Name
Abstract | ||
|---|---|---|
Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a n... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMI.2018.2823756 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Convolution,Computed tomography,Noise reduction,Image reconstruction,X-ray imaging,Machine learning,Neural networks | Noise reduction,Iterative reconstruction,Computer vision,Residual,Pattern recognition,Convolution,Convolutional neural network,Artificial intelligence,Deep learning,Artificial neural network,Mathematics,Wavelet | Journal |
Volume | Issue | ISSN |
37 | 6 | 0278-0062 |
Citations | PageRank | References |
16 | 0.64 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eunhee Kang | 1 | 22 | 1.80 |
| Won Chang | 2 | 16 | 0.64 |
| Jae Jun Yoo | 3 | 157 | 9.48 |
| Jong Chul Ye | 4 | 715 | 79.99 |