NEMA NU 1-2018 performance characterization and Monte Carlo model validation of the Cubresa Spark SiPM-based preclinical SPECT scanner

EJNMMI Physics

Original Research

Drew R. DeBay¹  Matthew E. Strugari²  Steven D. Beyea³  Kimberly D. Brewer⁴ 

[1] Biomedical Translational Imaging Centre, Halifax, NS, Canada;Cubresa Inc., Winnipeg, MB, Canada;Biomedical Translational Imaging Centre, Halifax, NS, Canada;Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada;Biomedical Translational Imaging Centre, Halifax, NS, Canada;Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada;Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada;School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada;Biomedical Translational Imaging Centre, Halifax, NS, Canada;Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada;Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada;School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada;Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada;

关键词: Molecular imaging;    Nuclear medicine;    SPECT;    Animal imaging instrumentation;    Monte Carlo method;    Computer-assisted image processing;    Imaging phantoms;   

DOI  :  10.1186/s40658-023-00555-6

 received in 2022-08-09, accepted in 2023-05-15,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe Cubresa Spark is a novel benchtop silicon-photomultiplier (SiPM)-based preclinical SPECT system. SiPMs in SPECT significantly improve resolution and reduce detector size compared to preclinical cameras with photomultiplier tubes requiring highly magnifying collimators. The NEMA NU 1 Standard for Performance Measurements of Gamma Cameras provides methods that can be readily applied or extended to characterize preclinical cameras with minor modifications. The primary objective of this study is to characterize the Spark according to the NEMA NU 1-2018 standard to gain insight into its nuclear medicine imaging capabilities. The secondary objective is to validate a GATE Monte Carlo simulation model of the Spark for use in preclinical SPECT studies.MethodsNEMA NU 1-2018 guidelines were applied to characterize the Spark’s intrinsic, system, and tomographic performance with single- and multi-pinhole collimators. Phantoms were fabricated according to NEMA specifications with deviations involving high-resolution modifications. GATE was utilized to model the detector head with the single-pinhole collimator, and NEMA measurements were employed to tune and validate the model. Single-pinhole and multi-pinhole SPECT data were reconstructed with the Software for Tomographic Image Reconstruction and HiSPECT, respectively.ResultsThe limiting intrinsic resolution was measured as 0.85 mm owing to a high-resolution SiPM array combined with a 3 mm-thick scintillation crystal. The average limiting tomographic resolution was 1.37 mm and 1.19 mm for the single- and multi-pinhole collimators, respectively, which have magnification factors near unity at the center of rotation. The maximum observed count rate was 15,400 cps, and planar sensitivities of 34 cps/MBq and 150 cps/MBq were measured at the center of rotation for the single- and multi-pinhole collimators, respectively. All simulated tests agreed well with measurement, where the most considerable deviations were below 7%.ConclusionsNEMA NU 1-2018 standards determined that a SiPM detector mitigates the need for highly magnifying pinhole collimators while preserving detailed information in projection images. Measured and simulated NEMA results were highly comparable with differences on the order of a few percent, confirming simulation accuracy and validating the GATE model. Of the collimators initially provided with the Spark, the multi-pinhole collimator offers high resolution and sensitivity for organ-specific imaging of small animals, and the single-pinhole collimator enables high-resolution whole-body imaging of small animals.

【 授权许可】

CC BY   
© The Author(s) 2023

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