Gómez Fernández, Antonio Jesús; (2018) Transurethral shear wave elastography for prostate cancer. Doctoral thesis (Ph.D), UCL (University College London).

Preview

Text Gomez Fernandez_10050416_thesis.pdf Download (21MB) | Preview

Abstract

Prostate cancer remains a major healthcare issue. Limitations in current diagnosis and treatment monitoring techniques imply that there is still a need for improvements. The efficacy of prostate cancer diagnosis is still low. High intensity focused ultrasound ablation is an emerging treatment modality, which enables the noninvasive ablation of pathogenic tissue. Successful focal ablation treatment of prostate cancer is critically dependent on accurate diagnostic means and would be greatly benefited by a monitoring system. While magnetic resonance imaging remains the gold standard for prostate imaging, its wider implementation remains prohibitively expensive. Conventional ultrasound is currently limited to guiding biopsy. Elastography techniques are emerging as a promising imaging method, as cancer nodules are usually stiffer than adjacent healthy tissue, and even stiffer in the case of thermally ablated tissue. In this thesis, a novel transurethral elastography approach is proposed for the diagnosis of prostate cancer and its focal ablation monitoring, based on the transmission and detection of shear waves through the urethral wall. A viscoelastic wave propagation model is developed, using a finite difference time domain technique and based on a Kelvin-Voigt fractional derivative constitutive law. Validation of the model is achieved by high-speed camera tests carried out on translucent tissue-mimicking media. A Reverse Time Migration and a Genetic Algorithm techniques are proposed for reconstructing the parameters of the stiff lesion. A comparative study of the two techniques is presented. The Reverse Time Migration method finds the stiff lesion area in short computational time. The Genetic Algorithm provides full reconstruction of the location, size and stiffness of the lesion, however the computation time is much longer. A combination of both techniques achieves improved results by combining the speed of the Reverse Time Migration and the full reconstruction capacity of the Genetic Algorithm. Preliminary results support the feasibility of the method and encourage further investigation.

Download activity - last month

Export as XMLJSONCSV

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item