Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: A phantom study

Yuki Sakai, Erina Kitamoto, Kazutoshi Okamura, Masato Tatsumi, Takashi Shirasaka, Ryoji Mikayama, Masatoshi Kondo, Hiroshi Hamasaki, Toyoyuki Kato, Kazunori Yoshiura

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number20200553
JournalDentomaxillofacial Radiology
Volume50
Issue number7
DOIs
Publication statusPublished - Oct 1 2021

All Science Journal Classification (ASJC) codes

  • Otorhinolaryngology
  • Radiology Nuclear Medicine and imaging
  • Dentistry(all)

Fingerprint

Dive into the research topics of 'Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: A phantom study'. Together they form a unique fingerprint.

Cite this