The easiest way to start is through the PorPy following examples.
| Example File | Description |
|---|---|
| 1_basic_tutorial.ipynb | Demonstrates the main functionalities of PortPy (e.g., Access data, create an IMRT plan, visualize) |
| vmat_scp_tutorial.ipynb | Creates a VMAT plan using sequential convex programming |
| vmat_scp_dose_prediction.ipynb | Predicts 3D dose distribution using deep learning and converts it into a deliverable VMAT plan |
| 3d_slicer_integration.ipynb | Creates an IMRT plan and visualizes it in 3D-Slicer |
| imrt_tps_import.ipynb | 1. Outputs IMRT plan in DICOM RT format and imports it into TPS. 2. Outputs IMRT plan optimal fluence in an Eclipse-compatable format and imports it into Eclipse |
| vmat_tps_import.ipynb | Outputs VMAT plan in DICOM RT format and imports it into TPS |
| imrt_dose_prediction.ipynb | Predicts 3D dose distribution using deep learning and converts it into a deliverable IMRT plan |
| vmat_global_optimal.ipynb | Finds a globally optimal VMAT plan |
| beam_orientation_global_optimal.ipynb | Finds globally optimal beam angles for IMRT |
| dvh_constraint_global_optimal.ipynb | Finds a globally optimal plan meeting Dose Volume Histogram (DVH) constraints |
| structure_operations.ipynb | Creates new structures by expanding/shrinking the existing ones or using boolean operations |
| inf_matrix_down_sampling.pynb | Down-samples beamlets and/or voxels for computational efficiency |
| inf_matrix_sparsification.ipynb | Sparsifies (i.e., truncates) the influence matrix for computational efficiency |