Update README.md

README.md

@@ -43,57 +43,124 @@ configs:
   - split: test_previews
     path: data/test_previews-*
 ---
-# SimNICT
+# SimNICT: Simulated Non-Ideal CT Dataset
 
-- **SimNICT** is the first dataset for training **universal non-ideal measurement CT (NICT)** enhancement models.
+**SimNICT** is the first comprehensive dataset for training universal non-ideal measurement CT (NICT) enhancement models, addressing critical needs in medical imaging research.
 
-- The dataset comprises **over 10.8 million NICT-ICT image pairs**, including low dose CT (LDCT), sparse view CT (SVCT), and limited angle CT (LACT), under varying defect degrees across whole-body regions.
+## Dataset Overview
 
-- We have currently uploaded part of the SimNICT dataset, [**SimNICT-AMOS-Sample**](#simnict-amos-sample), with preview images in the dataset viewer. The complete SimNICT dataset will be gradually uploaded in future releases.
+- **10.8+ million NICT-ICT image pairs** across whole-body regions
+- **7,742 CT volumes** from 8 medical datasets (AutoPET & HECKTOR22 excluded due to licensing)
+- **3 NICT types**: Low-dose CT (LDCT), Sparse-view CT (SVCT), Limited-angle CT (LACT)
+- **3 severity levels** per type (Low, Mid, High) = **9 variations** per original CT volume
+- **File format**: NIfTI (.nii.gz), 16-bit, gzip compressed
 
+## Quick Start
 
-# SimNICT-AMOS-Sample
+### Option 1: Hugging Face Sample (55 volumes)
 
-- SimNICT-AMOS-Sample dataset contains **55 ICT volumes** from the AMOS dataset in SimNICT, and each ICT volume has been simulated using the same NICT simulation method as in SimNICT, generating **9 types of NICT volumes**.
+```python
+from datasets import load_dataset
+dataset = load_dataset("YutingHe-list/SimNICT")
+sample = dataset["train_previews"][0]
+ict_image = sample["ICT"]  # Ground truth
+ldct_low = sample["LDCT_Low"]  # Low-dose simulation
+```
 
-- This dataset is divided into training and test sets, with 20% and 80% of the total volumes, respectively, to evaluate the performance of our proposed [TAMP](***) model.
+### Option 2: Complete Datasets from Internet Archive
 
+**Automated download** (recommended):
 
-# Source Dataset Statistics
+```bash
+wget https://huggingface.co/datasets/YutingHe-list/SimNICT/resolve/main/download_simnict.py
+python download_simnict.py --datasets AMOS LUNA --output_dir ./data
+python download_simnict.py --all --output_dir ./data
+```
 
-- SimNICT starts from the ICT images from ten publicly CT datasets that encompass whole-body regions.
-- By removing low-quality volumes, our SimNICT dataset finally obtains **3,633,465 images** from **9,639 ICT volumes**. 
+**Manual download**:
 
-| Source   | Provenance  | Volume | Slice | License   |
-|-----------------------|-------------------------------------------------------------------------------------------------------------|--------|-----------|--------------------------------------------------------------------------------------------------|
-| COVID-19-NY-SBU        | [TCIA](https://www.cancerimagingarchive.net/collection/covid-19-ny-sbu/)    | 459    | 118,119   | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)    |
-| STOIC                 | [Grand Challenge](https://stoic2021.grand-challenge.org/)   | 2,000  | 867,376   | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)       |
-| MELA                  | [Grand Challenge](https://mela.grand-challenge.org/)    | 1,100    | 496,673   | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)   |
-| LUNA                  | [Grand Challenge](https://luna16.grand-challenge.org/)     | 888    | 227,225   | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)    |
-| LNDb                  | [Grand Challenge](https://lndb.grand-challenge.org/)     | 294    | 94,153    | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)  |
-| HECKTOR22             | [Grand Challenge](https://hecktor.grand-challenge.org/)   | 883    | 200,100   | []()      |
-| CT_COLONOGRAPHY       | [TCIA](https://www.cancerimagingarchive.net/collection/ct-colonography/)     | 1,730  | 938,082   | [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/)     |
-| AutoPET               | [Grand Challenge](https://autopet.grand-challenge.org/)    | 1,014  | 560,796   | []()      |
-| AMOS                  | [Grand Challenge](https://amos22.grand-challenge.org/) | 500    | 76,679    | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)       |
-| CT Images in COVID-19 | [TCIA](https://www.cancerimagingarchive.net/collection/ct-images-in-covid-19/) | 771    | 54,262    | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)       |
+```bash
+pip install internetarchive
+ia download simnict-amos simnict-luna  # etc.
+```
 
