Lee Donghyeon, Yun Sungho, Soh Jeongtae, Lim Sunho, Kim Hyoyi, Cho Seungryong
Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
KAIST Institutes for ITC, AI, and HST, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
J Xray Sci Technol. 2022;30(3):549-566. doi: 10.3233/XST-211054.
Dual-energy computed tomography (DECT) is a widely used and actively researched imaging modality that can estimate the physical properties of an object more accurately than single-energy CT (SECT). Recently, iterative reconstruction methods called one-step methods have received attention among various approaches since they can resolve the intermingled limitations of the conventional methods. However, the one-step methods typically have expensive computational costs, and their material decomposition performance is largely affected by the accuracy in the spectral coefficients estimation.
In this study, we aim to develop an efficient one-step algorithm that can effectively decompose into the basis material maps and is less sensitive to the accuracy of the spectral coefficients.
By use of a new loss function that employs the non-linear forward model and the weighted squared errors, we propose a one-step reconstruction algorithm named generalized simultaneous algebraic reconstruction technique (GSART). The proposed algorithm was compared with the image-domain material decomposition and other existing one-step reconstruction algorithm.
In both simulation and experimental studies, we demonstrated that the proposed algorithm effectively reduced the beam-hardening artifacts thereby increasing the accuracy in the material decomposition.
The proposed one-step reconstruction for material decomposition in dual-energy CT outperformed the image-domain approach and the existing one-step algorithm. We believe that the proposed method is a practically very useful addition to the material-selective image reconstruction field.
双能计算机断层扫描(DECT)是一种广泛应用且正在积极研究的成像方式,它比单能CT(SECT)能更准确地估计物体的物理特性。近来,被称为一步法的迭代重建方法在各种方法中受到关注,因为它们能够解决传统方法交织在一起的局限性。然而,一步法通常具有高昂的计算成本,并且其物质分解性能在很大程度上受光谱系数估计准确性的影响。
在本研究中,我们旨在开发一种高效的一步算法,该算法能够有效地分解为基物质图,并且对光谱系数的准确性不太敏感。
通过使用一种采用非线性前向模型和加权平方误差的新损失函数,我们提出了一种名为广义同时代数重建技术(GSART)的一步重建算法。将所提出的算法与图像域物质分解和其他现有的一步重建算法进行比较。
在模拟和实验研究中,我们都证明了所提出的算法有效地减少了束硬化伪影,从而提高了物质分解的准确性。
所提出的用于双能CT中物质分解的一步重建方法优于图像域方法和现有的一步算法。我们相信所提出的方法是物质选择性图像重建领域中一项非常实用的补充。