Name
Title | ||
|---|---|---|
A new Mumford-Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction. |
Abstract | ||
|---|---|---|
Total variation (TV) minimization for the sparse-view x-ray computer tomography (CT) reconstruction has been widely explored to reduce radiation dose. However, owing to the piecewise constant assumption, CT images reconstructed by TV minimization-based algorithms often suffer from image edge over-smoothness. To address this issue, an improved sparse-view CT reconstruction algorithm is proposed in this work by incorporating a Mumford–Shah total variation (MSTV) model into the penalized weighted least-squares (PWLS) scheme, termed as “PWLS-MSTV”. The MSTV model is derived by coupling TV minimization and Mumford–Shah segmentation, to achieve good edge-preserving performance during image denoising. To evaluate the performance of the present PWLS-MSTV algorithm, both qualitative and quantitative studies were conducted by using a digital XCAT phantom and a physical phantom. Experimental results show that the present PWLS-MSTV algorithm has noticeable gains over the existing algorithms in terms of noise reduction, contrast-to-ratio measure and edge-preservation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2018.01.037 | Neurocomputing |
Keywords | Field | DocType |
Computer tomography,Mumford–Shah total variation,Sparse-view,Image reconstruction | Noise reduction,Iterative reconstruction,Pattern recognition,Segmentation,Imaging phantom,Algorithm,Tomography,Minification,Reconstruction algorithm,Artificial intelligence,Mathematics,Piecewise | Journal |
Volume | ISSN | Citations |
285 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Chen | 1 | 25 | 5.19 |
| Zhaoying Bian | 2 | 42 | 8.56 |
| Xiaohui Zhou | 3 | 27 | 9.21 |
| Wen-Sheng Chen | 4 | 391 | 39.97 |
| Jianhua Ma | 5 | 123 | 23.36 |
| Zhengrong Liang | 6 | 684 | 93.03 |