Update README.md

README.md

@@ -43,73 +43,112 @@ configs:
   - split: test_previews
     path: data/test_previews-*
 ---
-# SimNICT: Simulated Non-Ideal CT Dataset
+# SimNICT: Simulated Non-Ideal measurement CT Dataset
 
-**SimNICT** is the first comprehensive dataset for training universal non-ideal measurement CT (NICT) enhancement models, addressing critical needs in medical imaging research.
+**SimNICT** is the first comprehensive dataset for training universal non-ideal measurement CT (NICT) enhancement models, containing simulated low-dose, limited-angle, and sparse-view CT from different body regions.
 
-## Dataset Overview
+We open source the **[SimNICT Dataset](#part-1-simnict-dataset)** (~823 GB, 8 datasets) for comprehensive NICT research with a subset **[SimNICT-AMOS-Sample Dataset](#part-2-simnict-amos-sample)** (~78 MB) for quick exploration and prototyping.
 
-- **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
+**💡 Recommendation**: Start with **SimNICT-AMOS-Sample** for initial exploration and prototyping, then download specific datasets from the **SimNICT Dataset** based on your research needs.
 
-## Quick Start
+---
 
-### Option 1: Hugging Face Sample (55 volumes)
+## Part 1: SimNICT Dataset
 
-```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
-```
+| Dataset                         | Volumes | Body Regions | License                                                                                                   | Download Link                                                                           |
+| ------------------------------- | ------- | ------------ | --------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
+| **AMOS**                  | 500     | Abdomen      | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)                                                    | [simnict-amos](https://archive.org/details/simnict-amos)                                   |
+| **COVID-19-NY-SBU**       | 459     | Chest        | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)                                                    | [simnict-covid-19-ny-sbu](https://archive.org/details/simnict-covid-19-ny-sbu)             |
+| **CT Images in COVID-19** | 771     | Chest        | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)                                                    | [simnict-ct-images-in-covid-19](https://archive.org/details/simnict-ct-images-in-covid-19) |
+| **CT_COLONOGRAPHY**       | 1,730   | Abdomen      | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)                                                    | [simnict-ct-colonography](https://archive.org/details/simnict-ct-colonography)             |
+| **LNDb**                  | 294     | Chest        | [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/)                                        | [simnict-lndb](https://archive.org/details/simnict-lndb)                                   |
+| **LUNA**                  | 888     | Chest        | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)                                                    | [simnict-luna](https://archive.org/details/simnict-luna)                                   |
+| **MELA**                  | 1,100   | Chest        | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)                                                    | [simnict-mela](https://archive.org/details/simnict-mela)                                   |
+| **STOIC**                 | 2,000   | Chest        | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)                                              | [simnict-stoic](https://archive.org/details/simnict-stoic)                                 |
+| **AutoPET**               | 1,014   | Whole-body   | [NIH Controlled Data Access Policy](https://www.cancerimagingarchive.net/nih-controlled-data-access-policy/) | -                                                                                       |
+| **HECKTOR22**             | 882     | Head, neck   | [Custom Research License](https://hecktor.grand-challenge.org/Participation/)                                | -                                                                                       |
 
-### Option 2: Complete Datasets from Internet Archive
+*Note: AutoPET and HECKTOR22 datasets are not publicly available due to licensing restrictions.*
 
-**Automated download** (recommended):
+### Dataset Overview
 
-```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
-```
+The **SimNICT dataset** is a large-scale medical imaging dataset containing:
+
+- **📊 9,513 CT volumes** from 10 medical imaging datasets (2 out of 10 datasets are not open-source due to licensing restrictions)
+- **🔬 3 NICT types**: Low-dose CT (LDCT), Sparse-view CT (SVCT), Limited-angle CT (LACT)
+- **⚙️ Randomized parameters**:
+  - **SVCT**: Views randomly sampled from 15-360 range
+  - **LACT**: Angular range randomly sampled from 75°-270°
+  - **LDCT**: Dose levels randomly sampled from 5%-75% range
+- **💾 Total size**: ~823 GB
+- **📁 File format**: NIfTI (.nii.gz), 16-bit, gzip compressed
 
-**Manual download**:
+### Data Release Strategy
+
+The SimNICT dataset provides **preprocessed ICT data** with [**NICT simulation code**](https://huggingface.co/datasets/YutingHe-list/SimNICT/resolve/main/simnict_generator.py):
+
+**1. 🔍 Preprocessed ICT Data**
+
+The preprocessed ICT data of SimNICT dataset is hosted on [Internet Archive](https://archive.org/search.php?query=simnict), ensuring stable access for the global research community. You can download data through the [dataset table above](#part-1-simnict-dataset) or execute batch download scripts below:
 
 ```bash
-pip install internetarchive
-ia download simnict-amos simnict-luna  # etc.
+# Download batch download script
+wget https://huggingface.co/datasets/YutingHe-list/SimNICT/blob/main/simnict_download.py
+
+# Download all datasets (~823 GB)
+python simnict_download.py --all --output_dir ./data
+
+# Download specific datasets
+python simnict_download.py --datasets AMOS LUNA --output_dir ./data
 ```
 
-## Complete Dataset Details
+**2. ⚙️ NICT Simulation Code**
 
-| 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)                                 |
+After downloading preprocessed ICT data, generate NICT data with the following simulation code to construct complete SimNICT dataset:
 
-All datasets use **CC BY 4.0** license (CT_COLONOGRAPHY uses CC BY 3.0).
+**📋 Simulation Usage:**
 
-## Dataset Structure
+```python
+# Download simulation code
+wget https://huggingface.co/datasets/YutingHe-list/SimNICT/blob/main/simnict_generator.py
 
+# Configure paths and run
+python simnict_generator.py
 ```
-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
+
+The simulation code uses advanced physics-based modeling with **ODL** (Operator Discretization Library) and **ASTRA Toolbox** for accurate CT reconstruction simulation.
+
+---
+
+## Part 2: SimNICT-AMOS-Sample
+
+**SimNICT-AMOS-Sample** is a preview subset of SimNICT dataset for quick exploration and prototyping.
+
+### Sample Dataset Specifications
+
+- **📂 Source**: Selected from AMOS dataset (part of SimNICT)
+- **📊 Content**: 55 CT volumes (44 train + 11 test)
+- **🔬 Coverage**: 3 NICT types × 3 fixed severity levels (different from SimNICT dataset's randomized parameters)
+- **💾 Size**: ~78 MB (1000× smaller than SimNICT dataset)
+- **🚀 Format**: Preprocessed and optimized for Hugging Face platform
+
+### Quick Start with Sample Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the preview sample dataset
+dataset = load_dataset("YutingHe-list/SimNICT")
+sample = dataset["train_previews"][0]
+
+# Access different NICT simulations
+ict_image = sample["ICT"]        # Ground truth
+ldct_low = sample["LDCT_Low"]    # Low-dose simulation
+svct_mid = sample["SVCT_Mid"]    # Sparse-view simulation  
+lact_high = sample["LACT_High"]  # Limited-angle simulation
 ```
 
-## NICT Simulation Parameters
+### NICT Simulation Parameters (Sample Dataset Only)
 
 | Type           | Low         | Mid         | High        |
 | -------------- | ----------- | ----------- | ----------- |
@@ -117,44 +156,20 @@ SimNICT/{dataset_name}/
 | **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
+### 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}
 }
 ```
 
-## Links
+### 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)