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Purpose: Recently, several attempts were conducted to transfer deep learningto medical image reconstruction. An increasingly number of publications followthe concept of embedding the CT reconstruction as a known operator into aneural network. However, most of the approaches presented lack an efficient CTreconstruction framework fully integrated into deep learning environments. As aresult, many approaches are forced to use workarounds for mathematicallyunambiguously solvable problems. Methods: PYRO-NN is a generalized framework toembed known operators into the prevalent deep learning framework Tensorflow.The current status includes state-of-the-art parallel-, fan- and cone-beamprojectors and back-projectors accelerated with CUDA provided as Tensorflowlayers. On top, the framework provides a high level Python API to conduct FBPand iterative reconstruction experiments with data from real CT systems.Results: The framework provides all necessary algorithms and tools to designend-to-end neural network pipelines with integrated CT reconstructionalgorithms. The high level Python API allows a simple use of the layers asknown from Tensorflow. To demonstrate the capabilities of the layers, theframework comes with three baseline experiments showing a cone-beam short scanFDK reconstruction, a CT reconstruction filter learning setup, and a TVregularized iterative reconstruction. All algorithms and tools are referencedto a scientific publication and are compared to existing non deep learningreconstruction frameworks. The framework is available as open-source softwareat \url{https://github.com/csyben/PYRO-NN}. Conclusions: PYRO-NN comes with theprevalent deep learning framework Tensorflow and allows to setup end-to-endtrainable neural networks in the medical image reconstruction context. Webelieve that the framework will be a step towards reproducible research

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