Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
Department of Oral and Maxillofacial Radiology, Faculty of Dental Science, Kyushu University, Fukuoka, Japan.
Dentomaxillofac Radiol. 2021 Oct 1;50(7):20200553. doi: 10.1259/dmfr.20200553. Epub 2021 Apr 29.
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.
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.
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).
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.
本研究旨在通过评估口腔超高分辨率 CT（UHRCT）扫描仪中采集和重建参数对金属伪影降低（MAR）算法的影响，来提高 MAR 算法的效果。
使用超高分辨率、高分辨率（HR）和常规分辨率（NR）模式对带有和不带有病变的下颌骨牙齿模型进行扫描。使用基于深度学习的重建（DLR）和混合迭代重建（HIR）算法对图像进行 MAR 处理。两名牙科放射科医生独立对金属伪影的严重程度（1，非常严重；5，最小）和病变形状可重复性（1，轻微；5，几乎完美）进行评分。定量计算了信号-伪影比（SAR）、病变 CT 数的准确性和图像噪声。采用 Tukey-Kramer 检验（α 值<0.05）确定统计学意义。
HR 视觉评分在金属伪影严重程度（4.6±0.5 和 2.6±0.5，<0.0001）和病变形状可重复性（4.5±0.5 和 2.9±1.1，=0.0005）方面均优于 NR 评分。HR 的 SAR 明显优于 NR（4.9±0.4 和 2.1±0.2，<0.0001），HR 中 CT 数的绝对百分比误差也低于 NR（HR 中为 0.8%，NR 中为 23.8%）。HR 的图像噪声也低于 NR（HR 中为 15.7±1.4，NR 中为 51.6±15.3，<0.0001）。
本研究表明，在 UHRCT 扫描仪中，HR 模式与 DLR 的结合提高了 MAR 算法在口腔中的应用效果。
