Technical Note: PYRO-NN: Python reconstruction operators in neural networks

Syben-Leisner C, Michen M, Stimpel B, Seitz S, Ploner S, Maier A (2019)

Publication Type: Journal article, Original article

Publication year: 2019


Book Volume: 46

Pages Range: 5110-5115

Journal Issue: 11


DOI: 10.1002/mp.13753

Open Access Link:


Purpose: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches use workarounds for mathematically unambiguously solvable problems. Methods: PYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan-, and cone-beam projectors, and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high-level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems. Results: The framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high-level Python API allows a simple use of the layers as known from Tensorflow. All algorithms and tools are referenced to a scientific publication and are compared to existing non-deep learning reconstruction frameworks. To demonstrate the capabilities of the layers, the framework comes with baseline experiments, which are described in the supplementary material. The framework is available as open-source software under the Apache 2.0 licence at Conclusions: PYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step toward reproducible research and give the medical physics community a toolkit to elevate medical image reconstruction with new deep learning techniques.

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How to cite


Syben-Leisner, C., Michen, M., Stimpel, B., Seitz, S., Ploner, S., & Maier, A. (2019). Technical Note: PYRO-NN: Python reconstruction operators in neural networks. Medical Physics, 46(11), 5110-5115.


Syben-Leisner, Christopher, et al. "Technical Note: PYRO-NN: Python reconstruction operators in neural networks." Medical Physics 46.11 (2019): 5110-5115.

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