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: 8107915
• 负责人: Kenichi Oishi
• 金额: $ 47.64万
• 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 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8107915
• 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 treatment; Gestational Age; Goals; Image; Image Analysis; Impairment; Infant; Intervention; Knowledge; Lesion; Life; Live Birth; Magnetic Resonance Imaging; Manuals; Maps; Measures; Medical; Mental disorders; Methods; Metric; Modality; Neonatal; Neonatal Intensive Care; Neurocognitive Deficit; Neurologic; Neurological outcome; Normal Range; Outcome; Patients; Population; Pregnancy; Premature Birth; Premature Infant; Process; 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; imaging modality; improved; ischemic lesion; neonate; nervous system disorder; neuropsychological; outcome forecast; premature; prognostic indicator; 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. PUBLIC HEALTH RELEVANCE: We will establish an automated quantification method for neonatal brain MRI to evaluate the brain anatomical abnormalities of preterm born neonates. The number of very preterm born babies is increasing in the US, partly due to improved neonatal intensive care for these babies, and about half of these infants develop neurological or psychiatric disorders. We believe that this proposed MRI method will improve the diagnosis and hence early intervention for treatments of preterm born neonates and pediatric patients.

描述（由申请人提供）：我们将为新生儿大脑MRI建立一种定量方法，以评估早产诞生的新生儿的异常。 在美国，所有新生儿中约有12％是早产（妊娠<37周），其中2％是早产（VPB，妊娠28-32周）。 在过去的十年中，早产的百分比一直在增加，部分原因是对早产儿的新生儿护理的改善。 然而，大约一半的VPB婴儿可能会出现临床上的神经或心理疾病，如果包括微妙的功能异常，数量可能会更高。 早产儿（33-36周妊娠）的神经认知缺陷程度也未知。 但是，在生命的第一年，大多数神经认知缺陷都不容易发现。 为了从早期干预中受益并开发出更多缺陷的干预措施，我们需要在早期检测和量化脑异常的方法。 MRI是检测新生儿大脑中微妙的解剖异常的最有前途和敏感的方法之一。 先前的大脑MRI研究发现，几种类型的异常和神经系统结果之间存在一些相关性，但也有报道发现信号改变与神经系统结局之间没有关系。 因此，当前的知识不能证明将MRI用于常规临床评估。 为了优化MRI对新生儿和小儿护理的有用性，需要基于定量图像分析和功能相关性的系统研究。 该方法基于两种用于定量新生儿脑解剖结构的核心技术：具有详细解剖信息和高度弹性拓扑保存的翘曲方法的可变形脑图集。 该组合将提供来自176个自动分割大脑结构的多个MR参数。 该项目的目标是为新生儿大脑建立一种基于ATLA的自动定量方法，以评估期限年龄的早产新生儿的细节解剖结构。 总体假设是，我们的DTI指导定量脑分析将以区域特异性方式敏感地检测早产新生儿的解剖异常。 我们有四个具体的目标。 在AIM 1中，我们将创建一种多对比度（T1-，T2加权和DTI）正常的新生儿脑图集，以进行定量大脑分析，这将是人群的统计表示（“ Bayesian Atlas”）。 在AIM 2中，我们将将贝叶斯地图集与高弹性拓扑的翘曲（大变形差异度量映射，LDDMM）相结合，以进行自动脑分割并测试分割精度。 在AIM 3中，我们将使用贝叶斯地图集和LDDMM的组合来执行新生儿脑MRIS术语的T1/T2/DTI定量。 在AIM 4中，我们将从学期等效的早产儿（出生于28-36周胎龄）将方法应用于大脑MRI，并将MR参数与婴儿术语中的MR参数进行比较。 这项研究将是寻求早期预后指标的第一步，以解决早产中解剖学脑异常的功能结果。 公共卫生相关性：我们将为新生儿大脑MRI建立一种自动定量方法，以评估早产新生儿的大脑解剖异常。 在美国，非常早产的婴儿的数量正在增加，部分原因是这些婴儿的新生儿重症监护病得到了改善，这些婴儿中约有一半患有神经或精神病。 我们认为，这种提出的MRI方法将改善诊断，从而早期干预早产新生儿和小儿患者。