Noise reduction performance of a deep learning-based reconstruction in brain computed tomography images acquired with organ-based tube current modulation

Abstract

We aimed to evaluate the image quality of brain computed tomography (CT) images reconstructed using deep learning-based reconstruction (DLR) in organ-based tube current modulation (OB-TCM) acquisition. An anthropomorphic head phantom and a cylindrical low-contrast phantom were scanned at the standard dose level for adult brain CT in axial volume acquisition without OB-TCM. Moreover, image acquisition with OB-TCM was performed. The radiation dose on the eye lens was measured using a scintillation fibre-optic dosimeter placed on the anthropomorphic phantom's eye surface. The task transfer function (TTF), contrast-to-noise ratio (CNR), and low-contrast object specific CNR obtained from low-contrast phantom images reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR), and two types of DLR (DLR_CTA and DLR_LCD) were compared. In result, OB-TCM achieved a 32.5% dose reduction in the eye lens. Although HIR, DLR_CTA, and DLR_LCD showed lower TTF than FBP, the difference in TTF at the highest contributing spatial frequency corresponding to the contrast rod diameter was < 10%. Despite the OB-TCM acquisition, DLR_CTA and DLR_LCD achieved significantly lower noise and a higher CNR than FBP without OB-TCM (p < 0.05). However, low-contrast object specific CNR was equivalent among all reconstruction methods for the objective diameter of 5 mm and slightly improved in DLR_LCD for the objective diameter of 7 mm. DLR with OB-TCM acquisition enabled dose reduction for the eye lens and high CNR image appearance, whereas the low contrast detectability evaluated by low-contrast object specific CNR did not always improve.

Keywords: Computed tomography; Deep learning-based reconstruction; Image quality assessment; Low contrast; Organ-based tube current modulation.