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README.md
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<div align="center">
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# PPCNet Dataset
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##
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[](LICENSE)
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[]()
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<div align="center">
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| Split | Patients | Usage |
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|:-----:|:--------:|:-----:|
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| Train | 829 | Model training |
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| Test | 105 | Final evaluation |
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| **Total** | **1,037** | |
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---
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## 🔬 DRR Generation
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DRRs are generated using **Plastimatch's ray-casting algorithm** with the following parameters:
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| Parameter | Value |
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| Source-to-Axis Distance (SAD) | 1,000 mm |
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| Bone Enhancement | 2.5× for HU > 300 |
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| Post-processing | CLAHE normalisation |
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Each DRR comes with a calibrated **3×4 perspective projection matrix** encoding the exact source–detector geometry.
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<div align="center">
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# PPCNet Dataset
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## Biplanar DRRs + Projection Matrices + Ground-Truth Point Clouds
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[](LICENSE)
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[]()
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Fixed split in `dataset_split.json` (seed=42):
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| Split | Patients | Usage |
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|:-----:|:--------:|:-----:|
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| Train | 829 | Model training |
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| Test | 105 | Final evaluation |
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| **Total** | **1,037** | |
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---
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## 🔬 DRR Generation
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DRRs are generated using **Plastimatch's ray-casting algorithm** with the following parameters:
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| Parameter | Value |
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|:---------:|:-----:|
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| Source-to-Axis Distance (SAD) | 1,000 mm |
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| Bone Enhancement | 2.5× for HU > 300 |
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| Post-processing | CLAHE normalisation |
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Each DRR comes with a calibrated **3×4 perspective projection matrix** encoding the exact source–detector geometry.
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---
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