Yahui Hu, Ying Chen, Jianjun Huo, Linxin Zhang, Yan Li, Qian Liu, Yaohui Yu, Haicheng Qi, Zicheng Zhao, Jiaojiao Zhang, Min Xu, Yan Xing
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引用次数: 0
Abstract
Objective: To evaluate the efficacy of single-energy metal artifact reduction (SEMAR) algorithm in removing metallic artifacts for patients with novel leadless pacemakers (PMs) compared to those with conventional transvenous PMs.
Methods: 30 patients with transvenous PMs and 30 patients with leadless PMs having coronary CT angiography (CCTA) were included. Images were reconstructed using two methods: a conventional hybrid-iterative reconstruction (HIR) algorithm separately, and the HIR combined with SEMAR algorithm. The artifact index (AI) was calculated for both datasets to quantitatively assess the artifact reduction. Two senior radiologists independently scored the artifact extent and diagnostic confidence of surrounding tissues. The position of artifacts-impaired tissues was also visually evaluated as follows: non-impaired, impaired, and equivocal. The study was ethics committee-approved.
Results: The leadless PMSEMAR exhibited significantly lower AIs than the leadless PMnon-SEMAR, with an improvement of hypodense artifacts and a significant reduction of hyperdense artifacts (all p < 0.001). The AIs of leadless PMs performed with and without SEMAR were slightly lower than those of transvenous PMs, although no statistical difference existed (all p > 0.05). Qualitatively, the artifact extent of SEMAR significantly outperformed non-SEMAR in both types of PM (p < 0.001). The aorta, right coronary artery, right atrium, and left ventricle were less impaired in leadless PMnon-SEMAR compared to transvenous PMnon-SEMAR (all p < 0.05).
Conclusions: SEMAR provides effective reduction of metallic artifacts from both types of cardiac PMs. The artifact-impaired tissues are fewer on leadless PMs, resulting in an enhanced diagnostic confidence in the surrounding tissues.
期刊介绍:
BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.