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Proceedings Paper

Performance modeling of a wearable brain PET (BET) camera
Author(s): C. R. Schmidtlein; J. N. Turner; M. O. Thompson; K. C. Mandal; I. Häggström; J. Zhang; J. L. Humm; D. H. Feiglin; A. Krol
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Paper Abstract

Purpose: To explore, by means of analytical and Monte Carlo modeling, performance of a novel lightweight and low-cost wearable helmet-shaped Brain PET (BET) camera based on thin-film digital Geiger Avalanche Photo Diode (dGAPD) with LSO and LaBr3 scintillators for imaging in vivo human brain processes for freely moving and acting subjects responding to various stimuli in any environment.

Methods: We performed analytical and Monte Carlo modeling PET performance of a spherical cap BET device and cylindrical brain PET (CYL) device, both with 25 cm diameter and the same total mass of LSO scintillator. Total mass of LSO in both the BET and CYL systems is about 32 kg for a 25 mm thick scintillator, and 13 kg for 10 mm thick scintillator (assuming an LSO density of 7.3 g/ml). We also investigated a similar system using an LaBr3 scintillator corresponding to 22 kg and 9 kg for the 25 mm and 10 mm thick systems (assuming an LaBr3 density of 5.08 g/ml). In addition, we considered a clinical whole body (WB) LSO PET/CT scanner with 82 cm ring diameter and 15.8 cm axial length to represent a reference system. BET consisted of distributed Autonomous Detector Arrays (ADAs) integrated into Intelligent Autonomous Detector Blocks (IADBs). The ADA comprised of an array of small LYSO scintillator volumes (voxels with base a×a: 1.0 ≤ a ≤ 2.0 mm and length c: 3.0 ≤ c ≤ 6.0 mm) with 5–65 μm thick reflective layers on its five sides and sixth side optically coupled to the matching array of dGAPDs and processing electronics with total thickness of 50 μm. Simulated energy resolution was 10.8% and 3.3% for LSO and LaBr3 respectively and the coincidence window was set at 2 ns. The brain was simulated as a sphere of uniform F-18 activity with diameter of 10 cm embedded in a center of water sphere with diameter of 10 cm.

Results: Analytical and Monte Carlo models showed similar results for lower energy window values (458 keV versus 445 keV for LSO, and 492 keV versus 485 keV for LaBr3), and for the relative performance of system sensitivity. Monte Carlo results further showed that the BET geometry had >50% better noise equivalent count (NEC) performance relative to the CYL geometry, and >1100% better performance than a WB geometry for 25 mm thick LSO and LaBr3. For 10 mm thick LaBr3 equivalent mass systems LSO (7 mm thick) performed ~40% higher NEC than LaBr3. Analytic and Monte Carlo simulations also showed that 1×1×3 mm scintillator crystals can achieve ~1.2 mm FWHM spatial resolution.

Conclusions: This study shows that a spherical cap brain PET system can provide improved NEC while preserving spatial resolution when compared to an equivalent dedicated cylindrical PET brain camera and shows greatly improved PET performance relative to a conventional whole body PET/CT. In addition, our simulations show that LSO will generally outperform LaBr3 for NEC unless the timing resolution for LaBr3 is considerably smaller than presently used for LSO, i.e. well below 300 ps.

Paper Details

Date Published: 29 March 2016
PDF: 11 pages
Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978806 (29 March 2016); doi: 10.1117/12.2217020
Show Author Affiliations
C. R. Schmidtlein, Memorial Sloan Kettering Cancer Ctr. (United States)
J. N. Turner, SUNY Binghamton (United States)
M. O. Thompson, Cornell Univ. (United States)
K. C. Mandal, Univ. of South Carolina (United States)
I. Häggström, Memorial Sloan Kettering Cancer Ctr. (United States)
J. Zhang, SUNY Upstate Medical Univ. (United States)
J. L. Humm, Memorial Sloan Kettering Cancer Ctr. (United States)
D. H. Feiglin, SUNY Upstate Medical Univ. (United States)
A. Krol, SUNY Upstate Medical Univ. (United States)


Published in SPIE Proceedings Vol. 9788:
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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