Image Quality Assessment of Deep Learning Image Reconstruction in Torso Computed Tomography Using Tube Current Modulation

Novel deep learning image reconstruction (DLIR) reportedly changes the image quality characteristics based on object contrast and image noise. In clinical practice, computed tomography image noise is usually controlled by tube current modulation (TCM) to accommodate changes in object size. This study aimed to evaluate the image quality characteristics of DLIR for different object sizes when the in-plane noise was controlled by TCM. Images acquisition was performed on a GE Revolution CT system to investigate the impact of the DLIR algorithm compared to the standard reconstructions of filtered-back projection (FBP) and hybrid iterative reconstruction (hybrid-IR). The image quality assessment was performed using phantom images, and an observer study was conducted using clinical cases. The image quality assessment confirmed the excellent noise- reduction performance of DLIR, despite variations due to phantom size. Similarly, in the observer study, DLIR received high evaluations regardless of the body parts imaged. We evaluated a novel DLIR algorithm by replicating clinical behaviors. Consequently, DLIR exhibited higher image quality than those of FBP and hybrid-IR in both phantom and observer studies, albeit the value depended on the reconstruction strength, and proved itself capable of providing stable image quality in clinical use.