论文标题

重建具有稀疏参数算法的多个伽马射点源的位置和强度

Reconstructing the Position and Intensity of Multiple Gamma-Ray Point Sources with a Sparse Parametric Algorithm

论文作者

Vavrek, Jayson R., Hellfeld, Daniel, Bandstra, Mark S., Negut, Victor, Meehan, Kathryn, Vanderlip, William J., Cates, Joshua W., Pavlovsky, Ryan, Quiter, Brian J., Cooper, Reynold J., Joshi, Tenzing H. Y.

论文摘要

我们提出了添加点源定位(APSL)的实验证明,这是一种稀疏的参数成像算法,可重建多个伽马射点源的3D位置和活动。使用手持式伽马射线检测器阵列和多达四个$ 8 $ $ $ $ $ CI $^{137} $ CS伽马射线源,我们在室内实验室环境中进行了源搜索和源搜索和源分离实验。在大多数源搜索测量值中,APSL重建了$ {\ sim} 20 $ cm的位置精度的正确数量的来源,并且给定了两到三分钟的测量时间(对于$ {\ sim} 20 $ cm} $ {\ sim} 20 $ cm} $ {\ sim} 20 \%$的活动准确性(未签名)20 \%$。在可以自由移动环境的探测器的源分离测量中,APSL能够解决两个源,仅给定$ {\ sim} 60 $ s的测量时间,分开了$ 75 $ cm或更多。在这些源分离测量中,APSL产生的较大的总活动错误为$ {\ sim} 40 \%$,但获得了源分离距离准确至$ 15 $ cm。我们还将APSL结果与传统的最大似然期望最大化(ML-EM)重建进行了比较,并在ML-EM上使用APSL证明了提高图像的准确性和可解释性。这些结果表明,APSL能够使用现有检测器硬件的测量结果准确地重建伽马射线源位置和活动。

We present an experimental demonstration of Additive Point Source Localization (APSL), a sparse parametric imaging algorithm that reconstructs the 3D positions and activities of multiple gamma-ray point sources. Using a handheld gamma-ray detector array and up to four $8$ $μ$Ci $^{137}$Cs gamma-ray sources, we performed both source-search and source-separation experiments in an indoor laboratory environment. In the majority of the source-search measurements, APSL reconstructed the correct number of sources with position accuracies of ${\sim}20$ cm and activity accuracies (unsigned) of ${\sim}20\%$, given measurement times of two to three minutes and distances of closest approach (to any source) of ${\sim}20$ cm. In source-separation measurements where the detector could be moved freely about the environment, APSL was able to resolve two sources separated by $75$ cm or more given only ${\sim}60$ s of measurement time. In these source-separation measurements, APSL produced larger total activity errors of ${\sim}40\%$, but obtained source separation distances accurate to within $15$ cm. We also compare our APSL results against traditional Maximum Likelihood-Expectation Maximization (ML-EM) reconstructions, and demonstrate improved image accuracy and interpretability using APSL over ML-EM. These results indicate that APSL is capable of accurately reconstructing gamma-ray source positions and activities using measurements from existing detector hardware.

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