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Initial performance studies of a wearable brain positron emission tomography camera based on autonomous thin-film digital Geiger avalanche photodiode arrays
Memorial Sloan Kettering Cancer Center.
State University of New York at Binghamton.
Cornell University.
University of South Carolina.
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2017 (English)In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 4, no 1, article id 011003Article in journal (Refereed) Published
Abstract [en]

Using analytical and Monte Carlo modeling, we explored performance of a lightweight wearable helmet-shaped brain positron emission tomography (PET), or BET camera, based on thin-film digital Geiger avalanche photodiode arrays with Lutetium-yttrium oxyorthosilicate (LYSO) or LaBr3 scintillators for imaging in vivo human brain function of freely moving and acting subjects. We investigated a spherical cap BET and cylindrical brain PET (CYL) geometries with 250-mm diameter. We also considered a clinical whole-body (WB) LYSO PET/CT scanner. The simulated energy resolutions were 10.8% (LYSO) and 3.3% (LaBr3), and the coincidence window was set at 2 ns. The brain was simulated as a water sphere of uniform F-18 activity with a radius of 100 mm. We found that BET achieved >40% better noise equivalent count (NEC) performance relative to the CYL and >800% than WB. For 10-mm-thick LaBr3 equivalent mass systems, LYSO (7-mm thick) had similar to 40% higher NEC than LaBr3. We found that 1 x 1 x 3 mm scintillator crystals achieved similar to 1.1 mm full-width-half-maximum spatial resolution without parallax errors. Additionally, our simulations showed that LYSO generally outperformed LaBr3 for NEC unless the timing resolution for LaBr3 was considerably smaller than that presently used for LYSO, i.e., well below 300 ps.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2017. Vol. 4, no 1, article id 011003
Keywords [en]
Positron emission tomography, Sensors, Scintillators, Brain, Photons, Imaging systems, Neuroimaging
National Category
Medical Equipment Engineering
Identifiers
URN: urn:nbn:se:umu:diva-133937DOI: 10.1117/1.JMI.4.1.011003ISI: 000392230300002PubMedID: 27921074OAI: oai:DiVA.org:umu-133937DiVA, id: diva2:1090086
Available from: 2017-04-21 Created: 2017-04-21 Last updated: 2018-06-09Bibliographically approved

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Häggström, Ida

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