Impact of a prototype, dedicated AI-based spectral image reconstruction algorithm on the quality of low-keV virtual monoenergetic images and iodine maps: A phantom study.

ABSTRACT:

Background

Recently, CT manufacturers have developed deep-learning-based reconstruction (DLR) algorithms for conventional and spectral CT images. On dual-energy CT platforms, DLR algorithms have been developed in particular to reduce image noise in virtual monoenergetic images (VMIs), and to improve the accuracy of iodine concentrations on iodine maps.

Purpose

To assess the impact of a prototype, dedicated Spectral AI Reconstruction (SAIR) algorithm (Spectral Precise Image; Philips Healthcare) compared with a hybrid iterative reconstruction (HIR) algorithm ("Spectral", Philips Healthcare) on the quality of low-keV VMIs and iodine maps in abdomen-pelvis CT conditions.

Methods

A spectral phantom was scanned on the dual-layer spectral CT system at 120 kVp and a CTDI

SUBMITTER: Greffier J 

PROVIDER: S-EPMC12635434 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature