Nasirudin Radin A, Mei Kai, Penchev Petar, Fehringer Andreas, Pfeiffer Franz, Rummeny Ernst J, Fiebich Martin, Noël Peter B
Department of Diagnostic and Interventional Radiology, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany.
Chair for Biomedical Physics and Institute for Medical Engineering, Technische Universität München, James-Franck-Strasse 1, 85748 Garching, Germany.
PLoS One. 2015 May 8;10(5):e0124831. doi: 10.1371/journal.pone.0124831. eCollection 2015.
The exciting prospect of Spectral CT (SCT) using photon-counting detectors (PCD) will lead to new techniques in computed tomography (CT) that take advantage of the additional spectral information provided. We introduce a method to reduce metal artifact in X-ray tomography by incorporating knowledge obtained from SCT into a statistical iterative reconstruction scheme. We call our method Spectral-driven Iterative Reconstruction (SPIR).
The proposed algorithm consists of two main components: material decomposition and penalized maximum likelihood iterative reconstruction. In this study, the spectral data acquisitions with an energy-resolving PCD were simulated using a Monte-Carlo simulator based on EGSnrc C++ class library. A jaw phantom with a dental implant made of gold was used as an object in this study. A total of three dental implant shapes were simulated separately to test the influence of prior knowledge on the overall performance of the algorithm. The generated projection data was first decomposed into three basis functions: photoelectric absorption, Compton scattering and attenuation of gold. A pseudo-monochromatic sinogram was calculated and used as input in the reconstruction, while the spatial information of the gold implant was used as a prior. The results from the algorithm were assessed and benchmarked with state-of-the-art reconstruction methods.
Decomposition results illustrate that gold implant of any shape can be distinguished from other components of the phantom. Additionally, the result from the penalized maximum likelihood iterative reconstruction shows that artifacts are significantly reduced in SPIR reconstructed slices in comparison to other known techniques, while at the same time details around the implant are preserved. Quantitatively, the SPIR algorithm best reflects the true attenuation value in comparison to other algorithms.
It is demonstrated that the combination of the additional information from Spectral CT and statistical reconstruction can significantly improve image quality, especially streaking artifacts caused by the presence of materials with high atomic numbers.
使用光子计数探测器（PCD）的光谱CT（SCT）的令人兴奋的前景将带来计算机断层扫描（CT）中的新技术，这些技术利用所提供的额外光谱信息。我们引入一种方法，通过将从SCT获得的知识纳入统计迭代重建方案来减少X射线断层扫描中的金属伪影。我们将我们的方法称为光谱驱动迭代重建（SPIR）。
所提出的算法由两个主要部分组成：材料分解和惩罚最大似然迭代重建。在本研究中，使用基于EGSnrc C++类库的蒙特卡罗模拟器模拟了使用能量分辨PCD的光谱数据采集。在本研究中，将带有由金制成的牙种植体的颌骨模型用作对象。总共分别模拟了三种牙种植体形状，以测试先验知识对算法整体性能的影响。首先将生成的投影数据分解为三个基函数：光电吸收、康普顿散射和金的衰减。计算伪单色正弦图并将其用作重建的输入，而金植入物的空间信息用作先验。用最先进的重建方法对算法的结果进行评估和基准测试。
分解结果表明，任何形状的金植入物都可以与模型的其他组件区分开来。此外，惩罚最大似然迭代重建的结果表明，与其他已知技术相比，SPIR重建切片中的伪影显著减少，同时植入物周围的细节得以保留。定量地说，与其他算法相比，SPIR算法最能反映真实的衰减值。
结果表明，光谱CT的额外信息与统计重建的结合可以显著提高图像质量，特别是由高原子序数材料的存在引起的条纹伪影。
