Name
Abstract | ||
|---|---|---|
Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework using a deep learning-based prior. We utilized the denoising convolutional neural network (DnCNN) method and trained the network using full-dose images as the ground truth and low dose images reconstructed from downsampled data by Poisson thinning as i... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMI.2018.2832613 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Noise level,Image reconstruction,Training,Machine learning,Noise reduction,Computed tomography,Image quality | Noise reduction,Iterative reconstruction,Computer vision,Pattern recognition,Convolutional neural network,Image quality,Ground truth,Artificial intelligence,Test data,Deep learning,Standard deviation,Mathematics | Journal |
Volume | Issue | ISSN |
37 | 6 | 0278-0062 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyung Sang Kim | 1 | 22 | 6.56 |
| Dufan Wu | 2 | 16 | 3.49 |
| Kuang Gong | 3 | 23 | 5.10 |
| Joyita Dutta | 4 | 12 | 4.36 |
| Jong Hoon Kim | 5 | 1 | 0.35 |
| Y. D. Son | 6 | 15 | 2.03 |
| Hang-Keun Kim | 7 | 5 | 1.77 |
| Georges El Fakhri | 8 | 67 | 18.03 |
| Quanzheng Li | 9 | 181 | 32.36 |