Wang Wenying, Tivnan Matthew, Gang Grace J, Ma Yiqun, Cao Qian, Lu Minghui, Star-Lack Josh, Colbeth Richard E, Zbijewski Wojciech, Stayman J Webster
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205.
Varex Imaging Corp., 683 River Oaks Pkwy, San Jose, CA 95134.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11312. doi: 10.1117/12.2549549. Epub 2020 Mar 16.
In this work, we present a novel model-based material decomposition (MBMD) approach for x-ray CT that includes system blur in the measurement model. Such processing has the potential to extend spatial resolution in material density estimates - particularly in systems where different spectral channels exhibit different spatial resolutions. We illustrate this new approach for a dual-layer detector x-ray CT and compare MBMD algorithms with and without blur in the reconstruction forward model. Both qualitative and quantitative comparisons of performance with and without blur modeling are reported. We find that blur modeling yields images with better recovery of high-resolution structures in an investigation of reconstructed line pairs as well as lower cross-talk bias between material bases that is ordinarily found due to mismatches in spatial resolution between spectral channels. The extended spatial resolution of the material decompositions has potential application in a range of high-resolution clinical tasks and spectral CT systems where spectral channels exhibit different spatial resolutions.
在这项工作中，我们提出了一种用于X射线计算机断层扫描（CT）的基于模型的新型材料分解（MBMD）方法，该方法在测量模型中纳入了系统模糊。这种处理方式有潜力提高材料密度估计中的空间分辨率，特别是在不同光谱通道具有不同空间分辨率的系统中。我们展示了这种用于双层探测器X射线CT的新方法，并比较了重建前向模型中有无模糊的MBMD算法。报告了有无模糊建模的性能的定性和定量比较。我们发现，在对重建线对的研究中，模糊建模产生的图像能更好地恢复高分辨率结构，并且在材料基之间的串扰偏差更低，这种串扰偏差通常是由于光谱通道之间空间分辨率不匹配而产生的。材料分解的扩展空间分辨率在一系列高分辨率临床任务和光谱通道具有不同空间分辨率的光谱CT系统中具有潜在应用。
