Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China.
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, People's Republic of China.
Phys Med Biol. 2023 Aug 17;68(17). doi: 10.1088/1361-6560/acec29.
Cone beam computed tomography (CBCT) has been wildly used in clinical treatment of dental diseases. However, patients often have metallic implants in mouth, which will lead to severe metal artifacts in the reconstructed images. To reduce metal artifacts in dental CBCT images, which have a larger amount of data and a limited field of view compared to computed tomography images, a new dental CBCT metal artifact reduction method based on a projection correction and a convolutional neural network (CNN) based image post-processing model is proposed in this paperThe proposed method consists of three stages: (1) volume reconstruction and metal segmentation in the image domain, using the forward projection to get the metal masks in the projection domain; (2) linear interpolation in the projection domain and reconstruction to build a linear interpolation (LI) corrected volume; (3) take the LI corrected volume as prior and perform the prior based beam hardening correction in the projection domain, and (4) combine the constructed projection corrected volume and LI-volume slice-by-slice in the image domain by two concatenated U-Net based models (CNN1 and CNN2). Simulated and clinical dental CBCT cases are used to evaluate the proposed method. The normalized root means square difference (NRMSD) and the structural similarity index (SSIM) are used for the quantitative evaluation of the method.The proposed method outperforms the frequency domain fusion method (FS-MAR) and a state-of-art CNN based method on the simulated dataset and yields the best NRMSD and SSIM of 4.0196 and 0.9924, respectively. Visual results on both simulated and clinical images also illustrate that the proposed method can effectively reduce metal artifacts.. This study demonstrated that the proposed dual-domain processing framework is suitable for metal artifact reduction in dental CBCT images.
锥形束计算机断层扫描（CBCT）已广泛应用于口腔疾病的临床治疗中。然而，患者口中常常有金属植入物，这会导致重建图像中出现严重的金属伪影。为了减少与 CT 图像相比数据量更大、视场有限的口腔 CBCT 图像中的金属伪影，本文提出了一种基于投影校正和基于卷积神经网络（CNN）的图像后处理模型的新型口腔 CBCT 金属伪影减少方法。
（1）在图像域中进行容积重建和金属分割，使用正向投影得到投影域中的金属掩模；（2）在投影域中进行线性插值和重建，构建线性插值（LI）校正容积；（3）以 LI 校正容积为先验，在投影域中进行基于先验的束硬化校正；（4）通过两个串联的基于 U-Net 的模型（CNN1 和 CNN2），在图像域中将构建的投影校正容积和 LI 容积逐片合并。使用模拟和临床口腔 CBCT 病例来评估所提出的方法。归一化均方根差（NRMSD）和结构相似性指数（SSIM）用于方法的定量评估。
在所提出的方法在模拟数据集上优于频域融合方法（FS-MAR）和最先进的基于 CNN 的方法，并分别产生最佳的 NRMSD 和 SSIM 值 4.0196 和 0.9924。模拟和临床图像的视觉结果也表明，该方法可以有效地减少金属伪影。这项研究表明，所提出的双域处理框架适用于口腔 CBCT 图像中的金属伪影减少。
