Download dataset here.
Image scoring criteria
We obtained image quality scores for each CT image using abdominal soft-tissue windows (width/level: 350/40) from five experienced radiologists. The final human perceptual score for each image was calculated by averaging the scores assigned by the five radiologists. To ensure that the diagnostic image quality assessment criteria reflect the clinical relevance, we carefully defined them. These criteria can be found in the table below.
Image generation prodecure
For a detailed information about the image generation procedure, click here.
Funding Declaration for this Challenge
This research was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University)), and by the National Research Foundation of Korea (NRF-2022R1A2C1092072), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174276, RS-2020-KD000016).
Original Low-dose CT Dataset Reference
Acknowledge: NIH grants EB017095 and EB017185 (Cynthia McCollough, PI) from the National Institute of Biomedical Imaging and Bioengineering, and American Association of Physicists in Medicine, Low Dose CT Grand Challenge Dataset, https://app.box.com/s/eaw4jddb53keg1bptavvvd1sf4x3pe9h, (related paper: Moen, T. R., Chen, B., Holmes, D. R., III, Duan, X., Yu, Z., Yu, L., Leng, S., Fletcher, J. G., & McCollough, C. H. (2020). Low dose CT image and projection
How to cite the dataset
Citation of these data should use the following:
Lee, Wonkyeong, Wagner, Fabian, Maier, Andreas, Wang, Adam, Baek, Jongduk, Hsieh, Scott, & Choi, Jang-Hwan. (2023). Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7833096