Name
Title | ||
|---|---|---|
Learning to Reconstruct Computed Tomography (CT) Images Directly from Sinogram Data under A Variety of Data Acquisition Conditions. |
Abstract | ||
|---|---|---|
Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided that the acquired data satisfy the data sufficiency condition as well as other conditions regarding the view angle sampling interval and the severity ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2019.2910760 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Image reconstruction,Computed tomography,Kernel,Convolution,Training,Deep learning | Kernel (linear algebra),Iterative reconstruction,Computer vision,Line integral,Data truncation,Convolution,Data acquisition,Artificial intelligence,Pixel,Compressed sensing,Mathematics | Journal |
Volume | Issue | ISSN |
38 | 10 | 0278-0062 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinsheng Li | 1 | 2 | 2.06 |
| Ke Li | 2 | 1 | 0.69 |
| Chengzhu Zhang | 3 | 1 | 0.69 |
| Juan Montoya | 4 | 1 | 0.35 |
| Guang-Hong Chen | 5 | 15 | 2.88 |