Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration
Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration
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DOI：
10.3389/fninf.2013.00029
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发表时间：
2013-01-01
影响因子：
3.5
通讯作者：
Johnson, Hans J.
中科院分区：
文献类型：
--
作者：
Kim, Eun Young;Johnson, Hans J.
一个强大的多模态工具，用于自动注册，偏差校正和组织分类，已经实现了大规模异构多位点纵向MR数据分析。这项工作的重点是改进偏差校正、配准和组织分类之间的迭代优化框架。主要贡献是通过结合以下四个元素来提高鲁棒性：(1)利用多模态和重复扫描，(2)结合高变形配准，(3)使用扩展的组织定义集，(4)使用多模态感知强度-上下文先验。通过模拟大脑数据集（brain Web）和应用来自32个站点的高异质性成像研究数据，通过专家视觉检查进行质量评估，研究了这些增强功能的好处。该工具的实现是为大规模数据处理量身定制的，但不限于具有灵活接口的大量数据变化。在本文中，我们描述了联合配准，偏差校正和组织分类的增强，提高了处理多地点收集的多模态纵向MR扫描的泛化性和鲁棒性。该工具通过模拟、模拟和人类受试者MRI图像进行评估。通过这些增强，结果显示大规模异构MRI处理的鲁棒性得到了提高。
A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness improvements from incorporation of following four elements: (1) utilize multi-modal and repeated scans, (2) incorporate high-deformable registration, (3) use extended set of tissue definitions, and (4) use of multi-modal aware intensity-context priors. The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (Brain Web) and by applying to highly-heterogeneous data from a 32 site imaging study with quality assessments through the expert visual inspection. The implementation of this tool is tailored for, but not limited to, large-scale data processing with great data variation with a flexible interface. In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal MR scans collected at multi-sites. The tool was evaluated by using both simulated and simulated and human subject MRI images. With these enhancements, the results showed improved robustness for large-scale heterogeneous MRI processing.