Name
Title | ||
|---|---|---|
A High-Quality Photon-Counting Ct Technique Based On Weight Adaptive Total-Variation And Image-Spectral Tensor Factorization For Small Animals Imaging |
Abstract | ||
|---|---|---|
Photon-counting X-ray computed tomography (CT) has been attracting great attention in tissue characterization, material discrimination, and so on. The emitting X-ray energy spectrum cutting into several energy bins that can result in only a part of X-ray photons can be collected within each narrow bin. This can compromise the image quality. In this case, how to obtain high-quality tomography is a big challenge. In this study, to overcome these issues, we mainly focus on developing an advanced imaging software based on the latest photon-counting CT system (MARS scanner). Specifically, we first design a weight adaptive total variation (TV) using compressed sensing theory. Then, combining the weight adaptive TV and nonlocal low-rank tensor factorization to formulate a new weight adaptive total-variation and image-spectral tensor factorization (WATITF) model for high-quality imaging. Finally, the optimization model is performed to obtain its solution. The studies including the numerical and preclinical mice are performed to validate and evaluate its outperformance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TIM.2020.3026804 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
Keywords | DocType | Volume |
Imaging, photon-counting computed tomography (CT), tensor factorization, total variation (TV), small animals | Journal | 70 |
ISSN | Citations | PageRank |
0018-9456 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwen Wu | 1 | 5 | 2.54 |
| Dianlin Hu | 2 | 5 | 4.17 |
| Kang An | 3 | 9 | 3.45 |
| Shaoyu Wang | 4 | 0 | 0.68 |
| Fulin Luo | 5 | 34 | 5.85 |