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Post-Neutron-Tomography SPECT for 3D Isotopic Reconstruction in Bulk Samples

The non-destructive assessment of interior elemental composition and spatial distribution in bulk samples (greater than a few hundred \mu \mathrm{m}) is essential across various fields of research including archaeometry, palaeontology and cultural heritage. Traditional neutron activation analysis (N...

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Bibliographic Details
Main Authors: MacLeod, S., Bevitt, J., Zahra, D., Chacon, A., Franklin, D., Safavi-Naeini, M.
Format: Conference Proceeding
Language:English
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Summary:The non-destructive assessment of interior elemental composition and spatial distribution in bulk samples (greater than a few hundred \mu \mathrm{m}) is essential across various fields of research including archaeometry, palaeontology and cultural heritage. Traditional neutron activation analysis (NAA) techniques offer elemental identification, but lack the 3D spatial context. Similarly, advanced imaging techniques like X-ray and neutron tomography provide 3D structural information but often fall short in extrapolating internal elemental distributions. To address this issue, we propose a novel method: Post-Neutron-Tomography SPECT. Using ANSTO's thermal neutron imaging beamline (DINGO), samples are irradiated to induce temporary activation through neutron capture. The characteristic gamma lines from these decaying radionuclides can be used to identify the created isotopes. By performing single-photon emission computed tomography (SPECT) of the neutron-irradiated samples, the spatial distribution of resulting isotopes, determined using delayed gamma emission, can reconstructed through additional energy windowing techniques. Successful 3D reconstructions of copper (Cu-64) and gold (Au-198) samples demonstrate the method's potential in providing 3D isotopic distributions of bulk objects. Simulation modelling can also be used to predict and inform irradiation time and which isotopes may be present in the collected gamma spectra. Combined with structural imaging modalities, this approach enhances our capacity to estimate elemental distributions in bulk samples of unknown compositions.
ISSN:2577-0829
DOI:10.1109/NSS/MIC/RTSD57108.2024.10656616