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@@ -48,4 +48,4 @@ The easiest way to start is through the PorPy following Jupiter Notebook example
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|[imrt_dose_prediction.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/imrt_dose_prediction.ipynb)| Predicts 3D dose distribution using deep learning and converts it into a deliverable IMRT plan |
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|[vmat_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_global_optimal.ipynb)| Finds a globally optimal VMAT plan |
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|[beam_orientation_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/beam_orientation_global_optimal.ipynb)| Finds globally optimal beam angles for IMRT |
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|[dvh_constraint_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/dvh_constraint_global_optimal.ipynb)| Finds a globally optimal plan meeting Dose Volume Histogram (DVH) constraints |
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|[dvh_constraint_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/dvh_constraint_global_optimal.ipynb)| Finds a globally optimal plan meeting Dose Volume Histogram (DVH) constraints |
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