Reduction of CT radiation dose is important due to the potential effects on patients. But lowering dose incurs degradation in the reconstructed image quality, furthering compromise in the diagnostic and image-based analyses performance. Considering the patient health risks, high quality reference images cannot be easily obtained, making the assessment challenging. Therefore, automatic no-reference image quality assessment is desirable. Leveraging an innovative self-supervised regularization in a convolutional neural network, we propose a novel, fully automated, no-reference CT image quantification method namely self-supervised image quality assessment (SSIQA). Extensive experimentation via in-domain (abdomen CT) and cross-domain (chest CT) evaluations demonstrates SSIQA is accurate in quantifying CT image quality, generalized across the scan types, and consistent with the established metrics and different relative dose levels.
|Title of host publication||2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021|
|Number of pages||4|
|State||Published - Apr 13 2021|
|Event||18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France|
Duration: Apr 13 2021 → Apr 16 2021
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||18th IEEE International Symposium on Biomedical Imaging, ISBI 2021|
|Period||4/13/21 → 4/16/21|
Bibliographical noteFunding Information:
This work was supported by GE Healthcare.
© 2021 IEEE.
- Image quality assessment
- Radiation dose
- Self-supervised learning
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging