Ssiqa: Multi-Task Learning For Non-Reference Ct Image Quality Assessment With Self-Supervised Noise Level Prediction

Abstract

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.