Continued Development of Infant Brain Analysis Tools
婴儿大脑分析工具的持续开发
基本信息
- 批准号:9755508
- 负责人:
- 金额:$ 46.53万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-08-03 至 2023-04-30
- 项目状态:已结题
- 来源:
- 关键词:2 year oldAddressAdultAgeAge-MonthsAppearanceAtlasesAutomobile DrivingBase of the BrainBrainComplexComputer softwareDataData SetDevelopmentDocumentationEducation and OutreachEnvironmentExhibitsGoalsGrowthHumanImageInfantInfant DevelopmentJointsLabelLearningLettersLifeMRI ScansMagnetic Resonance ImagingMapsMeasurementMethodsMultimodal ImagingNational Institute of Mental HealthNeurodevelopmental DisorderOnline SystemsPaperPlayProbabilityProcessPublicationsReportingResearchResearch PersonnelRoleSeriesShapesSoftware ToolsSourceSpeedStrategic PlanningStructureSurfaceT2 weighted imagingTechniquesTimeTissuesTrainingVariantadaptive learningbasecomputerized toolsconnectomecraniumcritical perioddata acquisitiondiffusion weightedexperiencefile formatgray matterimaging modalityimaging studyimprovedinnovationinteroperabilitynovelpostnatalrandom forestreconstructiontoolwhite matter
项目摘要
Continued Development of Infant Brain Analysis Tools
Abstract:
The increasing availability of infant brain MR images, such as those that will be collected through the Baby
Connectome Project (BCP, on which Dr. Shen is a Co-PI, focusing on data acquisition), affords
unprecedented opportunities for precise charting of dynamic early brain developmental trajectories in
understanding normative and aberrant growth. However, to fully benefit from these datasets, a major barrier
that needs to be overcome is the critical lacking of computational tools for accurate processing and analysis of
infant MRI data, which typically exhibit poor tissue contrast, large within tissue intensity variation, and
regionally-heterogeneous and dynamic changes. To fill this critical gap, in 2012 we pioneered in creating an
infant-centric MRI processing software package, called infant Brain Extraction and Analysis Tool (iBEAT),
and a set of infant-specific atlases, called UNC 0-1-2 Infant Atlases, and further made them freely and publicly
available via NITRC. Over the last 4 years, iBEAT and UNC 0-1-2 Infant Atlases have been downloaded 2900+
and 5600+ times, respectively, and contributed to 160+ independent research papers. As indicated by 30+
support letters, iBEAT is now driving the research for MRI studies of early brain development in many labs
throughout the world. Results produced by iBEAT are also highlighted in the National Institute of Mental
Health (NIMH)'s 2015-2020 Strategic Plan.
This project is dedicated to the continuous development, hardening, and dissemination of iBEAT, by
developing innovative software modules with comprehensive user support. To achieve this goal, we
propose four aims. In Aim 1, we will create an innovative learning-based multi-source information
integration framework for joint skull stripping and tissue segmentation for accurate structural measurements.
Our method employs random forest to adaptively learn the optimal image appearance features from
multimodality images and also informative context features from tissue probability maps. In Aim 2, we will
construct longitudinal infant brain atlases at multiple time points (i.e., 1, 3, 6, 9, and 12 months of age)
for both T1-/T2-weighted and diffusion-weighted MR images. We propose a longitudinally-consistent
sparse representation technique to construct representative atlases with significantly improved structural
details by explicitly dealing with possible misalignments between images even after registration. In Aim 3, we
will develop a novel learning-based approach for cortical topology correction and integrate it, along with
our infant-centric analysis tools and atlases for cortical surfaces, into iBEAT for precise mapping of
dynamic and complex cortical changes in infants. Unlike existing tools that perform poorly for infant brains, we
will incorporate infant-dedicated tools for topology correction, surface reconstruction, registration, parcellation,
and measurements. We will further integrate longitudinal infant cortical surface atlases equipped with
parcellations based on growth trajectories. In Aim 4, we will significantly enhance iBEAT in terms of its
software functionalities as well as user support via systematic outreach and training.
Finally, we will employ iBEAT to process all imaging data from BCP and will release both the iBEAT
software package and the processed BCP data to the public via NITRC.
婴儿大脑分析工具的持续发展
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Gang Li其他文献
Gang Li的其他文献
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{{ truncateString('Gang Li', 18)}}的其他基金
Developing an Individualized Deep Connectome Framework for ADRD Analysis
开发用于 ADRD 分析的个性化深度连接组框架
- 批准号:
10515550 - 财政年份:2022
- 资助金额:
$ 46.53万 - 项目类别:
Mapping Trajectories of Alzheimer's Progression via Personalized Brain Anchor-nodes
通过个性化大脑锚节点绘制阿尔茨海默病的进展轨迹
- 批准号:
10571842 - 财政年份:2022
- 资助金额:
$ 46.53万 - 项目类别:
Mapping Trajectories of Alzheimer's Progression via Personalized Brain Anchor-nodes
通过个性化大脑锚节点绘制阿尔茨海默病的进展轨迹
- 批准号:
10346720 - 财政年份:2022
- 资助金额:
$ 46.53万 - 项目类别:
Infant Functional Connectome Fingerprinting based on Deep Learning
基于深度学习的婴儿功能连接组指纹图谱
- 批准号:
10288361 - 财政年份:2021
- 资助金额:
$ 46.53万 - 项目类别:
Harmonizing and Archiving of Large-scale Infant Neuroimaging Data
大规模婴儿神经影像数据的协调和归档
- 批准号:
10189251 - 财政年份:2021
- 资助金额:
$ 46.53万 - 项目类别:
Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI
基于多模态 MRI 发育模式的婴儿大脑皮层分区
- 批准号:
10162317 - 财政年份:2018
- 资助金额:
$ 46.53万 - 项目类别:
Using High Throughput Approach to Identify/Characterize Functional Variants on MS
使用高通量方法在 MS 上识别/表征功能变异
- 批准号:
9670361 - 财政年份:2018
- 资助金额:
$ 46.53万 - 项目类别:
Continued Development of Infant Brain Analysis Tools
婴儿大脑分析工具的持续开发
- 批准号:
9919645 - 财政年份:2018
- 资助金额:
$ 46.53万 - 项目类别:
Continued Development of Infant Brain Analysis Tools
婴儿大脑分析工具的持续开发
- 批准号:
10396127 - 财政年份:2018
- 资助金额:
$ 46.53万 - 项目类别:
Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI
基于多模态 MRI 发育模式的婴儿大脑皮层分区
- 批准号:
10407000 - 财政年份:2018
- 资助金额:
$ 46.53万 - 项目类别:
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