Abstract

Objective. Depth-of-interaction (DOI) information is essential for improving the spatial resolution in positron emission tomography (PET) imaging. Existing deep learning–based DOI discrimination methods rely on side-irradiation measurements for each detector to obtain labeled data, making them impractical for system-level calibration. Approach. This study proposes a system-level DOI discrimination method based on semi-supervised domain adaptation (SSDA) for a brain-dedicated DOI-PET scanner. The method requires only side-irradiation data from a small number of detectors (even one) as labeled data, greatly reducing calibration time and labor. It was evaluated using a two-layer lutetium yttrium oxyorthosilicate detector array with different pixel sizes: the top layer is a 16 16 array of 1.53 1.53 5 mm crystals, and the bottom layer is an 8 8 array of 3 3 15 mm crystals. Performance was further evaluated on a PET prototype scanner comprising 72 detectors, through assessments of flood image quality and image spatial resolution of a Na point source positioned at different radial offsets. Main Results. The method achieved an accuracy of 98.21% on a benchmark established using fully supervised learning, with flood image quality of 4.80 0.47 and 6.84 0.53 for the top and bottom layers, and of 0.04 0.03 and 0.07 0.04, respectively. In the flood image quality evaluation of the 72-detector PET prototype scanner, it maintained stable and high performance comparable to fully supervised learning while using side-irradiation data from only one detector as labeled data, achieving of 4.82 0.55 and 6.65 0.65 and identifying merely 0.80% misclassified crystal clusters. In the spatial resolution evaluation, the method achieved an average radial spatial resolution of mm for the two-level DOI configuration and mm for the four-level DOI configuration. Significance. With its low cost, high accuracy, and strong scalability, the proposed method provides an efficient and practical solution for system-level DOI calibration in next-generation high-performance brain-dedicated PET scanners.

Title

A system-level DOI discrimination method based on SSDA for a brain-dedicated DOI-PET scanner

Authors

Xiaolong Jiang, Xiangtao Zeng, Hang Yang, Zhang Chen, Sheng Huang, Wenjie Huang, Wen He, Ming Niu and Zheng Gu*

Journal Information

Physics in Medicine & Biology (2026)

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