基于高分辨率模型的多层平板探测器材料分解。

Yiqun Q Ma, Wenying Wang, Matt Tivnan, Junyuan Li, Minghui Lu, Jin Zhang, Josh Star-Lack, Richard E Colbeth, Wojciech Zbijewski, J Webster Stayman
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引用次数: 0

摘要

在这项工作中,我们比较了一种新的基于模型的材料分解(MBMD)方法与使用多层平板探测器的高分辨率光谱CT的标准方法。物理实验使用了一个原型双层探测器和定制的高分辨率碘增强线对幻影。采用传统的滤波反投影法(FBP)和图像域分解法(iMBMD)、无模糊建模的理想化MBMD和系统模糊建模的MBMD三种方法进行重建。我们发现两种方法都比FBP方法获得了更高的分解分辨率和更低的噪声,并且由于额外的模糊建模,bMBMD比iMBMD进一步提高了空间分辨率。这些结果表明MBMD在分辨率性能和噪声控制方面优于传统的频谱CT方法。因此,基于模型的材料分解在高分辨率光谱CT应用中具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-Resolution Model-based Material Decomposition for Multi-Layer Flat-Panel Detectors.

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.

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