Development of quantitative MRI DTI analysis tool for preterm neonate

早产儿定量MRI DTI分析工具的开发

• 批准号: 8893110
• 负责人: Kenichi Oishi
• 金额: $ 41.84万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-20 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8893110
• 关键词: Age; Anatomy; Atlases; Birth; Brain; Brain Mapping; Caring; Child; Childhood; Cognitive deficits; Databases; Deformity; Development; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Early Intervention; Gestational Age; Goals; Image; Image Analysis; Impairment; Infant; Intervention; Knowledge; Lesion; Life; Live Birth; Magnetic Resonance Imaging; Manuals; Maps; Measures; Medical; Mental disorders; Methods; Modality; Neonatal; Neonatal Intensive Care; Neurocognitive Deficit; Neurologic; Neurological outcome; Normal Range; Outcome; Population; Pregnancy; Premature Birth; Premature Infant; Process; Prognostic Marker; Reporting; Research; Signal Transduction; Staging; Structure; Survival Rate; Symptoms; Technology; Term Birth; Testing; Time; United States; Weight; base; brain volume; clinically relevant; functional outcomes; gray matter; image guided; imaging modality; improved; ischemic lesion; neonatal care; neonate; nervous system disorder; neuropsychological; outcome forecast; pediatric patients; premature; quantitative imaging; research clinical testing; tool; white matter

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.

描述（申请人提供）：我们将建立一种新生儿脑MRI量化方法来评估早产儿的异常情况。在美国，大约12%的新生儿早产（妊娠<37周），其中2%是非常早产（VPB，妊娠28-32周）。在过去十年中，早产的百分比一直在增加，部分原因是早产儿的新生儿护理得到改善。然而，大约一半的VPB婴儿可能会出现临床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尚不清楚。然而，大多数神经认知缺陷在出生后的第一年不易被发现。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。MRI是检测新生儿大脑细微解剖异常最有前途和最灵敏的方法之一。先前的脑MRI研究发现了几种异常类型与神经系统结果之间的一些相关性，但也有报告发现信号改变与神经系统结果之间没有关系。因此，目前的知识并不能证明MRI用于常规临床评估是合理的。为了优化MRI对新生儿和儿童护理的有用性，需要基于定量图像分析和功能关联的系统研究。提出的方法是基于两个核心技术的新生儿脑解剖量化：一个可变形的脑图谱与详细的解剖信息和高度弹性的拓扑保存的翘曲方法。该组合将提供176个自动分割的大脑结构的多个MR参数。该项目的目标是建立一种基于地图集的新生儿大脑自动量化方法，以评估足月等值年龄早产儿的详细解剖结构。总的假设是，我们的dti引导定量脑分析将敏感地检测出区域特异性早产儿的解剖异常。我们有四个具体目标。在目标1中，我们将创建一个用于定量脑分析的多对比（T1， t2加权和DTI）正常足月新生儿脑图谱，这将是人口的统计表示（“贝叶斯图谱”）。在Aim 2中，我们将把贝叶斯图谱与高度弹性拓扑保存的翘曲（大变形微分对称度量映射，LDDMM）相结合，用于自动脑分割并测试分割精度。在Aim 3中，我们将结合贝叶斯图谱和LDDMM对足月新生儿脑mri进行T1/T2/DTI量化。在Aim 4中，我们将把该方法应用于足月早产儿（28-36孕周出生）的大脑mri，并将其磁共振参数与足月婴儿的磁共振参数进行比较。这项研究将是为早产儿脑解剖异常的功能结果寻找早期预后指标的第一步。