Relationship between Image Quality and Reconstruction FOV in Deep Learning Reconstructed Images of CT

Abstract

In this study, we compared the image quality of deep learning reconstruction (DLR) with that of conventional image reconstruction methods under the same conditions of reconstruction FOV and acquisition dose assuming abdomen computed tomography (CT) in children. Standard deviation (SD) of the CT value, noise power spectrum (NPS), and task-based modulation transfer function (TTF) were evaluated. DLR reduced image noise while maintaining sharpness, and the noise reduction effect showed a different characteristic depending on the size of reconstruction FOV from the conventional image reconstruction methods. The SD of CT value increased gradually in the range from 320 mm to 240 mm, but there was almost no change from 240 mm to 200 mm. The NPS showed completely different characteristics. The low-frequency component increased, and the high-frequency component decreased at 240 mm. However, the frequency component below 0.5 cycle/mm decreased at 200 mm and the peak frequency moved to the lower side at 320 mm. DLR showed the highest TTF value compared to the conventional reconstruction methods.