Quantitative imaging complements structural imaging by providing quantitative estimations of subsurface material properties as opposed to the sizes, shapes and positions of scatterers available from structural methods. The ability to reconstruct material properties from a series of wave measurements is extremely valuable in a range of applications as it potentially allows diagnostic technology with superior sensitivity and selectivity. Breast cancer, for example, is stiffer and hence of higher sound velocity than the surrounding tissue, so reconstructing velocity from ultrasonic measurements could allow cancer detection. Using this concept, breast ultrasound tomography has the potential to significantly improve the cost, safety and reliability of breast cancer screening and diagnosis over mammography, the gold-standard. Key to unlocking this potential is the availability of an accurate, fast, robust and high-resolution algorithm to reconstruct wave velocity. This thesis introduces HARBUT, the Hybrid Algorithm for Robust Breast Ultrasound Tomography, a new imaging approach combining the complementary strengths of low resolution bent-ray tomography and high resolution diffraction tomography. HARBUT's theoretical foundation is explained and applied to simulated and experimental, in-vivo, breast ultrasound tomography data, confirming that it generates a step change in image quality over existing techniques, revealing lesions that would not be visible on a mammogram. This thesis also shows how, by combining data from many slices, the out-of-plane resolution can be significantly improved compared to treating each slice independently. HARBUT is applied to alternative problems including guided wave tomography, which aims to quantify the remaining wall thickness of a potentially corroded, inaccessible plate-like structure. Thickness estimates within 1mm for a 10mm nominal thickness plate were demonstrated for both simulated and experimental data. The thesis finally investigates HARBUT's performance with limited view configurations, and introduces VISCIT, the Virtual Image Space Component Iterative Technique, which accounts for the missing data, significantly improving the reconstructed image.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:556565 |
| Date | January 2012 |
| Creators | Huthwaite, Peter Edward |
| Contributors | Lowe, Michael ; Simonetti, Francesco |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10044/1/9765 |
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