Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: a phantom study.
Dentomaxillofac Radiol
; 50(7): 20200553, 2021 Oct 01.
Article
em En
| MEDLINE
| ID: mdl-33914646
ABSTRACT
OBJECTIVES:
This study aimed to improve the impact of the metal artefact reduction (MAR) algorithm for the oral cavity by assessing the effect of acquisition and reconstruction parameters on an ultra-high-resolution CT (UHRCT) scanner.METHODS:
The mandible tooth phantom with and without the lesion was scanned using super-high-resolution, high-resolution (HR), and normal-resolution (NR) modes. Images were reconstructed with deep learning-based reconstruction (DLR) and hybrid iterative reconstruction (HIR) using the MAR algorithm. Two dental radiologists independently graded the degree of metal artefact (1, very severe; 5, minimum) and lesion shape reproducibility (1, slight; 5, almost perfect). The signal-to-artefact ratio (SAR), accuracy of the CT number of the lesion, and image noise were calculated quantitatively. The Tukey-Kramer method with a p-value of less than 0.05 was used to determine statistical significance.RESULTS:
The HRDLR visual score was better than the NRHIR score in terms of degree of metal artefact (4.6 ± 0.5 and 2.6 ± 0.5, p < 0.0001) and lesion shape reproducibility (4.5 ± 0.5 and 2.9 ± 1.1, p = 0.0005). The SAR of HRDLR was significantly better than that of NRHIR (4.9 ± 0.4 and 2.1 ± 0.2, p < 0.0001), and the absolute percentage error of the CT number in HRDLR was lower than that in NRHIR (0.8% in HRDLR and 23.8% in NRIR). The image noise of HRDLR was lower than that of NRHIR (15.7 ± 1.4 and 51.6 ± 15.3, p < 0.0001).CONCLUSIONS:
Our study demonstrated that the combination of HR mode and DLR in UHRCT scanner improved the impact of the MAR algorithm in the oral cavity.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article