瘦肌肉质量（LMM）是人类健康的一个重要方面。颞肌厚度是一种很有前途的 LMM 标记，但由于其正常生长轨迹和参考范围未知以及缺乏标准化测量，其实用性有限。在这里，我们开发了一个自动化深度学习管道，可以通过常规脑磁共振成像 (MRI) 准确测量颞肌厚度 (iTMT)。我们将 iTMT 应用于 23,876 名 4 至 35 岁健康受试者的 MRI，并生成带有百分位数的性别特异性 iTMT 正常生长图表。我们发现 iTMT 与特定的生理特征相关，包括热量摄入、体力活动、性激素水平和恶性肿瘤的存在。我们在多个人口群体和患有脑肿瘤的儿童中验证了 iTMT，并证明了个体化纵向监测的可行性。 iTMT 管道提供了对人类发育过程中颞肌生长的前所未有的见解，并支持使用 LMM 跟踪来为临床决策提供信息。© 2023。作者。

Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.© 2023. The Author(s).