Name
Title | ||
|---|---|---|
Deep learning-based solvability of underdetermined inverse problems in medical imaging |
Abstract | ||
|---|---|---|
•Explain about learning the causal relationship regarding the structure of the training data suitable for deep learning, to solve highly underdetermined problems.•Present a particular low-dimensional solution model to highlight the advantage of deep learning methods over conventional methods Analyze whether deep learning methods can learn the desired reconstruction map from training data in the three models (undersampled MRI, sparse-view CT, interior tomography).•Analyze the nonlinearity structure of underdetermined linear systems and conditions of learning (called M-RIP condition). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.media.2021.101967 | Medical Image Analysis |
Keywords | DocType | Volume |
Underdetermined linear inverse problem,Deep learning,Medical imaging,Magnetic resonance imaging,Computed tomography | Journal | 69 |
ISSN | Citations | PageRank |
1361-8415 | 1 | 0.47 |
References | Authors | |
43 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyun Chang Min | 1 | 1 | 0.47 |
| Baek Seong Hyeon | 2 | 1 | 0.47 |
| Lee Mingyu | 3 | 1 | 0.47 |
| Lee Sung Min | 4 | 1 | 0.47 |
| Jin Keun Seo | 5 | 376 | 58.65 |