AbstractMagnetic Induction Tomography (MIT) is a contactless and non-invasive method for imaging the passive electrical properties of objects. An MIT system employs an array of excitation coils in order to induce eddy currents within an object and then uses detection coils to measure the resulting magnetic field perturbations. An image of conductivity distribution within the object is reconstructed by iteratively solving a non-linear inverse problem.
As a relatively new imaging technique, MIT encounters several challenges both in terms of the formulation of the algorithms and the computational intensity needed for industrial and medical applications. This is because such real life models are often necessarily large and complex, and the computation is consequently highly time consuming. For instance, in the case of stroke detection and monitoring, which is one of the potential medical applications of MIT, it has been a major challenge to develop realistic computer brain models which allow the reconstruction of high resolution images within practical time frames.
Parallel implementation is an obvious solution for addressing such computational challenges. Therefore, the development of a fast and efficient 3D image reconstruction solver and its implementation in parallel platforms are very important, as this will lead to considerable improvements in this field and allow the use of MIT in both medical and industrial applications. This thesis investigates potential hardware architectures, efficient parallel algorithms and optimisation methods for MIT.
In this study, an efficient 3D iterative image reconstruction algorithm was developed using the reciprocity method and was shown to provide better absolute conductivity images of sample than existing work. Significant improvements in computation time were achieved by the parallel implementations of both forward and inverse model parts of the image reconstruction algorithm. These implementations were developed, tested and compared across many hardware platforms using various parallelisation
Progresses made in this study will invariably hasten future developments of MIT as a real-life and low-cost imaging modality with many potential applications in the medical and industrial arena.
|Date of Award||2 Mar 2009|
|Supervisor||Ali Roula (Supervisor) & Robert Williams (Supervisor)|
- Image processing