Transcript Proof of Concept Studies for Surface Based Mechanical

First experiments in
surface-based mechanical
property reconstruction of
gelatine phantoms
A. Peters, S. Wortmann, R. Elliott, M. Staiger,
J.G. Chase, E.E.W. Van Houten
Introduction
The DIET System
 Digital Image-based ElastoTomography (DIET) aims to be a lowcost alternative to current breast
cancer screening modalities
 Based on elastographic principles and
low-cost digital imaging techniques
 Four major steps in the DIET system
Actuate  Capture  Process  Reconstruct
 Simulation studies undertaken have
proven the concept of surface-based
mechanical property reconstruction[1]
[1] Peters et. al, JSME Int. Journal, (2004)
Methods
Phantom Studies
 Cylindrical tissue-approximating
gelatine phantoms
 Actuation achieved using dSPACETM,
laser interferometer, linear voice-coil
actuator with amplifier
 Motion captured using two consumerlevel digital cameras
 Manually-applied dots on tracked on
phantom surface
 Real motion approximated with a
least-squares fitted ellipsoid
Methods
FE Simulation
 Finite Element (FE) model of
cylinder created and meshed
 Actuated with same constraints
as real gelatine phantom
 Sparse parallel direct matrix
inversion and solution
performed with MUMPS[2] and
Goto BLAS[3]
Projecting a measured motion
point  back to the surface of
a 3D mesh to allow motion
comparison
[2] Amestoy et. al, Parallel Computing, (2005) [3] http://www.tacc.utexas.edu/resources/software/
Results
Simulated Motion
 Forward FE simulation performed at small intervals over a
range of homogeneous stiffness values
 Testing showed 22k node mesh
solutions were converged at 10kPa
and above
Sample displacement
solutions at a range of
stiffness values
Results
Motion Error Sweep
Results
Direct Comparison
 Qualitative comparison made between actual motion
and simulated phantom motion at 27kPa
MEASURED
SIMULATED
27kPa
Homogeneous gelatine phantom stiffness
successfully identified using steady-state
motion measurements and a FE model
Conclusions
Current Challenges
 Damping and phase
 Material non-linearity
 More advanced reconstruction
 Multiple parameters
 Gradient-descent
 Genetic algorithm/simulated annealing
 Tighter integration of motion capture
and processing
 Acknowledgements
 PhD supervisors
 Data collection
 Jérôme Rouzé & Arnaud Milsant
 Edouard Ravini & Fabrice Jandet