Transcript Biomeasurements on tissue and tissue substitutes

Ultrasound measurements on
tissue
Penny Probert Smith
Institute of Biomedical Engineering
Department of Engineering Science
University of Oxford
(also Professors Alison Noble, Harvey Burd;
Dr Fares Mayia, Russ Shannon
Chris Haw, Emma Crowley, Jon Dennis)
Mechanical model of tissue
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Viscoelastic properties
Kelvin or Voigt
model
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Non-linear
Almost incompressible
G,E<<K
E
G
2(1   )
E
K
3(1  2 )
Maxwell Model
  0.5; E  3G
Why ultrasound?
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Possibility of in-vivo measurements
Compared with MRI:

Cheaper
Faster (so possibility of measurements
during muscle action)
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BUT LESS ACCURATE
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Propagation of ultrasound in tissue
Relevant material properties
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Wave propagation velocity depends mainly on
elasticity, density:
c
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U

m / sec
Independent of frequency
Attenuation (longitudinal and transverse waves)
depends on shear viscosity
 Also frequency dependent
 BUT also affected by scattering
Multimode operation
Spectral response
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Stokes-Navier eqn inherently non-linear; normally
make linear assumption
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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
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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
Mixture rule: relates volume fraction, x , to changes in
material properties
e.g. velocity
1 x (1  x)
 2 2
2
c c1
c2
Experimental rig
sample
TX
RX
velocity 
Lsample  Lcalibrate
time of flight
Correlation with tissue composition

High repeatability in measurement system
Good repeatability and correlation with elastic properties
in phantom (normally a gel) or water
Speed of sound

Height of water column
Speed of sound
But not so good in tissue ..
Fat content
(from chemical analysis)
Causes of error in samples
Structure
Region of muscle
Region of fat (myosepta)
Shape and orientation
Loading: 0.2% compressive strain - but hard to judge 0% strain
Specimen preparation: Degassing – air bubbles have huge effect
Velocities in other tissues
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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
Oscilloscope
Low frequency vibration excites shear wave
Time of flight measurement gives velocity
Pressure from motor?
Time dependent effects?
For eye lens ..
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High attenuation at ultrasound frequencies
Mechanical (or low frequency) wave excitation
Results compare well
with other estimates
(spinning lens,
deformation)
In-vivo methods
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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
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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
Sample Volume
…
P=
P0+P
v
Window Length
P = P0
v
Beam Width
Prof. Alison Noble
Measurements of tissue strain
.. in-vivo
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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
(from embedded heat source)
Ultrasound image
Strain estimation
Based on coherent (r.f.) ultrasound data
Strain imaging – pilot study results
Blue=high strain “ok”
Red =low strain “suspect”
Cancer
Cyst
Fibroadenoma
Prof. Alison Noble
DCIS
Tendon elastography
Uses B-mode image;
tracks speckle pattern
Revell et al, IEEE Trans Medical
Imaging, 24 6 2006
http://www.cs.bris.ac.uk/Research
/Digitalmedia/cve/invivo.html
BUT ..
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Inverse problem (local strain to
elastic constants) very hard to solve
Effect of surrounding tissue
Orientation – limited number of
ultrasound windows
Shear measurements
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Generate a low frequency shear
wave
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Through differential movement
Through interference pattern from two
transducers
From ‘pushing pulse’
Watch propagation of wave with
hgih frequency ultrasound
Shear measurements on muscles
Differential movement
Muscle
Shear modulus (relaxed) Shear modulus (contracted)
Rectus femoris
5.87kPa
11.17kPa
Biceps brachii
6.09
8.42
Hoyt et al, 2008
ARFI (Acoustic Radiation Force)
imaging
‘Pushing pulse’ acting locally – can be high
frequency for good focal volume control.
Longitudinal wave
Excites shear wave
High speed image
Tissue
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’
Shearwave generation
With thanks to Chris Haw, Alison Noble
Conclusions
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Ex-vivo
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Holding tissue – end effects?
Artificial loading conditions
Effect of neighbouring structure
In-vivo
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Quantitative shear measurements
Displays of compression
Possibility of measuring under real loading
Limitation of viewing windows