Introduction: We describe the foundation of PETRIC, an image reconstruction challenge to minimise the computational runtime of related algorithms for Positron Emission Tomography (PET). Purpose: Although several similar challenges are well-established in the field of medical imaging, there have been no prior challenges for PET image reconstruction. Methods: Participants are provided with open-source software for implementation of their reconstruction algorithm(s). We define the objective function and reconstruct "gold standard" reference images, and provide metrics for quantifying algorithmic performance. We also received and curated phantom datasets (acquired with different scanners, radionuclides, and phantom types), which we further split into training and evaluation datasets. The automated computational framework of the challenge is released as open-source software. Results: Four teams with nine algorithms in total participated in the challenge. Their contributions made use of various tools from optimisation theory including preconditioning, stochastic gradients, and artificial intelligence. While most of the submitted approaches appear very similar in nature, their specific implementation lead to a range of algorithmic performance. Conclusion: As the first challenge for PET image reconstruction, PETRIC's solid foundations allow researchers to reuse its framework for evaluating new and existing image reconstruction methods on new or existing datasets. Variant versions of the challenge have and will continue to be launched in the future.

翻译：引言：我们介绍了PETRIC的基础——一个旨在最小化正电子发射断层扫描（PET）相关算法计算运行时间的图像重建挑战。目的：尽管医学成像领域已有多个类似挑战，但此前尚无针对PET图像重建的专项挑战。方法：参与者可使用开源软件实现其重建算法。我们定义了目标函数并重建“金标准”参考图像，同时提供了量化算法性能的指标。我们还收集并整理了体模数据集（来自不同扫描仪、放射性核素及体模类型），并将其进一步划分为训练集与评估集。该挑战的自动化计算框架已作为开源软件发布。结果：共有四支团队携九种算法参与挑战。其贡献利用了优化理论中的多种工具，包括预处理、随机梯度与人工智能。尽管提交的大多数方法在本质上非常相似，但其具体实现导致了算法性能的差异。结论：作为首个PET图像重建挑战，PETRIC的坚实基础允许研究人员复用其框架，在新数据或现有数据上评估新型及现有图像重建方法。该挑战的变体版本已启动并将持续推出。