Metal Artefact Reduction in the Oral Cavity Using Deep Learning Reconstruction Algorithm in Ultra-high-resolution Computed Tomography: a Phantom Study

Overview

Journal Dentomaxillofac Radiol

Publisher Oxford University Press

Date 2021 Apr 29

PMID 33914646

Citations 1

Authors

Yuki Sakai

Erina Kitamoto

Kazutoshi Okamura

Masato Tatsumi

Takashi Shirasaka

Ryoji Mikayama

Masatoshi Kondo

Hiroshi Hamasaki

Toyoyuki Kato

Kazunori Yoshiura

Affiliations

Soon will be listed here.

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 -value of less than 0.05 was used to determine statistical significance.

Results: The HR visual score was better than the NR score in terms of degree of metal artefact (4.6 ± 0.5 and 2.6 ± 0.5, < 0.0001) and lesion shape reproducibility (4.5 ± 0.5 and 2.9 ± 1.1, = 0.0005). The SAR of HR was significantly better than that of NR (4.9 ± 0.4 and 2.1 ± 0.2, < 0.0001), and the absolute percentage error of the CT number in HR was lower than that in NR (0.8% in HR and 23.8% in NR). The image noise of HR was lower than that of NR (15.7 ± 1.4 and 51.6 ± 15.3, < 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.

Citing Articles

Comparison between ultra-high-resolution computed tomographic angiography and conventional computed tomographic angiography in the visualization of the subcallosal artery.

Sato Y, Endo T, Kayano S, Nemoto H, Shimada K, Ito A Surg Neurol Int. 2021; 12:528.

PMID: 34754578 PMC: 8571191. DOI: 10.25259/SNI_887_2021.

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