Shape based reconstruction of experimental data in 3D electrical capacitance tomography

Abstract

Electrical capacitance tomography (ECT) is a non-invasive and non-destructive imaging technique that uses electrical capacitance measurement at the periphery of an object. The image reconstruction problem in ECT is an ill-posed inverse problem. This paper presents a level set based shape reconstruction method applied to 3D ECT using experimental data. The finite element models have been implemented based on a 32 electrode ECT system to formulate the forward problem. Development of the level set technique enables detection of smaller inclusions and improves the accuracy of boundary shapes of inclusions. The paper uses a shape based method rather than traditional image based methods. The shape-based approach offers several advantages compared with more traditional voxel-based approaches. The incorporation of an intrinsic regularization in the form of a-priori assumptions, regarding the general anatomical structures present in the medium, reduces the dimensionality of the inverse problem and thereby stabilizes the reconstruction. The level set strategy (which is an implicit representation of the shapes) can handle the topological during this reconstruction process. Additionally in this paper a new two-stage level set method has been developed, which shows significant improvement compared with the traditional level set reconstruction algorithm.