Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge 2023
Creators: name: Wonkyeong Lee
name: Wagner, Fabian
name: Maier, Andreas
name: Wang, Adam
name: Jongduk Baek
name: Hsieh, Scott S.
name: Jang-Hwan Choi
Publisher: name: Zenodo
Year: 2023
Subjects: Computed tomography
no-reference image quality assessment
perceptual image quality
radiation dose
streak artifact
MICCAI Challenges

description: Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating highquality<br> images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to the risk posed to patients as a result of radiation dosage. To overcome these limitations, several studies have aimed to develop a no-reference novel image quality metric that correlates well with radiologists’ opinion on image quality without any reference images (2, 4, 5). Nevertheless, due to the lack of open-source datasets specifically for CT IQA, experiments have been conducted with datasets that differ from each other, rendering their results incomparable and introducing difficulties in determining a standard image quality metric for CT imaging. Besides, unlike real low-dose CT images with quality<br> degradation due to various combinations of artifacts, most studies are conducted with only one type of artifact (e.g., low-dose noise (6-11), view aliasing (12), metal artifacts (13), scattering (14-16), motion artifacts (17-22), etc.). Therefore, this challenge aims to 1) evaluate various NR-IQA models on CT images containing complex noise/artifacts, 2) to compare their correlations with scores produced by radiologists, and 3) to grant insights into the determination of the best-performing metric of CT imaging in terms of correlating with the perception of radiologists’. Furthermore, considering that low-dose CT images are achieved by reducing the number of projections per rotation and by reducing the X-ray current, the combination of two major artifacts, namely the sparse view streak and noise generated by these methods, is dealt with in this challenge so that the best-performing IQA model applicable in real clinical environments can be verified. 1. Lee W, Cho E, Kim W, Choi J-H. Performance evaluation of image quality metrics for perceptual assessment of<br> low-dose computed tomography images. Medical Imaging 2022: Image Perception, Observer Performance, and<br> Technology Assessment: SPIE, 2022.<br> 2. Lee W, Cho E, Kim W, Choi H, Beck KS, Yoon HJ, Baek J, Choi J-H. No-reference perceptual CT image quality<br> assessment based on a self-supervised learning framework. Machine Learning: Science and Technology 2022.<br> 3. Choi D, Kim W, Lee J, Han M, Baek J, Choi J-H. Integration of 2D iteration and a 3D CNN-based model for multitype<br> artifact suppression in C-arm cone-beam CT. Machine Vision and Applications 2021;32(116):1-14.<br> 4. Pal D, Patel B, Wang A. SSIQA: Multi-task learning for non-reference CT image quality assessment with selfsupervised<br> noise level prediction. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI): IEEE,<br> 2021; p. 1962-1965.<br> 5. Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image<br> Process 2012;21(12):4695-4708. doi: 10.1109/TIP.2012.2214050<br> 6. Lee J-YK, Wonjin; Lee, Yebin; Lee, Ji-Yeon; Ko, Eunji; Choi, Jang-Hwan. Unsupervised Domain Adaptation for<br> Low-dose Computed Tomography Denoising. IEEE Access 2022.<br> 7. Jeon S-Y, Kim W, Choi J-H. MM-Net: Multi-frame and Multi-mask-based Unsupervised Deep Denoising for<br> Low-dose Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences 2022.<br> 8. 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