Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Graduate School of Health Sciences, Kumamoto University, Kumamoto, Japan.
Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.
Eur J Radiol. 2020 Nov;132:109293. doi: 10.1016/j.ejrad.2020.109293. Epub 2020 Sep 21.
To investigate whether a novel iterative cone-beam computed tomography (CBCT) reconstruction algorithm reduces metal artifacts in head and neck patient images.
An anthropomorphic phantom and 35 patients with dental metal prostheses or implants were analyzed. All CBCT images were acquired using a TrueBeam linear accelerator and reconstructed with a Feldkamp-Davis-Kress algorithm-based CBCT (FDK-CBCT) and an iterative CBCT algorithm. The mean Hounsfield unit (HU) and standard deviation values were measured on the tongue near the metal materials and the unaffected region as reference values. The artifact index (AI) was calculated. For objective image analysis, the HU value and AI were compared between FDK-CBCT and iterative CBCT images in phantom and clinical studies. Subjective image analyses of metal artifact scores and soft tissue visualizations were conducted using a five-point scale by two reviewers in the clinical study.
The HU value and AI showed significant artifact reduction for the iterative CBCT than for the FDK-CBCT images (phantom study: 389.8 vs.-10.3 for HU value, 322.9 vs. 96.2 for AI, FDK-CBCT vs. iterative CBCT, respectively; clinical study: 210.3 vs. 69.0 for HU value, 149.6 vs. 70.7 for AI). The subjective scores in the clinical patient study were improved in the iterative CBCT images (metal artifact score: 1.1 vs. 2.9, FDK-CBCT vs. iterative CBCT, respectively; soft tissue visualization: 1.8 vs. 3.6).
The iterative CBCT reconstruction algorithm substantially reduced metal artifacts caused by dental metal prostheses and improved soft tissue visualization compared to FDK-CBCT in phantom and clinical studies.
研究一种新型迭代锥束 CT(CBCT)重建算法是否能降低头颈部患者图像中的金属伪影。
分析了一个人体模型和 35 名带有牙用金属修复体或植入物的患者。所有 CBCT 图像均使用 TrueBeam 直线加速器采集,并采用基于 Feldkamp-Davis-Kress 算法的 CBCT(FDK-CBCT)和迭代 CBCT 算法进行重建。在金属材料附近的舌部和未受影响的区域测量平均亨氏单位(HU)和标准差值作为参考值。计算伪影指数(AI)。在体模和临床研究中,对 FDK-CBCT 和迭代 CBCT 图像的 HU 值和 AI 进行了客观图像分析。在临床研究中,两位评审员使用五分制对金属伪影评分和软组织可视化进行了主观图像分析。
与 FDK-CBCT 图像相比,迭代 CBCT 图像的 HU 值和 AI 均显示出明显的伪影减少(体模研究:HU 值分别为 389.8 和-10.3,AI 分别为 322.9 和 96.2;临床研究:HU 值分别为 210.3 和 69.0,AI 分别为 149.6 和 70.7)。临床患者研究中的主观评分在迭代 CBCT 图像中得到了改善(金属伪影评分:1.1 和 2.9,FDK-CBCT 和迭代 CBCT 分别;软组织可视化:1.8 和 3.6)。
与 FDK-CBCT 相比,迭代 CBCT 重建算法在体模和临床研究中可显著降低牙用金属修复体引起的金属伪影,改善软组织可视化。