README.md

---
license: cc-by-nd-4.0
size_categories:
- n>1T
tags:
- medical
dataset_info:
  features:
  - name: ICT
    dtype: image
  - name: LDCT_Low
    dtype: image
  - name: LDCT_Mid
    dtype: image
  - name: LDCT_High
    dtype: image
  - name: LACT_Low
    dtype: image
  - name: LACT_Mid
    dtype: image
  - name: LACT_High
    dtype: image
  - name: SVCT_Low
    dtype: image
  - name: SVCT_Mid
    dtype: image
  - name: SVCT_High
    dtype: image
  splits:
  - name: train_previews
    num_bytes: 62199112.0
    num_examples: 44
  - name: test_previews
    num_bytes: 16108271.0
    num_examples: 11
  download_size: 153191938
  dataset_size: 78307383.0
configs:
- config_name: default
  data_files:
  - split: train_previews
    path: data/train_previews-*
  - split: test_previews
    path: data/test_previews-*
---
# SimNICT: Simulated Non-Ideal measurement CT Dataset

**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.
We release the **SimNICT Dataset** (823 GB, 8 datasets)  for comprehensive NICT research, and provide **SimNICT-AMOS-Sample** (78 MB) for quick exploration and prototyping.

**💡 Recommendation**: Start with [SimNICT-AMOS-Sample Dataset](#part-2-simnict-amos-sample) for initial exploration and prototyping, then download specific datasets from the [SimNICT Dataset](#part-1-simnict-dataset) based on your research needs.

---

## Part 1: SimNICT Dataset

| 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/)                                | -                                                                                       |

*Note: AutoPET and HECKTOR22 datasets are not publicly available due to licensing restrictions.*

### Dataset Overview

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

### 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
# 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
```

**2. ⚙️ NICT Simulation Code**

After downloading preprocessed ICT data, generate NICT data with the following simulation code to construct complete SimNICT dataset:

```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
```

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 (Sample Dataset Only)

| 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  |

---

### 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}
}
```

### 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)
- 📑 [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!*