High Intensity Focused Ultrasound (HIFU) is a new cancer thermal therapy method which has achieved encouraging results in clinics recently. However, the lack of a temperature monitoring makes it hard to apply widely, safely and efficiently. Conventional ultrasonic temperature estimation based on echo strain suffers from artifacts caused by signal distortion over time, leading to poor estimation and visualization of the 2D temperature map. This thesis presents a novel model-based stochastic framework for ultrasonic temperature estimation, which combines the temperature information from the ultrasound images and a theoretical model of the heat diffusion. Consequently the temperature estimation is more consistent over time and its visualisation is improved. There are 3 main contributions of this thesis related to: improving the conventional echo strain method to estimate temperature, developing and applying approximate heat models to model temperature, and finally combining the estimation and the models. First in the echo strain based temperature estimation, a robust displacement estimator is first introduced to remove displacement outliers caused by the signal distortion over time due to the thermo-acoustic lens effect. To transfer the echo strain to temperature more accurately, an experimental method is designed to model their relationship using polynomials. Experimental results on a gelatine phantom show that the accuracy of the temperature estimation is of the order of 0.1 ◦C. This is better than results reported previously of 0.5 ◦C in a rubber phantom. Second in the temperature modelling, heat models are derived approximately as Gaussian functions which are mathematically simple. Simulated results demonstrate that the approximate heat models are reasonable. The simulated temperature result is analytical and hence computed in much less than 1 second, while the conventional simulation of using finite element methods requires about 25 minutes under the same conditions. Finally, combining the estimation and the heat models is the main contribution of this thesis. A 2D spatial adaptive Kalman filter with the predictive step defined by the shape model from the heat models is applied to the temperature map estimated from ultrasound images. It is shown that use of the temperature shape model enables more reliable temperature estimation in the presence of distorted or blurred strain measurements which are typically found in practice. The experimental results on in-vitro bovine liver show that the visualisation on the temperature map over time is more consistent and the iso-temperature contours are clearly visualised.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:494315 |
| Date | January 2008 |
| Creators | Ye, Guoliang |
| Contributors | Smith, Penny Probert ; Noble, Alison |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:6f4c4f84-3ca6-46f2-a895-ab0aa3d9af51 |
Page generated in 0.012 seconds