Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA; School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
Neuroimage. 2019 Jan 15;185:906-925. doi: 10.1016/j.neuroimage.2018.03.042. Epub 2018 Mar 21.
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
人类大脑在出生后的最初几年经历着结构、功能和连接的动态且关键的发育过程。非侵入性婴儿脑部磁共振成像（MRI）的应用日益广泛，为准确、可靠地描绘正常和异常生长的早期大脑发育轨迹提供了前所未有的机会。然而，与成人脑部 MRI 图像相比，婴儿脑部 MRI 图像通常显示出组织对比度降低（尤其是在 6 个月左右）、组织内强度变化大以及区域异质性和动态变化等问题。因此，现有的针对成人大脑开发的计算工具通常不适用于婴儿脑部 MRI 图像处理。为了解决这些挑战，已经提出了许多针对婴儿大脑的计算方法，用于婴儿大脑的计算神经解剖学。在这篇综述中，我们全面回顾了用于婴儿脑 MRI 处理和分析的最新计算方法，这些方法促进了我们对早期产后大脑发育的理解。我们还总结了现有的专门针对婴儿的资源，包括 MRI 数据集、计算工具、重大挑战和脑图谱。最后，我们讨论了当前研究的局限性，并提出了潜在的未来研究方向。
