---
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

- **SimNICT** is the first dataset for training **universal non-ideal measurement CT (NICT)** enhancement models.

- The dataset comprises **over 10.9 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.

- 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.


# SimNICT-AMOS-Sample

- 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**.

- 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.


# Source Dataset Statistics

- 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**. 

| 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/)       |


<!-- # Dataset Structure

- [More Information Needed] -->

# Ongoing
- [ ] Release the SimNICT dataset containing 10.9 million NICT-ICT image pairs.
- [x] Release the SimNICT-AMOS-Sample dataset, a subset of the SimNICT dataset.

## Citation
```
@misc{liu2024imagingfoundationmodeluniversal,
      title={Imaging foundation model for universal enhancement of non-ideal measurement CT}, 
      author={Yuxin Liu and Rongjun Ge and Yuting He and Zhan Wu and Chenyu You and Shuo Li and Yang Chen},
      year={2024},
      eprint={2410.01591},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2410.01591}, 
}
```
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->