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---
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. -->
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