Bibliographic Details
| Title: |
Effect of a Patient-Specific Structural Prior Mask on Electrical Impedance Tomography Image Reconstructions |
| Authors: |
Rongqing Chen, Sabine Krueger-Ziolek, Alberto Battistel, Stefan J. Rupitsch, Knut Moeller |
| Source: |
Sensors, Vol 23, Iss 9, p 4551 (2023) |
| Publisher Information: |
MDPI AG, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Chemical technology |
| Subject Terms: |
electrical impedance tomography, structural prior, image reconstruction, inverse problem, Chemical technology, TP1-1185 |
| More Details: |
Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1424-8220 |
| Relation: |
https://www.mdpi.com/1424-8220/23/9/4551; https://doaj.org/toc/1424-8220 |
| DOI: |
10.3390/s23094551 |
| Access URL: |
https://doaj.org/article/e4224ba7357d475fa50873149d47af44 |
| Accession Number: |
edsdoj.4224ba7357d475fa50873149d47af44 |
| Database: |
Directory of Open Access Journals |
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