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Metal Artifact Reduction in Head CT Performed for Patients with Deep Brain Stimulation Devices: Effectiveness of a Single-Energy Metal Artifact Reduction Algorithm

Authors :
Seitaro Oda
Y. Yamashita
Shota Tanoue
Osamu Ikeda
Takeshi Nakaura
Daisuke Sakabe
Masafumi Kidoh
Ako Sasao
Takafumi Emoto
Yasunori Nagayama
Hiroyuki Uetani
Kazumichi Yamada
Source :
AJNR Am J Neuroradiol
Publication Year :
2019
Publisher :
American Society of Neuroradiology (ASNR), 2019.

Abstract

BACKGROUND AND PURPOSE: Deep brain stimulation electrodes induce massive artifacts on CT images, deteriorating the diagnostic value of examinations. We aimed to investigate the usefulness and potential limitations of a single-energy metal artifact reduction algorithm in head CT performed in patients with implanted deep brain stimulation devices. MATERIALS AND METHODS: Thirty-four patients with deep brain stimulation (bilateral, n = 28) who underwent head CT on a 320–detector row scanner and whose images were reconstructed with and without single-energy metal artifact reduction at the examinations were retrospectively included. The severity of artifacts around electrodes was assessed objectively using SDs and an artifact index. Two radiologists subjectively evaluated the severity of artifacts from electrodes, the visibility of electrode localization and surrounding structures, and overall diagnostic confidence on 4-point scales. Background image quality (GM-WM contrast and image noise) was subjectively and objectively assessed. The presence and location of artifacts newly produced by single-energy metal artifact reduction were analyzed. RESULTS: Single-energy metal artifact reduction provided lower objective and subjective metal artifacts and improved visualization of electrode localization and surrounding structures and diagnostic confidence compared with non-single-energy metal artifact reduction images, with statistical significance (all, P

Details

ISSN :
1936959X and 01956108
Volume :
41
Database :
OpenAIRE
Journal :
American Journal of Neuroradiology
Accession number :
edsair.doi.dedup.....caee1aa58d77e86c13febfeef3d5b0b1
Full Text :
https://doi.org/10.3174/ajnr.a6375