Comparison of image quality in 40 keV virtual monoenergetic images of dual-energy CT pulmonary angiography using deep learning and iterative reconstruction algorithms under optimized low dose scanning protocols.

ABSTRACT:

Background

Pulmonary embolism is a potentially fatal cardiovascular condition that demands prompt and accurate diagnostic imaging. Traditional single-energy computed tomography pulmonary angiography (CTPA), while widely used, is associated with high radiation doses and substantial volumes of contrast agents, which may increase the risks of radiation-induced tissue damage and contrast-induced nephropathy (CIN), respectively. Dual-energy CTPA (DE-CTPA) presents a promising alternative, though challenges, including elevated image noise at low kilo-electron volt (keV) levels (e.g., 40 keV), persist. The primary aim of this study is to evaluate and compare the image quality of 40 keV virtual monoenergetic images (VMI) reconstructed using deep learning image reconstruction (DLIR) and Ada

SUBMITTER: Zhang D 

PROVIDER: S-EPMC12682530 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature