School of Integrated Technology, Yonsei University, Incheon, South Korea.
Departments of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, and Medical Device Innovation Center, Pohang University of Science and Technology, Pohang, South Korea.
Med Phys. 2022 Sep;49(9):6253-6277. doi: 10.1002/mp.15884. Epub 2022 Aug 10.
Sparse-view sampling has attracted attention for reducing the scan time and radiation dose of dental cone-beam computed tomography (CBCT). Recently, various deep learning-based image reconstruction techniques for sparse-view CT have been employed to produce high-quality image while effectively reducing streak artifacts caused by the lack of projection views. However, most of these methods do not fully consider the effects of metal implants. As sparse-view sampling strengthens the artifacts caused by metal objects, simultaneously reducing both metal and streak artifacts in sparse-view CT images has been challenging. To solve this problem, in this study, we propose a novel framework.
The proposed method was based on the normalized metal artifact reduction (NMAR) method, and its performance was enhanced using two convolutional neural networks (CNNs). The first network reduced the initial artifacts while preserving the fine details to generate high-quality priors for NMAR processing. Subsequently, the second network was employed to reduce the streak artifacts after NMAR processing of sparse-view CT data. To validate the proposed method, we generated training and test data by computer simulations using both extended cardiac-torso (XCAT) and clinical data sets.
Visual inspection and quantitative evaluations demonstrated that the proposed method effectively reduced both metal and streak artifacts while preserving the details of anatomical structures compared with the conventional metal artifact reduction methods.
We propose a framework for reconstructing accurate CT images in metal-inserted sparse-view CT. The proposed method reduces streak artifacts from both metal objects and sparse-view sampling while recovering the anatomical details, indicating the feasibility of fast-scan dental CBCT imaging.
稀疏视角采样引起了人们的关注，可减少牙科锥形束 CT（CBCT）的扫描时间和辐射剂量。最近，各种基于深度学习的稀疏视图 CT 图像重建技术已被用于生成高质量图像，同时有效减少因投影视图不足而导致的条纹伪影。然而，这些方法大多没有充分考虑金属植入物的影响。由于稀疏视角采样会增强金属物体引起的伪影，因此在稀疏视角 CT 图像中同时减少金属伪影和条纹伪影一直具有挑战性。为了解决这个问题，本研究提出了一种新的框架。
所提出的方法基于归一化金属伪影减少（NMAR）方法，并使用两个卷积神经网络（CNN）来增强其性能。第一个网络在生成高质量 NMAR 处理先验的同时，减少初始伪影并保留精细细节。随后，第二个网络用于在稀疏视图 CT 数据的 NMAR 处理后减少条纹伪影。为了验证所提出的方法，我们使用扩展心脏-胸部（XCAT）和临床数据集通过计算机模拟生成训练和测试数据。
视觉检查和定量评估表明，与传统的金属伪影减少方法相比，所提出的方法在保留解剖结构细节的同时，有效减少了金属和条纹伪影。
我们提出了一种在插入金属的稀疏视图 CT 中重建准确 CT 图像的框架。该方法减少了金属物体和稀疏视角采样产生的条纹伪影，同时恢复了解剖细节，表明快速扫描牙科 CBCT 成像的可行性。
