Impact of deep learning based reconstruction algorithms on CT radiomic features of carotid plaques.

ABSTRACT:

Background

Radiomics is increasingly applied in carotid plaques analysis to evaluate plaque characteristics and predict cardiovascular risk. However, the influence of different image reconstruction algorithms, particularly deep learning reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V), on the reproducibility of radiomic features remains poorly understood.

Purpose

To evaluate the impact of DLIR and ASIR-V on CT radiomic features of carotid plaques.

Methods

76 patients with 104 carotid plaques who underwent head & neck CT angiography were retrospectively enrolled. Images were reconstructed by filtered back projection (FBP), ASIR-V (30%, 50%, and 80%) and DLIR (DL, DM, and DH). A total of 214 CT-based radiomic features were organized b

SUBMITTER: Wang H 

PROVIDER: S-EPMC12618174 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature