Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan
Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan
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DOI：
10.1016/j.neuroimage.2019.116450
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发表时间：
2020-03-01
期刊：
影响因子：
5.7
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Pomponio, Raymond;Erus, Guray;Davatzikos, Christos
随着医学成像进入信息时代，对大数据分析的需求迅速增长，跨不同队列、不同采集协议的成像数据的强大汇集和协调变得至关重要。我们描述了一个综合的努力，合并和协调了来自18个不同研究的10,477个结构脑MRI扫描的大规模数据集，这些数据来自没有已知的神经或精神疾病的参与者，这些研究代表了地理多样性。我们使用该数据集和基于多地图集的图像处理方法，从更大的解剖区域到单个皮质和深层结构，获得大脑的分层划分，并得出大脑结构在生命周期（3-96岁）中的年龄趋势。关键的是，我们提出并验证了一种在非线性年龄趋势存在的情况下协调这个汇集数据集的方法。我们提供了一个基于网络的可视化界面来生成和呈现最终的年龄趋势，使未来的大脑结构研究能够将他们的数据与大脑发育和衰老的参考数据进行比较，并检查可能与疾病相关的范围偏差。
As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.