Name
Title | ||
|---|---|---|
B-spline based sharp feature preserving shape reconstruction approach for electrical impedance tomography. |
Abstract | ||
|---|---|---|
This paper presents a B-spline based shape reconstruction approach for electrical impedance tomography (EIT). In the proposed approach, the conductivity distribution to be reconstructed is assumed to be piecewise constant. The geometry of the inclusions is parameterized using B-spline curves, and the EIT forward solver is modified as a set of control points representing the inclusions' boundary to the data on the domain boundary. The low order representation decreases the computational demand and reduces the ill-posedness of the EIT reconstruction problem. The performance of the proposed B-spline based approach is tested with simulations which demonstrate the most popular biomedical application of EIT: lung imaging. The approach is experimentally validated using water tank data. In addition, robustness studies of the proposed approach considering varying initial guesses, inaccurately known contact impedances, differing numbers of control points, and degree of B-spline are performed. The simulation and experimental results show that the B-spline based approach offers improvements in image quality in comparison to the traditional Fourier series based reconstruction approach, as measured by quantitative metrics such as relative size coverage ratio and relative contrast. Inasmuch, the proposed approach is demonstrated to offer clear improvement in the ability to preserve the sharp properties of the inclusions to be imaged. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2019.2905245 | IEEE transactions on medical imaging |
Keywords | Field | DocType |
Tomography,Splines (mathematics),Shape,Image reconstruction,Conductivity,Fourier series,Numerical models | Iterative reconstruction,B-spline,Computer vision,Image quality,Algorithm,Tomography,Robustness (computer science),Fourier series,Artificial intelligence,Mathematics,Piecewise,Electrical impedance tomography | Journal |
Volume | Issue | ISSN |
38 | 11 | 1558-254X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Liu | 1 | 12 | 5.34 |
| Danping Gu | 2 | 4 | 2.10 |
| Danny Smyl | 3 | 2 | 2.19 |
| jiansong deng | 4 | 458 | 38.59 |
| Jiangfeng Du | 5 | 11 | 6.45 |