Digital Image Elasto-Tomography: Mechanical Property Reconstruction from Surface Measured Displacement Data
| dc.contributor.author | Peters, Ashton | en |
| dc.date.accessioned | 2009-09-02T21:14:46Z | |
| dc.date.available | 2009-09-02T21:14:46Z | |
| dc.date.issued | 2007 | en |
| dc.description.abstract | Interest in elastographic techniques for soft tissue imaging has grown as relevant research continues to indicate a correlation between tissue histology and mechanical stiffness. Digital Image Elasto-Tomography (DIET) presents a novel method for identifying cancerous lesions via a three-dimensional image of elastic properties. Stiffness reconstruction with DIET takes steady-state motion captured with a digital camera array as the input to an elastic property reconstruction algorithm, where finite element methods allow simulation of phantom motion at a range of internal stiffness distributions. The low cost and high image contrast achievable with a DIET system may be particularly suited to breast cancer screening, where traditional modalities such as mammography have issues with limited sensitivity and patient discomfort. Proof of concept studies performed on simulated data sets confirmed the potential of the DIET technique, leading to the development of an experimental apparatus for surface motion capture from a range of soft tissue approximating phantoms. Error studies performed on experimental data from these phantoms using a limited number of shape and modulus parameters indicated that accurate measurements of surface motion provide sufficient information to identify a stiffness distribution in both homogeneous and heterogeneous cases. The elastic reconstruction performed on simulated and experimental data considered both deterministic and stochastic algorithms, with a combination of the two approaches found to give the most accurate results, for a realistic increase in computational cost. The reconstruction algorithm developed has the ability to successfully resolve a hard spherical inclusion within a soft phantom, and in addition demonstrated promise in reconstructing the correct stiffness distribution when no inclusion is present. | en |
| dc.identifier.uri | http://hdl.handle.net/10092/2775 | |
| dc.identifier.uri | http://dx.doi.org/10.26021/2670 | |
| dc.language.iso | en | |
| dc.publisher | University of Canterbury. Department of Mechanical Engineering | en |
| dc.relation.isreferencedby | NZCU | en |
| dc.rights | Copyright Ashton Peters | en |
| dc.rights.uri | https://canterbury.libguides.com/rights/theses | en |
| dc.subject | elastography | en |
| dc.subject | finite element method | en |
| dc.subject | breast cancer | en |
| dc.subject | inverse problem | en |
| dc.subject | optimization | en |
| dc.subject | genetic algorithm | en |
| dc.subject | silicone | en |
| dc.title | Digital Image Elasto-Tomography: Mechanical Property Reconstruction from Surface Measured Displacement Data | en |
| dc.type | Theses / Dissertations | |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | University of Canterbury | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | |
| uc.bibnumber | 1054762 | en |
| uc.college | Faculty of Engineering | en |
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