+## Complete Dataset Details
 
-<!-- # Dataset Structure
+| Dataset                         | Volumes | Clinical Focus           | Download Link                                                                           |
+| ------------------------------- | ------- | ------------------------ | --------------------------------------------------------------------------------------- |
+| **AMOS**                  | 500     | Multi-organ segmentation | [simnict-amos](https://archive.org/details/simnict-amos)                                   |
+| **COVID-19-NY-SBU**       | 459     | COVID-19 pneumonia       | [simnict-covid-19-ny-sbu](https://archive.org/details/simnict-covid-19-ny-sbu)             |
+| **CT Images in COVID-19** | 771     | COVID-19 diagnosis       | [simnict-ct-images-in-covid-19](https://archive.org/details/simnict-ct-images-in-covid-19) |
+| **CT_COLONOGRAPHY**       | 1,730   | Colorectal screening     | [simnict-ct-colonography](https://archive.org/details/simnict-ct-colonography)             |
+| **LNDb**                  | 294     | Lung nodule detection    | [simnict-lndb](https://archive.org/details/simnict-lndb)                                   |
+| **LUNA**                  | 888     | Lung nodule analysis     | [simnict-luna](https://archive.org/details/simnict-luna)                                   |
+| **MELA**                  | 1,100   | Melanoma detection       | [simnict-mela](https://archive.org/details/simnict-mela)                                   |
+| **STOIC**                 | 2,000   | COVID-19 screening       | [simnict-stoic](https://archive.org/details/simnict-stoic)                                 |
 
-- [More Information Needed] -->
+All datasets use **CC BY 4.0** license (CT_COLONOGRAPHY uses CC BY 3.0).
 
-# Ongoing
-- [ ] Release the SimNICT dataset containing 10.8 million NICT-ICT image pairs.
-- [x] Release the SimNICT-AMOS-Sample dataset, a subset of the SimNICT dataset.
+## Dataset Structure
 
-## Citation
 ```
+SimNICT/{dataset_name}/
+├── volume_001.nii.gz              # Original ICT (ground truth)
+├── volume_001_ldct_{low,mid,high}.nii.gz    # Low-dose CT
+├── volume_001_svct_{low,mid,high}.nii.gz    # Sparse-view CT  
+└── volume_001_lact_{low,mid,high}.nii.gz    # Limited-angle CT
+```
+
+## NICT Simulation Parameters
+
+| Type           | Low         | Mid         | High        |
+| -------------- | ----------- | ----------- | ----------- |
+| **LDCT** | I₀=1×10⁵ | I₀=1×10⁴ | I₀=1×10³ |
+| **SVCT** | 120 views   | 60 views    | 30 views    |
+| **LACT** | 120° range | 90° range  | 60° range  |
+
+## Usage Example
+
+```python
+import nibabel as nib
+from torch.utils.data import Dataset
+
+class SimNICTDataset(Dataset):
+    def __init__(self, data_dir, nict_type="ldct", severity="mid"):
+        self.data_dir = data_dir
+        self.nict_type = nict_type
+        self.severity = severity
+        # Load file pairs...
+  
+    def __getitem__(self, idx):
+        ict = nib.load(f"{self.data_dir}/volume_{idx:03d}.nii.gz").get_fdata()
+        nict = nib.load(f"{self.data_dir}/volume_{idx:03d}_{self.nict_type}_{self.severity}.nii.gz").get_fdata()
+        return {"input": nict, "target": ict}
+```
+
+## Applications
+
+- **Deep Learning**: Train robust NICT enhancement models
+- **Clinical Research**: Validate algorithms across diverse conditions
+- **Benchmarking**: Standardized evaluation of enhancement methods
+- **Method Development**: Test novel CT reconstruction techniques
+
+## Citation
+
+```bibtex
 @article{liu2024imaging,
-        title={Imaging foundation model for universal enhancement of non-ideal measurement ct},
-        author={Liu, Yuxin and Ge, Rongjun and He, Yuting and Wu, Zhan and Yang, Shangwen and Gao, Yuan and You, Chenyu and Wang, Ge and Chen, Yang and Li, Shuo},
-        journal={arXiv preprint arXiv:2410.01591},
-        year={2024}
+    title={Imaging foundation model for universal enhancement of non-ideal measurement ct},
+    author={Liu, Yuxin and Ge, Rongjun and He, Yuting and Wu, Zhan and Yang, Shangwen and Gao, Yuan and You, Chenyu and Wang, Ge and Chen, Yang and Li, Shuo},
+    journal={arXiv preprint arXiv:2410.01591},
+    year={2024}
 }
 ```
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 
+## Links
+
+- 📊 [Browse sample data](https://huggingface.co/datasets/YutingHe-list/SimNICT/viewer)
+- 📥 [Download script](https://huggingface.co/datasets/YutingHe-list/SimNICT/resolve/main/download_simnict.py)
+- 📖 [Usage examples](https://huggingface.co/datasets/YutingHe-list/SimNICT/resolve/main/example_usage.py)
+- 📑 [Research paper](https://arxiv.org/abs/2410.01591)
+
+---
+
+*For questions or collaborations, contact via the [arXiv paper](https://arxiv.org/abs/2410.01591). Contributions welcome!*