Ma Yiqun Q, Wang Wenying, Tivnan Matt, Li Junyuan, Lu Minghui, Zhang Jin, Star-Lack Josh, Colbeth Richard E, Zbijewski Wojciech, Stayman J Webster
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205.
Varex Imaging Corporation, 683 River Oaks Parkway, San Jose, CA 95134.
Conf Proc Int Conf Image Form Xray Comput Tomogr. 2020 Aug;2020:62-64.
In this work we compare a novel model-based material decomposition (MBMD) approach against a standard approach in high-resolution spectral CT using multi-layer flat-panel detectors. Physical experiments were conducted using a prototype dual-layer detector and a custom high-resolution iodine-enhanced line-pair phantom. Reconstructions were performed using three methods: traditional filtered back-projection (FBP) followed by image-domain decomposition, idealized MBMD with no blur modeling (iMBMD), and MBMD with system blur modeling (bMBMD). We find that both MBMD methods yielded higher resolution decompositions with lower noise than the FBP method, and that bMBMD further improves spatial resolution over iMBMD due to the additional blur modeling. These results demonstrate the advantages of MBMD in resolution performance and noise control over traditional methods for spectral CT. Model-based material decomposition hence has great potential in high-resolution spectral CT applications.
在这项工作中，我们将一种基于模型的新型材料分解（MBMD）方法与使用多层平板探测器的高分辨率光谱CT中的标准方法进行了比较。使用原型双层探测器和定制的高分辨率碘增强线对体模进行了物理实验。使用三种方法进行重建：传统的滤波反投影（FBP），然后进行图像域分解；不进行模糊建模的理想化MBMD（iMBMD）；以及进行系统模糊建模的MBMD（bMBMD）。我们发现，两种MBMD方法都能产生比FBP方法更高分辨率且噪声更低的分解结果，并且由于额外的模糊建模，bMBMD在空间分辨率上比iMBMD有进一步提高。这些结果证明了MBMD在光谱CT分辨率性能和噪声控制方面优于传统方法。因此，基于模型的材料分解在高分辨率光谱CT应用中具有巨大潜力。
