Task-Focused Knowledge Transfer from Natural Images for CT Image Quality Assessment

Radiation dose in computed tomography (CT) and image quality are closely correlated. Good quality CT images better help radiologists diagnose diseases. Although increasing radiation dose improves image quality, it comes with various health risks for patients. Therefore, good-quality CT images at lower doses are required to balance the trade-off. However, assessing the quality of low-dose CT images requires feedback from different radiologists, which is time-consuming and laborious. Although several studies demonstrate automated CT image quality assessment (IQA), complete reference-free tools are rare. Moreover, most of the existing deep learning methods rely on the availability of large CT datasets with IQA scores as a proxy to radiologists’. However, it can be challenging to obtain large-labeled datasets and the proxy IQA scores might not correlate well to the diagnostic quality followed by clinicians. To achieve an assessment closely related to radiologists’ feedback, we propose a novel, automated, and reference-free CT image quality assessment method, namely Task-Focused Knowledge Transfer (TFKT) for IQA estimation leveraging natural images of similar tasks and an effective hybrid CNN-Transformer model. Extensive evaluations demonstrate the proposed TFKT’s effectiveness in accurately predicting in-domain radiologists’ provided IQA prediction and evaluating out-of-domain clinical images of pediatric CT exams.