Name
Abstract | ||
|---|---|---|
The patch manifold of a natural image has a low dimensional structure and accommodates rich structural information. Inspired by the recent work of the low-dimensional manifold model (LDMM), we apply the LDMM for regularizing X-ray computed tomography (CT) image reconstruction. This proposed method recovers detailed structural information of images, significantly enhancing spatial and contrast resolution of CT images. Both numerically simulated data and clinically experimental data are used to evaluate the proposed method. The comparative studies are also performed over the simultaneous algebraic reconstruction technique (SART) incorporated the total variation (TV) regularization to demonstrate the merits of the proposed method. Results indicate that the LDMM-based method enables a more accurate image reconstruction with high fidelity and contrast resolution. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Medical Physics | Iterative reconstruction,Manifold structure,Computer vision,Simultaneous Algebraic Reconstruction Technique,Contrast resolution,Curse of dimensionality,Regularization (mathematics),Artificial intelligence,Radon transform,Mathematics,Manifold |
DocType | Volume | Citations |
Journal | abs/1704.04825 | 0 |
PageRank | References | Authors |
0.34 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenxiang Cong | 1 | 71 | 14.65 |
| Ge Wang | 2 | 1000 | 142.51 |
| Qingsong Yang | 3 | 6 | 1.25 |
| Jiang Hsieh | 4 | 42 | 4.01 |
| Jia Li | 5 | 22 | 10.55 |
| Rongjie Lai | 6 | 239 | 19.84 |