Update README.md

README.md

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 license: mit
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+# Osteoarthritis Initiative (OAI) T2 Maps – EPG Fit Dataset
+---
+## Dataset Summary
+
+This dataset repository contains T2 maps derived from the Multi-Echo Spin-Echo (MESE) MRI data in the Osteoarthritis Initiative (OAI). 
+The maps were generated specifically for cartilage regions using the Extended Phase Graph (EPG) formalism, which improves the accuracy and reproducibility of cartilage T2 mapping, as detailed in the work of Marco Barbieri, Anthony A. Gatti, and Feliks Kogan (2024) [https://doi.org/10.1002/jmri.29646](https://doi.org/10.1002/jmri.29646).
+
+## Dataset Structure
+
+### Files and Folders
+
+The dataset is organized by acquisition timepoints. Each main folder represents a timepoint in the OAI dataset and contains subfolders for individual subjects.
+
+- **Timepoints**: `00m`, `12m`, `24m`, `36m`, `48m`, `72m`, `96m`.
+- **Subject folders**: Each folder name is the unique OAI subject ID (e.g., `9000099`).
+
+Within each subject folder:
+- **`t2.nii.gz`**: The T2 map computed using the EPG dictionary fitting method, specific to cartilage regions.
+- **`r2.nii.gz`**: The r-squared value of the fit (goodness of fit).
+
+### MESE Data Location Files
+
+For each acquisition timepoint (e.g., `00_month_mese_locations.csv`, `12_month_mese_locations.csv`, etc), a CSV file provides a mapping to the original MESE data within the OAI dataset. Each CSV file includes the following columns:
+
+- **subject_id**: The unique identifier for each OAI subject.
+- **visit**: The month corresponding to the acquisition timepoint (e.g., 36 for `36m`).
+- **laterality**: Indicates whether the MESE data is from the **RIGHT** or **LEFT** knee.
+- **dicom_mese_path**: The relative path to the original DICOM MESE data within the OAI dataset.
+- **t2map_nifti_path**: The relative path to the computed T2 map for that subject, located in this dataset.
+
+These CSV files help researchers locate the original MESE DICOM data within the OAI dataset, which may be useful for referencing or aligning with other imaging modalities.
+
+### Features
+
+- **Subject ID** (str): Unique identifier for each subject in the OAI study.
+- **T2 Map (`t2.nii.gz`)**: Computed T2 map for cartilage using the EPG fitting method.
+- **R-Squared Map (`r2.nii.gz`)**: Fit accuracy metric for the T2 computation.
+
+## Cartilage-Specific T2 Mapping
+
+The T2 map in this dataset is provided **only for cartilage regions**, as the EPG model used in the computation is specifically designed for cartilage magnetic resonance (MR) properties. The model leverages segmented cartilage regions from the femoral, tibial, and patellar regions. Here’s the complete mapping process:
+
+1. **Cartilage Segmentation**: For each subject, the femoral, tibial, and patellar cartilage were segmented from the corresponding Double Echo Steady State (DESS) image using the [ShapeMedKneeModel](https://huggingface.co/aagatti/ShapeMedKnee).
+  
+2. **Registration to MESE Images**: The segmented cartilage masks were then registered to the MESE images using [Elastix](https://github.com/SuperElastix/elastix), ensuring anatomical alignment across sequences.
+
+3. **Dilated Mask for T2 Mapping**: A dilated version of the cartilage mask was used during the T2 mapping process to allow researchers the flexibility to apply their segmentations if desired. This ensures that cartilage boundaries are fully captured while also accounting for anatomical variations.
+
+The cartilage segmentations used for the OAI dataset are available in the public repository [ShapeMedKnee](https://huggingface.co/aagatti/ShapeMedKnee) and will be regularly maintained and updated there.
+
+## Dataset Creation
+
+The T2 maps in this dataset were generated from the MESE data in the OAI dataset using the Extended Phase Graph (EPG) fitting method as described in the work by [Barbieri, Gatti, and Kogan, published in *Journal of Magnetic Resonance Imaging* (2024)](https://doi.org/10.1002/jmri.29646). 
+The code used to perform this fitting is open-source and accessible on GitHub at [EPGfit_for_cartilage_T2_mapping](https://github.com/barma7/EPGfit_for_cartilage_T2_mapping).
 
-This dataset repository contains the T2 maps from the Multi-Echo-Spin-Echo (MESE) data present in the Osteoarthritis Initiative (OAI) fitted using the Extended Phase Graph (EPG) formalism, as described in Marco Barbieri, Anthony A. Gatti, and Feliks Kogan's work "Improving Accuracy and Repeatability of Cartilage T2 Mapping in the OAI Dataset through Extended Phase Graph Modeling." 
-Currently,  MESE data have been processed until the 48-month follow-up, but we will upload the remaining time points soon. Check the repository frequently. 
+## Getting Started
 
-The journal article is currently in press in the Journal of Magnetic Resonance Imaging, but the full citation isn't still available and will be updated soon.
-If you use data from this repository, check frequently for the updated full citation and, please, cite the journal publication.
+### Installation
+
+You can install and access the dataset using the `datasets` library:
+
+```bash
+pip install datasets
+```
+
+### Usage
+
+Load and interact with the dataset in Python:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("barma7/oai-t2maps-epgfit")
+
+# Accessing a specific timepoint and subject data
+print(dataset["00m"]["9000099"]["t2"])
+print(dataset["00m"]["9000099"]["r2"])
+```
+
+## Dataset Details
+
+- **File Size**: Each T2 map file (`t2.nii.gz`) and r-squared file (`r2.nii.gz`) are stored in compressed `.nii.gz` format, with sizes varying per subject and time point.
+- **Number of Samples**: Covers subjects across seven OAI acquisition timepoints for which MESE was available.
+- **File Format**: `.nii.gz` files.
+
+## License
+
+This dataset is licensed under the MIT License, which allows for free use, modification, and distribution with attribution. For full license details, please see the LICENSE file in this repository.
+
+---
+
+## Acknowledgments
+
+This dataset was created based on the Osteoarthritis Initiative (OAI) dataset. The authors, Marco Barbieri, Anthony A. Gatti, and Feliks Kogan, acknowledge the original OAI study and the contributions of all OAI collaborators.
+
+## Citation
+
+If you use this dataset in your research, please cite:
 
-**Full journal citation:** 
 Barbieri, M., Gatti, A.A. and Kogan, F. (2024), Improving Accuracy and Reproducibility of Cartilage T2 Mapping in the OAI Dataset Through Extended Phase Graph Modeling. J Magn Reson Imaging. https://doi.org/10.1002/jmri.29646
 
-The code used to perform the fitting is publicly available on GitHub at: https://github.com/barma7/EPGfit_for_cartilage_T2_mapping
+---
+
+This README now covers all necessary information in a structured, accessible way. Let me know if you’d like further adjustments!