Evaluation of thin-slice abdominal DECT using deep-learning image reconstruction in 74?keV virtual monoenergetic images: an image quality comparison

摘要

Abstract Purpose To compare noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image quality using deep-learning image reconstruction (DLIR) vs. adaptive statistical iterative reconstruction (ASIR-V) in 0.625 and 2.5?mm slice thickness gray scale 74?keV virtual monoenergetic (VM) abdominal dual-energy CT (DECT).Methods This retrospective study was approved by the institutional review board and regional ethics committee. We analysed 30 portal-venous phase abdominal fast kV-switching DECT (80/140kVp) scans. Data were reconstructed to ASIR-V 60 and DLIR-High at 74?keV in 0.625 and 2.5?mm slice thickness. Quantitative HU and noise assessment were measured within liver, aorta, adipose tissue and muscle. Two board-certified radiologists evaluated image noise, sharpness, texture and overall quality based on a five-point Likert scale.Results DLIR significantly reduced image noise and increased CNR as well as SNR compared to ASIR-V, when slice thickness was maintained (p?