Chest CT-IQA: A multi-task model for chest CT image quality assessment and classification.

In recent years, especially during the COVID-19 pandemic, a large number of Computerized Tomography (CT) images are produced every day for the purpose of inspecting lung diseases. However, the diagnosis accuracy depends on the quality of CT imaging and low quality images may greatly affect clinical diagnosis, resulting in misdiagnosis. It is difficult to effectively rate the quality of massive CT images. To solve the above problems, we first constructed a dataset of 800 CT volumes for chest CT image quality assessment. Then we propose a multi-task model for chest CT image quality assessment and classification. This model can automatically classify CT image sequences of different visual inspection windows, and automatically estimate CT image quality score, to match the visual score from clinicians. The experimental results show that the window classification accuracy and the dose exposure classification accuracy of our model can reach 0.8375 and 0.8813 respectively. The Pearson Linear Correlation Coefficient (PLCC) and Root Mean Square Error (RMSE) between the model prediction results and the two radiologist's annotation average result reached 0.3288 and 1.9264. It shows that our model has a potential to mimic quality evaluation of experts. • The first dataset for chest CT-IQA without synthetic data is constructed. • A multi-task model of CT image classification and quality assessment is proposed. • Finetune the model using the clinician's manual annotation to improve the accuracy. [ABSTRACT FROM AUTHOR]