Neonatal-hypoxic ischemic encephalopathy (HIE) is the leading cause of acquired neonatal brain injury with the risk of developing serious neurologic sequelae and death. An accurate and robust prediction of short- and long-term outcomes may provide clinicians and families with fundamental evidence for their decision-making, the design of treatment strategies, and the discussion of developmental intervention plans after discharge. Diffusion tensor imaging (DTI) is one of the most powerful neuroimaging tools with which to predict the prognosis of neonatal HIE by providing microscopic features that cannot be assessed by conventional MRI. DTI provides various scalar measures that represent the properties of the tissue, such as fractional anisotropy (FA) and mean diffusivity (MD). Since the characteristics of the diffusion of water molecules represented by these measures are affected by the microscopic cellular and extracellular environment, such as the orientation of structural components and cell density, they are often used to study the normal developmental trajectory of the brain and as indicators of various tissue damage, including HIE-related pathologies, such as cytotoxic edema, vascular edema, inflammation, cell death, and Wallerian degeneration. Previous studies have demonstrated widespread alteration in DTI measurements in severe cases of HIE and more localized changes in neonates with mild-to-moderate HIE. In an attempt to establish cutoff values to predict the occurrence of neurological sequelae, MD and FA measured in the corpus callosum (CC), thalamus, basal ganglia, corticospinal tract (CST), and frontal white matter have proven to have an excellent ability to predict severe neurological outcomes. In addition, a recent study has suggested that a data-driven, unbiased approach using machine-learning techniques on features obtained from whole-brain image quantification may accurately predict the prognosis of HIE, including for mild-to-moderate cases. Further efforts are needed to overcome current challenges, such as MRI infrastructure, diffusion modeling methods, and data harmonization for clinical application. In addition, external validation of predictive models is essential for clinical application of DTI to prognostication.

中文翻译：

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用于新生儿缺氧缺血性脑病预后预测的弥散 MRI 量化。

新生儿缺氧缺血性脑病 (HIE) 是获得性新生儿脑损伤的主要原因，有发生严重神经系统后遗症和死亡的风险。对短期和长期结果的准确而可靠的预测可以为临床医生和家庭的决策、治疗策略的设计以及出院后发展干预计划的讨论提供基本证据。弥散张量成像 (DTI) 是最强大的神经影像学工具之一，可通过提供传统 MRI 无法评估的显微特征来预测新生儿 HIE 的预后。DTI 提供了代表组织特性的各种标量度量，例如分数各向异性 (FA) 和平均扩散率 (MD)。由于这些指标所代表的水分子扩散特性受微观细胞和细胞外环境的影响，例如结构成分的方向和细胞密度，因此它们常被用来研究大脑的正常发育轨迹，并作为指标各种组织损伤，包括 HIE 相关病理，如细胞毒性水肿、血管性水肿、炎症、细胞死亡和沃勒变性。以前的研究表明，在严重的 HIE 病例中，DTI 测量值发生了广泛变化，而在轻度至中度 HIE 的新生儿中，局部变化更为明显。为了建立临界值以预测神经后遗症的发生，MD 和 FA 在胼胝体 (CC)、丘脑、基底神经节、皮质脊髓束 (CST) 中测量，和额叶白质已被证明具有预测严重神经系统结果的出色能力。此外，最近的一项研究表明，对从全脑图像量化获得的特征使用机器学习技术的数据驱动、无偏见的方法可以准确预测 HIE 的预后，包括轻度至中度病例。需要进一步努力来克服当前的挑战，例如 MRI 基础设施、扩散建模方法和临床应用的数据协调。此外，预测模型的外部验证对于 DTI 的临床应用预测至关重要。对从全脑图像量化获得的特征使用机器学习技术的无偏方法可以准确预测 HIE 的预后，包括轻度至中度病例。需要进一步努力来克服当前的挑战，例如 MRI 基础设施、扩散建模方法和临床应用的数据协调。此外，预测模型的外部验证对于 DTI 的临床应用预测至关重要。对从全脑图像量化获得的特征使用机器学习技术的无偏方法可以准确预测 HIE 的预后，包括轻度至中度病例。需要进一步努力来克服当前的挑战，例如 MRI 基础设施、扩散建模方法和临床应用的数据协调。此外，预测模型的外部验证对于 DTI 的临床应用预测至关重要。