Update README.md

README.md

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----
-license: apache-2.0
-language:
-- en
-size_categories:
-- 10B<n<100B
-configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: data_info.jsonl
----
+---
+license: apache-2.0
+language:
+- en
+size_categories:
+- 10B<n<100B
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data_info.jsonl
+---
+
+---
+license: apache-2.0
+language:
+- en
+size_categories:
+- 10B<n<100B
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data_info.jsonl
+---
+# PortPy: Planning and Optimization for Radiation Therapy
+
+## Data Overview
+
+<img src="https://github.com/PortPy-Project/PortPy/blob/master/images/PortPy%20Data.png" align="right" width="50%" height="40%">
+
+PortPy equips researchers with a robust benchmark patient dataset, sourced from the FDA-approved Eclipse commercial treatment planning system through its API. This dataset embodies all necessary elements for optimizing various machine configurations such as beam angles, aperture shapes, and leaf movements. It includes
+
+1. **Dose Influence Matrix (AKA dose deposition matrix, dij matrix):** The dose contribution of each beamlet to each voxel,
+2. **Beamlets/Voxels Details:** Detailed information about the position and size of beamlets/voxels,
+3. **Expert-Selected Benchmark Beams:** An expert clinical physicist has carefully selected benchmark beams, providing reference beams for comparison and benchmarking,
+4. **Benchmark IMRT Plan:** A benchmark IMRT plan generated using our in-house automated treatment planning system called ECHO ([YouTube Video](https://youtu.be/895M6j5KjPs), [Paper](https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.13572)). This plan serves as a benchmark for evaluating new treatment planning algorithms.
+5. **Benchmark Clinical Criteria:** A set of clinically relevant mean/max/DVH criteria for plan evaluation. 
+Currently, this set encompasses only the Lung 2Gy×30 protocol but will be expanded in the future to more protocols as well as TCP/NTCP evaluation functions.  
+
+Subsequently, create a directory titled './data' in the current project directory and transfer the downloaded 
+file into it. For example, ./data/Lung_Phantom_Patient_1. 
+We have adopted the widely-used JSON and HDF5 formats for data storage.
+[HDFViwer](https://www.hdfgroup.org/downloads/hdfview/) can be utilized to view the contents of the HDF5 files.  
+
+
+
+**Note:** Initially, we will utilize a lung dataset from [TCIA](https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics). The original DICOM CT images and structure sets are not included in the PortPy dataset and need to be directly downloaded from the TCIA. Users can fetch the **TCIA collection ID** and the **TCIA subject ID** for each PortPy patient using the *get_tcia_metadata()* method in PortPy and subsequently download the data from TCIA (see [imrt_tps_import](https://github.com/PortPy-Project/PortPy/blob/master/examples/imrt_tps_import.ipynb))
+
+## Data Fields
+
+Each beam is divided into small 2D beamlets/spots, and the patient’s body is divided into small 3D voxels. Eclipse is used to calculate the dose contribution of each beamlet to every voxel, resulting in a **dose influence matrix** (also called a dose deposition matrix or dij matrix). Relevant beamlet and voxel information (e.g., size, coordinates) is stored, as well as CT data (e.g., voxel Hounsfield Units, coordinates) and structure data (e.g., structure names, masks).
+
+The scripts adopt the data format, where:
+
+-   Light-weight metadata is stored in human-readable `.json` files.
+    
+-   Large datasets (e.g., dose influence matrices) are stored in `.h5` (HDF5) files.
+    
+
+A typical output folder structure for a patient might look like this:
+
+```
+│
+├── Beams/
+│   ├── Beam_0_MetaData.json
+│   ├── Beam_0_Data.h5
+│   ├── Beam_1_MetaData.json
+│   ├── Beam_1_Data.h5
+├── CT_Data.h5
+├── CT_MetaData.json
+└── StructureSet_MetaData.json
+└── StructureSet_Data.h5
+
+```
+#### Example JSON and HDF5 Files
+
+##### Beam_0_metadata.json
+Below is an example `.json` file for a beam. Notice how the larger data arrays (e.g., beamlets, influence matrices) point to external `.h5` files with specific tags. For instance, ``` "influenceMatrixSparse_File": "Beam_0_Data.h5/inf_matrix_sparse"```, means the influence matrix is stored in a file named  *Beam_0_Data.h5* under a tag named *inf_matrix_sparse*. 
