Development of quantitative MRI DTI analysis tool for preterm neonate

描述（由申请人提供）：我们将建立一种新生儿脑MRI定量方法，以评估早产儿的异常。 在美国，所有新生儿中约12%早产（<37周妊娠），其中2%为极早产（VPB，28-32周妊娠）。 在过去十年中，早产的比例一直在增加，部分原因是对早产儿的新生儿护理得到了改善。 然而，约有一半的VPB婴儿可能会出现临床上明显的神经或心理障碍，如果包括细微的功能异常，这个数字可能会更高。 晚期早产儿（妊娠33-36周）的神经认知缺陷程度也未知。 然而，大多数神经认知缺陷在生命的第一年不容易被发现。 为了从早期干预中获益并开发更多针对缺陷的干预措施，我们需要在早期阶段检测和量化大脑异常的方法。 磁共振成像是最有前途的和敏感的方法来检测微妙的解剖异常，在新生儿的大脑。 以前的脑MRI研究已经发现了几种类型的异常和神经系统结果之间的一些相关性，但也有报道发现信号改变和神经系统结果之间没有关系。 因此，现有知识不能证明MRI用于常规临床评价的合理性。 为了优化MRI在新生儿和儿科护理中的实用性，需要基于定量图像分析和功能相关性的系统研究。 所提出的方法是基于两个核心技术的新生儿脑解剖结构的量化：一个可变形的大脑图谱与详细的解剖信息和高度弹性的拓扑保留的翘曲方法。 该组合将提供来自176个自动分割的大脑结构的多个MR参数。 本研究的目的是建立一种基于图谱的新生儿脑的自动定量方法，以评估足月新生儿的详细解剖结构。 总体假设是，我们的DTI引导的定量脑分析将以区域特异性方式灵敏地检测早产新生儿的解剖异常。 我们有四个具体目标。 在目标1中，我们将创建一个多对比度（T1，T2加权和DTI）正常足月新生儿脑图谱，用于定量脑分析，这将是人口的统计表示（“贝叶斯图谱”）。 在目标2中，我们将联合收割机将贝叶斯图谱与高度弹性的拓扑保持变形（大变形拓扑度量映射，LDDMM）相结合，用于自动化脑分割并测试分割准确性。 在目标3中，我们将使用贝叶斯图谱和LDDMM的组合来执行足月新生儿脑MRI的T1/T2/DTI定量。 在目标4中，我们将该方法应用于足月早产儿（出生于28-36周胎龄）的脑MRI，并将MR参数与足月婴儿的MR参数进行比较。 这项研究将是寻找早产儿脑解剖异常功能结局的早期预后指标的第一步。

DESCRIPTION (provided by applicant): We will establish a quantification method for neonatal brain MRI to evaluate the abnormalities of preterm born neonates. In the U.S., approximately 12% of all neonates are born preterm (<37 weeks gestation), with 2% of these being very preterm (VPB, 28-32 weeks gestation). The percentage of preterm births has been increasing over the last ten years, partly due to improved neonatal care for preterm infants. Nevertheless, about half of the VPB infants may develop clinically-evident neurological or psychological disorders and the number could be even higher if subtle functional abnormalities are included. The extent of neurocognitive deficits in the late preterm infants (33-36 weeks gestation) is also unknown. However, most of the neuro- cognitive deficits are not easily detected during the first year of life. To benefit from early intervention and to develop more deficits-specific interventions, we need methods to detect and quantify brain abnormalities at an early stage. MRI is one of the most promising and sensitive methods to detect subtle anatomic abnormalities in the neonatal brain. Previous brain MRI studies have found some correlations between several types of abnormalities and neurological outcomes, but there are also reports that found no relationship between signal alterations and neurological outcomes. Hence, the current knowledge does not justify the use of MRI for routine clinical evaluations. To optimize the usefulness of MRI for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is needed. The proposed method is based on two core technologies for the quantification of neonatal brain anatomy: a deformable brain atlas with detailed anatomic information and a highly elastic topology-preserved warping method. The combination will provide multiple MR parameters from 176 automatically segmented brain structures. The goals of this project are to establish an atlas-based, automated quantification method for the neonatal brain, to evaluate the detail anatomy of premature neonates at a term-equivalent age. The overall hypothesis is that our DTI-guided quantitative brain analysis will sensitively detect anatomical abnormalities of preterm born neonates in region- specific manner. We have four specific aims. In Aim 1, we will create a multi-contrast (T1-, T2-weighted, and DTI) normal-term neonatal brain atlas for quantitative brain analysis, which will be a statistical representation of the population ("Bayesian atlas"). In Aim 2, we will combine the Bayesian atlas with highly elastic topology- preserved warping (Large Deformation Diffeomorphic Metric Mapping, LDDMM) for automated brain segmentation and test the segmentation accuracy. In Aim 3, we will use the combination of the Bayesian atlas and LDDMM to perform T1/T2/DTI quantification of term neonatal brain MRIs. In Aim 4, we will apply the method to the brain MRIs from term-equivalent preterm born infants (born at 28-36 weeks gestational age) and compare the MR parameters to those in the term infants. This study will be a first step toward seeking very early prognostic indicators for functional outcomes of the anatomical brain abnormalities in preterm births.