In:
Acta Radiologica, SAGE Publications, Vol. 58, No. 3 ( 2017-03), p. 279-285
Abstract:
Metal artifacts often impair diagnostic accuracy in computed tomography (CT) imaging. Therefore, effective and workflow implemented metal artifact reduction algorithms are crucial to gain higher diagnostic image quality in patients with metallic hardware. Purpose To assess the clinical performance of a novel iterative metal artifact reduction (iMAR) algorithm for CT in patients with dental fillings. Material and Methods Thirty consecutive patients scheduled for CT imaging and dental fillings were included in the analysis. All patients underwent CT imaging using a second generation dual-source CT scanner (120 kV single-energy; 100/Sn140 kV in dual-energy, 219 mAs, gantry rotation time 0.28–1/s, collimation 0.6 mm) as part of their clinical work-up. Post-processing included standard kernel (B49) and an iterative MAR algorithm. Image quality and diagnostic value were assessed qualitatively (Likert scale) and quantitatively (HU ± SD) by two reviewers independently. Results All 30 patients were included in the analysis, with equal reconstruction times for iMAR and standard reconstruction (17 s ± 0.5 vs. 19 s ± 0.5; P 〉 0.05). Visual image quality was significantly higher for iMAR as compared with standard reconstruction (3.8 ± 0.5 vs. 2.6 ± 0.5; P 〈 0.0001, respectively) and showed improved evaluation of adjacent anatomical structures. Similarly, HU-based measurements of degree of artifacts were significantly lower in the iMAR reconstructions as compared with the standard reconstruction (0.9 ± 1.6 vs. –20 ± 47; P 〈 0.05, respectively). Conclusion The tested iterative, raw-data based reconstruction MAR algorithm allows for a significant reduction of metal artifacts and improved evaluation of adjacent anatomical structures in the head and neck area in patients with dental hardware.
Type of Medium:
Online Resource
ISSN:
0284-1851
,
1600-0455
DOI:
10.1177/0284185116646144
Language:
English
Publisher:
SAGE Publications
Publication Date:
2017
detail.hit.zdb_id:
2024579-8
