Name
Title | ||
|---|---|---|
Deriving Neural Network Architectures using Precision Learning: Parallel-to-fan beam Conversion. |
Abstract | ||
|---|---|---|
In this paper, we derive a neural network architecture based on an analytical formulation of the parallel-to-fan beam conversion problem following the concept of precision learning. The network allows to learn the unknown operators in this conversion in a data-driven manner avoiding interpolation and potential loss of resolution. Integration of known operators results in a small number of trainable parameters that can be estimated from synthetic data only. The concept is evaluated in the context of Hybrid MRI/X-ray imaging where transformation of the parallel-beam MRI projections to fan-beam X-ray projections is required. The proposed method is compared to a traditional rebinning method. The results demonstrate that the proposed method is superior to ray-by-ray interpolation and is able to deliver sharper images using the same amount of parallel-beam input projections which is crucial for interventional applications. We believe that this approach forms a basis for further work uniting deep learning, signal processing, physics, and traditional pattern recognition. |
Year | Venue | DocType |
|---|---|---|
2018 | GCPR | Conference |
Volume | Citations | PageRank |
abs/1807.03057 | 1 | 0.35 |
References | Authors | |
7 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Syben | 1 | 21 | 6.40 |
| Bernhard Stimpel | 2 | 3 | 1.76 |
| Jonathan Lommen | 3 | 1 | 0.35 |
| Tobias Würfl | 4 | 52 | 10.53 |
| a dorfler | 5 | 8 | 1.27 |
| Andreas K. Maier | 6 | 560 | 178.76 |