Abstract

Brain PET is a powerful imaging modality that directly reflects cerebral metabolic activity, making it an essential tool for human brain research. However, conventional brain PET typically requires subjects to remain stationary in a sitting or supine position during scanning. This study evaluated the performance of SmartBrain, a wearable brain PET system for real-time imaging. Methods: SmartBrain uses 192 detectors, with each detector consisting of a 6 × 6 lutetium–yttrium oxyorthosilicate crystal (3 × 3 × 5 mm³) array with a 3 × 3 silicon photomultiplier array. We evaluated the physical performance of SmartBrain in accordance with the National Electrical Manufacturers Association (NEMA) NU 2-2018 standard. In addition, we performed 18F-FDG imaging using a custom Hoffman brain phantom and a multilayer Derenzo phantom. Dynamic rat images and the ¹⁸F-FDG images from a healthy volunteer are presented. Results: Spatial resolution is 2.29 mm in the center of the field of view. The sensitivity was 720.2 cps/MBq. The peak noise-equivalent count rate was 4.67 kcps at 10.1 kBq/mL, and the scatter fraction was 29.5%. The NEMA image-quality contrast recovery coefficients ranged from 72.9% (10-mm sphere) to 89.4% (37-mm sphere), and background variability was 11.3% at a contrast ratio of 9.4:1. The time-of-flight resolution was 234 ps, and the energy resolution was 10.8%. SmartBrain showed that the main structures of the custom Hoffman brain phantom could be resolved and demonstrated the ability to separate rods as small as 1.7 mm. Conclusion: SmartBrain clearly demonstrated brain structures, confirming its suitability for clinical brain research. Moreover, as a wearable and mobile PET platform, it offers unique opportunities for naturalistic brain imaging and future clinical applications in the diagnosis and management of neurologic disorders.

Title

Performance Evaluation of SmartBrain: A Wearable PET System for Human Brain Imaging

Authors

Han Liu, Wenkang Qu, Da Liang, Xin Yu, Yuejie Lin, Haoyu Zou, Siyuan Han, Zhijun Zhao, Ying Lin, Xiaoyin Zhang, Jinyong Tao, Wenbin Li, Huiping Zhao, Yibin Zhang, Gongning Luo, Ningyi Jiang and Qiyu Peng

Journal Information

Journal of Nuclear Medicine(2026)

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