--- 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: 76595969 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 [MITAMP](***) 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/) | # Ongoing - [ ] Release the SimNICT dataset containing 10.6 million NICT-ICT image pairs. - [x] Release the SimNICT-AMOS-Sample dataset, a subset of the SimNICT dataset. ## Citation