| Paper ID | CI-2.3 |
| Paper Title |
LEARNING SPARSIFYING TRANSFORMS FOR IMAGE RECONSTRUCTION IN ELECTRICAL IMPEDANCE TOMOGRAPHY |
| Authors |
Kaiyi Yang, Narong Borijindargoon, Boon Poh Ng, Nanyang Technological University, Singapore; Saiprasad Ravishankar, Michigan State University, Singapore; Bihan Wen, Nanyang Technological University, Singapore |
| Session | CI-2: Computational Imaging for Inverse Problems |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Computational Imaging: [CIF] Computational Image Formation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Electrical Impedance Tomography (EIT) is a fast and non-invasive imaging technology that reconstructs the internal electrical properties of a subject. However, its functionality is limited by low spatial resolution arising from an ill-posed and ill-conditioned inverse problem. Several sparsity-promoting regularization methods have been applied to improve the quality of EIT image reconstruction, including various L0 and L1-based analytical models (TV, TwIST, etc.), and a patch-based sparse representation via a learned dictionary (using the K-SVD algorithm), dubbed CS-EIT. To further exploit the potential of compressed sensing in Electrical Impedance Tomography, this paper incorporates the recent novel method of transform learning for EIT image reconstruction. We propose a blind compressed sensing algorithm, dubbed TL-EIT, which simultaneously optimizes the sparsifying transform and updates the reconstructed image. We demonstrate using both synthetic and in vivo data that the proposed TL-EIT is more effective than other sparsity-based algorithms for reconstructing high-quality EIT images. In addition, TL-EIT also accelerates the reconstruction process in comparison to other learning-based algorithms like CS-EIT. |