+  
+```json
+{
+  "ID": 0,
+  "gantry_angle": 0,
+  "collimator_angle": 0,
+  "couch_angle": 0,
+  "iso_center": {
+    "x_mm": 119.2041,
+    "y_mm": 60.53891,
+    "z_mm": -9.122542
+  },
+  "beamlets": {
+    "id_File": "Beam_0_Data.h5/beamlets/id",
+    "width_mm_File": "Beam_0_Data.h5/beamlets/width_mm",
+    "height_mm_File": "Beam_0_Data.h5/beamlets/height_mm",
+    "position_x_mm_File": "Beam_0_Data.h5/beamlets/position_x_mm",
+    "position_y_mm_File": "Beam_0_Data.h5/beamlets/position_y_mm",
+    "MLC_leaf_idx_File": "Beam_0_Data.h5/beamlets/MLC_leaf_idx"
+  },
+  "jaw_position": {
+    "top_left_x_mm": -5,
+    "top_left_y_mm": 40,
+    "bottom_right_x_mm": 97.5,
+    "bottom_right_y_mm": -60
+  },
+  "influenceMatrixSparse_File": "Beam_0_Data.h5/inf_matrix_sparse",
+  "influenceMatrixFull_File": "Beam_0_Data.h5/inf_matrix_full",
+  "MLC_leaves_pos_y_mm_File": "Beam_0_Data.h5/MLC_leaves_pos_y_mm"
+}
+```
+
+##### Beam_0_Data.h5
+HDF5 (Hierarchical Data Format version 5) is a common and powerful format that is supported by most programming languages. It is designed to store and organize large amounts of complex data using a flexible, hierarchical structure, allowing efficient access, compression, and storage of multidimensional arrays. The following example shows the hierarchical data for a beam. [HDFViwer](https://www.hdfgroup.org/downloads/hdfview/) can be used to see through a .h5 file. 
+```
+Beam_0_Data.h5
+│
+├── beamlets/
+│   ├── id               (1D array of beamlet IDs)
+│   ├── width_mm         (1D array of beamlet widths in mm)
+│   ├── height_mm        (1D array of beamlet heights in mm)
+│   ├── position_x_mm    (1D array of x positions in mm)
+│   ├── position_y_mm    (1D array of y positions in mm)
+│   └── MLC_leaf_idx     (1D array of MLC leaf indices)
+│
+├── inf_matrix_sparse    (Sparse influence matrix)
+├── inf_matrix_full      (Full influence matrix)
+└── MLC_leaves_pos_y_mm (MLC leaves positions in mm in y direction)
+```
+
+## How to use it
+You can load the dataset with the following two lines of code.
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("PortPy-Project/PortPy_Datset", split="split_0")
+print(dataset)
+Dataset({
+    features: ['patient_id', 'ct_data', 'ct_metadata', 'structureset_data', 'structureset_metadata', 'beam_data_paths', 'beam_metadata_paths', 'optimization_voxels_data', 'optimization_voxels_metadata', 'planner_beams', 'rt_dose_echo_imrt', 'rt_plan_echo_imrt'],
+    num_rows: 50
+})
+
+# load ct metadata for 1st patient
+ct_metadata = load_dataset("json", data_files=dataset[0]['ct_metadata'])
+print(ct_metadata)
+
+DatasetDict({
+    train: Dataset({
+        features: ['origin_xyz_mm', 'resolution_xyz_mm', 'size_xyz_mm', 'direction', 'ct_hu_3d_File'],
+        num_rows: 1
+    })
+})
+
+# load ct metadata for 1st patient
+structureset_metadata = load_dataset("json", data_files=dataset[0]['structureset_metadata'])
+print(structureset_metadata)
+
+DatasetDict({
+    train: Dataset({
+        features: ['name', 'volume_cc', 'dicom_structure_name', 'fraction_of_vol_in_calc_box', 'structure_mask_3d_File'],
+        num_rows: 9
+    })
+})
+
+```
+
+# Team <a name="Team"></a>
+PortPy is a community project initiated at [Memorial Sloan Kettering Cancer Center](https://www.mskcc.org/) (MSK). It is currently developed and maintained by [Masoud Zarepisheh](https://masoudzp.github.io/) (Principal Investigator, zarepism@mskcc.org) and [Gourav Jhanwar](https://github.com/gourav3017) (Lead Developer, jhanwarg@mskcc.org). Other team members include: [Mojtaba Tefagh](https://www.ed.ac.uk/profile/mojtaba-tefagh) (Optimization/AI/ML expert from University of Edinburgh), [Linda Hong](https://www.mskcc.org/profile/linda-hong) (Medical Physicist from MSK), [Vicki Taasti](https://scholar.google.com/citations?user=PEPyvewAAAAJ&hl=en) (Proton Physicist from Aarhus University), and [Saad Nadeem](https://nadeemlab.org/) (AI/Imaging expert from MSK). 
+
+# License <a name="License"></a>
+PortPy code is distributed under **Apache 2.0 with Commons Clause** license, and is available for non-commercial academic purposes.
+
+# Reference <a name="Reference"></a>
+If you find our work useful in your research or if you use parts of this code please cite our [AAPM'23 abstract](https://aapm.confex.com/aapm/2023am/meetingapp.cgi/Paper/4208) :
+```
+@article{jhanwar2023portpy,
+  title={Portpy: An Open-Source Python Package for Planning and Optimization in Radiation Therapy Including Benchmark Data and Algorithms},
+  author={Jhanwar, Gourav and Tefagh, Mojtaba and Taasti, Vicki T and Alam, Sadegh R and Tuomaala, Seppo and Nadeem, Saad and Zarepisheh, Masoud},
+  journal={AAPM 65th Annual Meeting & Exhibition},
+  year={2023}
+}
+```