PRS Winter 2019/20 Christopher Syben - Perspective Distorted MR-Projections
Channel: Andreas Maier
Date: 2020-03-05

In this talk, we derive a neural network architecture based on an analytical formulation of the parallel-to-cone beam conversion problem following the concept of precision learning. The network allows learning the unknown operators in this conversion in a data-driven manner avoiding interpolation and potential loss of resolution. The 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 the 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.

Toggle slide and presenter view: https://www.video.uni-erlangen.de/clip/id/12844
Fan-beam rebinning: https://link.springer.com/chapter/10.1007/978-3-030-12939-2_35
Pyro-NN: https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.13753
Known operator paper: https://www.nature.com/articles/s42256-019-0077-5
Pyro-NN code: https://github.com/csyben/PYRO-NN
Example code capsule: https://doi.org/10.24433/CO.8086142.v2