Fiber based fast sparse sampling x-ray luminescence computed tomography

Wei Zhang; Michael Lun; Changqing Li

doi:10.1117/12.2252996

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15 February 2017 Fiber based fast sparse sampling x-ray luminescence computed tomography

Wei Zhang, Michael Lun, Changqing Li

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Proceedings Volume 10057, Multimodal Biomedical Imaging XII; 1005704 (2017) https://doi.org/10.1117/12.2252996
Event: SPIE BiOS, 2017, San Francisco, California, United States

Abstract

Super fine collimated x-ray beam based x-ray luminescence computed tomography (XLCT) has the potential to reconstruct the deeply embedded targets with a spatial resolution of hundreds of micrometers. However, due to the low x-ray photon utilization efficiency and low optical signal sensitivity of the electron multiplying charge coupled device (EMCCD) camera, XLCT usually requires a long measurement time. To overcome this limitation, we propose a fiber based, fast XLCT design, in which optical fiber bundles are applied to collect the emitted optical photons on the phantom surface. Highly sensitive photomultiplier tubes (PMT) with a cooling unit and pre-amplifier are used to measure the photons from the fiber bundles. The PMT outputs are collected by a high-speed data acquisition board. A linear scan is estimated to take about 130 seconds, thus for an XLCT scan with 6 projections, we require 13 minutes for each section, which makes it feasible to have a whole body scan of XLCT. To validate our design, numerical simulations and phantom experiments have been performed. In numerical simulation studies, we have investigated the effect of the number of optical fiber bundle on the XLCT reconstruction. We found that one optical fiber bundle is sufficient to reconstruct the deeply embedded targets if measurements from 6 projections are used. Phantom experiments with multiple targets have been performed to validate the proposed fast XLCT imaging.

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Wei Zhang, Michael Lun, and Changqing Li "Fiber based fast sparse sampling x-ray luminescence computed tomography", Proc. SPIE 10057, Multimodal Biomedical Imaging XII, 1005704 (15 February 2017); https://doi.org/10.1117/12.2252996

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