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 dSPACE TM, 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]

 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 SIMULATED 27kPa

 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