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结合光谱CT采集方法用于高灵敏度材料分解

Combining Spectral CT Acquisition Methods for High-Sensitivity Material Decomposition.

作者信息

Tivnan Matthew, Wang Wenying, Gang Grace J, Liapi Eleni, Noël Peter, Stayman J Webster

机构信息

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205.

Department of Radiology, Johns Hopkins University, Baltimore, MD, 21205.

出版信息

Proc SPIE Int Soc Opt Eng. 2020 Feb;11312. doi: 10.1117/12.2550025. Epub 2020 Mar 16.

Abstract

Quantitative estimation of contrast agent concentration is made possible by spectral CT and material decomposition. There are several approaches to modulate the sensitivity of the imaging system to obtain the different spectral channels required for decomposition. Spectral CT technologies that enable this varied sensitivity include source kV-switching, dual-layer detectors, and source-side filtering (e.g., tiled spatial-spectral filters). In this work, we use an advanced physical model to simulate these three spectral CT strategies as well as hybrid acquisitions using all combinations of two or three strategies. We apply model-based material decomposition to a water-iodine phantom with iodine concentrations from 0.1 to 5.0 mg/mL. We present bias-noise plots for the different combinations of spectral techniques and show that combined approaches permit diversity in spectral sensitivity and improve low concentration imaging performance relative to the those strategies applied individually. Better ability to estimate low concentrations of contrast agent has the potential to reduce risks associated with contrast administration (by lowering dosage) or to extend imaging applications into targets with much lower uptake.

摘要

通过光谱CT和物质分解可以实现造影剂浓度的定量估计。有几种方法可以调节成像系统的灵敏度,以获得分解所需的不同光谱通道。能够实现这种不同灵敏度的光谱CT技术包括源kV切换、双层探测器和源侧滤波(例如,平铺式空间光谱滤波器)。在这项工作中,我们使用先进的物理模型来模拟这三种光谱CT策略以及使用两种或三种策略的所有组合的混合采集。我们将基于模型的物质分解应用于碘浓度为0.1至5.0 mg/mL的水碘模型。我们展示了不同光谱技术组合的偏差-噪声图,并表明与单独应用的策略相比,组合方法允许光谱灵敏度的多样性,并提高了低浓度成像性能。更好地估计低浓度造影剂的能力有可能降低与造影剂给药相关的风险(通过降低剂量),或将成像应用扩展到摄取量低得多的目标。

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1
Optimized Spatial-Spectral CT for Multi-Material Decomposition.用于多物质分解的优化空间光谱CT
Proc SPIE Int Soc Opt Eng. 2019 Jun;11072. doi: 10.1117/12.2534333. Epub 2019 May 28.
2
Local response prediction in model-based CT material decomposition.基于模型的CT物质分解中的局部响应预测
Proc SPIE Int Soc Opt Eng. 2019 Jun;11072. doi: 10.1117/12.2534437. Epub 2019 May 28.

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