Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.

ABSTRACT:

Background

Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.

Purpose

To assess the impact of DLIR on image quality (IQ) and detectability of simulated hypervascular hepatocellular carcinoma (HCC) in fast kV-switching dual-energy CT (DECT).

Methods

An anthropomorphic phantom of a standard patient morphology (body mass index of 23 kg m^-2) with customized liver, including mimickers of hypervascular lesions in both late arterial phase (AP) and portal venous phase (PVP) enhancement, was scanned on a DECT. Virtual monoenergetic images were reconstructed from raw data at four energy levels (40/50/60/70 keV) using filtered back-projection

SUBMITTER: Pauthe A 

PROVIDER: S-EPMC11972042 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature