Title
Shape-Driven EIT Reconstruction Using Fourier Representations
Abstract
Shape-driven approaches have been proposed as an effective strategy for the electrical impedance tomography (EIT) reconstruction problem in recent years. In order to augment the shape-driven approaches, we propose a new method that transforms the shape to be reconstructed as basic primitives directly modeled by using Fourier representations. To allow automatic topological changes between the basic primitives and surrounding objects simultaneously, Boolean operations are employed. The Boolean operations with direct representation of primitives can be utilized for dimensionality and ill-posedness reduction, enabling feasible shape and topology optimization with shape-driven approaches. As a proof of principle, we leverage the proposed method for two dimensional shape reconstruction in EIT with various conductivity distributions. We demonstrate that our method is able to improve EIT reconstructions by enabling accurate shape and topology optimization.
Year
DOI
Venue
2021
10.1109/TMI.2020.3030024
IEEE Transactions on Medical Imaging
Keywords
DocType
Volume
Algorithms,Electric Impedance,Image Processing, Computer-Assisted,Tomography,Tomography, X-Ray Computed
Journal
40
Issue
ISSN
Citations 
2
0278-0062
1
PageRank 
References 
Authors
0.35
0
6
Name
Order
Citations
PageRank
Dong Liu1125.34
Danping Gu242.10
Danny Smyl332.08
Anil Kumar Khambampati483.09
jiansong deng545838.59
Jiangfeng Du6116.45