Park Hyoung Suk, Jeon Kiwan, Seo J K
National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seodaemun-gu, Seoul, Republic of Korea.
Philos Trans A Math Phys Eng Sci. 2025 Sep 25;383(2305):20240045. doi: 10.1098/rsta.2024.0045.
This paper examines the current challenges in computed tomography (CT), with a critical exploration of existing methodologies from a mathematical perspective. Specifically, it aims to identify research directions to enhance image quality in low-dose, cost-effective cone beam CT (CBCT) systems, which have recently gained widespread use in general dental clinics. Dental CBCT offers a substantial cost advantage over standard medical CT, making it affordable for local dental practices; however, this affordability brings significant challenges related to image quality degradation, further complicated by the presence of metallic implants, which are particularly common in older patients. This paper investigates metal-induced artefacts stemming from mismatches in the forward model used in conventional reconstruction methods and explains an alternative approach that bypasses the traditional Radon transform model. Additionally, it examines both the potential and limitations of deep learning-based methods in tackling these challenges, offering insights into their effectiveness in improving image quality in low-dose dental CBCT.This article is part of the theme issue 'Frontiers of applied inverse problems in science and engineering'.
本文探讨了计算机断层扫描（CT）当前面临的挑战，并从数学角度对现有方法进行了批判性探索。具体而言，其旨在确定研究方向，以提高低剂量、经济高效的锥束CT（CBCT）系统的图像质量，这种系统最近在普通牙科诊所中得到了广泛应用。牙科CBCT与标准医学CT相比具有显著的成本优势，使当地牙科诊所能够负担得起；然而，这种可承受性带来了与图像质量下降相关的重大挑战，而金属植入物的存在进一步加剧了这一问题，金属植入物在老年患者中尤为常见。本文研究了传统重建方法中使用的正向模型不匹配所导致的金属诱导伪影，并解释了一种绕过传统拉东变换模型的替代方法。此外，本文还研究了基于深度学习的方法在应对这些挑战方面的潜力和局限性，深入探讨了它们在改善低剂量牙科CBCT图像质量方面的有效性。本文是“科学与工程中应用反问题前沿”主题系列文章的一部分。
