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--- |
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license: cc-by-nd-4.0 |
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size_categories: |
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- n>1T |
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tags: |
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- medical |
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dataset_info: |
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features: |
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- name: ICT |
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dtype: image |
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- name: LDCT_Low |
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dtype: image |
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- name: LDCT_Mid |
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dtype: image |
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- name: LDCT_High |
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dtype: image |
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- name: LACT_Low |
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dtype: image |
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- name: LACT_Mid |
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dtype: image |
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- name: LACT_High |
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dtype: image |
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- name: SVCT_Low |
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dtype: image |
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- name: SVCT_Mid |
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dtype: image |
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- name: SVCT_High |
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dtype: image |
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splits: |
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- name: train_previews |
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num_bytes: 62199112.0 |
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num_examples: 44 |
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- name: test_previews |
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num_bytes: 16108271.0 |
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num_examples: 11 |
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download_size: 153191938 |
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dataset_size: 78307383.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train_previews |
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path: data/train_previews-* |
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- split: test_previews |
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path: data/test_previews-* |
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--- |
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# SimNICT |
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- **SimNICT** is the first dataset for training **universal non-ideal measurement CT (NICT)** enhancement models. |
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- 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. |
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- 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. |
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# SimNICT-AMOS-Sample |
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- 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**. |
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- 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 [MITAMP](***) model. |
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# Source Dataset Statistics |
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- SimNICT starts from the ICT images from ten publicly CT datasets that encompass whole-body regions. |
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- By removing low-quality volumes, our SimNICT dataset finally obtains **3,633,465 images** from **9,639 ICT volumes**. |
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| Source | Provenance | Volume | Slice | License | |
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|-----------------------|-------------------------------------------------------------------------------------------------------------|--------|-----------|--------------------------------------------------------------------------------------------------| |
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| 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/) | |
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| STOIC | [Grand Challenge](https://stoic2021.grand-challenge.org/) | 2,000 | 867,376 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
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| MELA | [Grand Challenge](https://mela.grand-challenge.org/) | 1,100 | 496,673 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
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| LUNA | [Grand Challenge](https://luna16.grand-challenge.org/) | 888 | 227,225 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
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| LNDb | [Grand Challenge](https://lndb.grand-challenge.org/) | 294 | 94,153 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
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| HECKTOR22 | [Grand Challenge](https://hecktor.grand-challenge.org/) | 883 | 200,100 | []() | |
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| 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/) | |
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| AutoPET | [Grand Challenge](https://autopet.grand-challenge.org/) | 1,014 | 560,796 | []() | |
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| AMOS | [Grand Challenge](https://amos22.grand-challenge.org/) | 500 | 76,679 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
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| 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/) | |
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<!-- # Dataset Structure |
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- [More Information Needed] --> |
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# Ongoing |
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- [ ] Release the SimNICT dataset containing 10.6 million NICT-ICT image pairs. |
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- [x] Release the SimNICT-AMOS-Sample dataset, a subset of the SimNICT dataset. |
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## Citation |
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<!-- 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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