 0.2% compressive strain - but hard to judge 0% strain

Specimen preparation: Degassing – air bubbles have huge effect

Velocities in other tissues

Important issue in ultrasound imaging

Fat composition very important Data mixed; poor repeatability

between different people/tissues In-vivo the fat layer causes most

distortion

Measuring shear velocity – the eye lens

Low frequency vibration excites shear waveTime of flight measurement gives velocity

Pressure from motor? Time dependent effects?

Oscilloscope

For eye lens ..

High attenuation at ultrasound frequencies Mechanical (or low frequency) wave excitation

Results compare well with other estimates(spinning lens, deformation)

In-vivo methods

Can monitor the tendon/muscle etc in use and under different (real) loading

Limited in ultrasound windows Signal may be affected by other tissue – eg

fat layer Possible to probe particular parts of the

anatomy

Elastography

Ultrasound modality becoming standard Designed for in-vivo use – used mainly in

tumour detection Measures tissue displacement – either

through B-mode or r.f. image

Soft tissue biomechanics Elasticity imaging

P = P0

P = P0+P

Window

Length

Beam Width

Sample Volume

…

vv

Prof. Alison Noble

Measurements of tissue strain .. in-vivo

No absolute measure of length Measure changes at different strains Correlation of successive traces

Displacement from strain (induced by temperature change in this case)

Strain estimation

Ultrasound image Strain estimation

(from embedded heat source)

Based on coherent (r.f.) ultrasound data

Strain imaging – pilot study results

Fibroadenoma

Blue=high strain “ok”

Red =low strain “suspect”

DCIS

CancerCyst

Prof. Alison Noble

Tendon elastography

Revell et al, IEEE Trans Medical Imaging, 24 6 2006http://www.cs.bris.ac.uk/Research/Digitalmedia/cve/invivo.html

Uses B-mode image; tracks speckle pattern

BUT ..

Inverse problem (local strain to elastic constants) very hard to solve

Effect of surrounding tissue Orientation – limited number of

ultrasound windows

Shear measurements

Generate a low frequency shear wave Through differential movement Through interference pattern from two

transducers From ‘pushing pulse’

Watch propagation of wave with hgih frequency ultrasound

Shear measurements on musclesDifferential movement

Hoyt et al, 2008

Muscle Shear modulus (relaxed) Shear modulus (contracted)Rectus femoris 5.87kPa 11.17kPaBiceps brachii 6.09 8.42

ARFI (Acoustic Radiation Force) imaging

‘Pushing pulse’ acting locally – can be high frequency for good focal volume control. Longitudinal wave

Excites shear waveHigh speed image acquisition to capture shear velocity

Adapted from Melodelima et al,Ultrasound in Medicine & Biology

Volume 32, Issue 3, March 2006, Pages 387-396 ‘pushing pulse’

Tissue

Shearwave generation

With thanks to Chris Haw, Alison Noble

Conclusions

Ex-vivo Holding tissue – end effects? Artificial loading conditions Effect of neighbouring structure

In-vivo Quantitative shear measurements Displays of compression Possibility of measuring under real loading Limitation of viewing windows