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

Digital Image-based Elasto-Tomography (DIET) aims to be a low-cost alternative to current breast cancer screening modalities

Based on elastographic principles and low-cost digital imaging techniques

Introduction

The DIET System

[1] Peters et. al, JSME Int. Journal, (2004)

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]

Cylindrical tissue-approximating gelatine phantoms

Actuation achieved using dSPACETM, laser interferometer, linear voice-coil actuator with amplifier

Methods

Phantom Studies

Motion captured using two consumer-level digital cameras

Manually-applied dots on tracked on phantom surface

Real motion approximated with a least-squares fitted ellipsoid

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]

Methods

FE Simulation

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/

Forward FE simulation performed at small intervals over a range of homogeneous stiffness values

Results

Simulated Motion

Sample displacement solutions at a range of

stiffness values

Testing showed 22k node mesh solutions were converged at 10kPa and above

Results

Motion Error Sweep

Qualitative comparison made between actual motion and simulated phantom motion at 27kPa

Results

Direct Comparison

Homogeneous gelatine phantom stiffness successfully identified using steady-state

motion measurements and a FE model

MEASURED SIMULATED27kPa

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

Conclusions

Current Challenges