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基于大脑模板的MRI扫描自动定位方法

严序1,2,周敏雄2,杨光2,徐冬溶1–4*   

  1. 1.脑功能基因组学教育部重点实验室,华东师范大学,上海 200062,;2.上海市磁共振重点实验室 物理系,华东师范大学,上海 200062;3.哥伦比亚大学 精神病学系,美国 纽约 10032;4.纽约州立精神疾病研究所,美国 纽约 10032
  • 收稿日期:2013-05-03 修回日期:2013-07-06 出版日期:2014-06-05 发布日期:2014-06-05
  • 作者简介:严序(1984-), 男, 湖北人, 博士研究生, 无线电物理专业,研究方向为磁共振成像. *通讯联系人:徐冬溶,电话:+01-212-543-5495,E-mail: XuD@nyspi.columbia.edu.
  • 基金资助:

    上海市科学技术委员会国际科技合作基金资助项目(10440710200),国家自然科学基金重点项目培育计划资助项目(91232701),华东师范大学大型精密仪器开放基金资助项目.

A Brain Atlas Based Automatic Localization Method for MRI

YAN Xu1,2,ZHOU Min-Xiong2,YANG Guang2,XU Dong-rong1–4*


 
  

  1. 1. Key Laboratory of Brain Functional Genomics of Ministry of Education, East China Normal University, Shanghai 200062, China; 2. Shanghai Key Laboratory of Magnetic Resonance, Physics Department, East China Normal University, Shanghai 200062, China; 3. Department of Psychiatry, Columbia University, New York 10032, USA; 4. New York State Psychiatric Institute, New York 10032, USA
  • Received:2013-05-03 Revised:2013-07-06 Online:2014-06-05 Published:2014-06-05
  • About author:*Corresponding author:XU Dong-rong, Tel: +01-212-543-5495, E-mail: XuD@nyspi.columbia.edu.
  • Supported by:

    上海市科学技术委员会国际科技合作基金资助项目(10440710200),国家自然科学基金重点项目培育计划资助项目(91232701),华东师范大学大型精密仪器开放基金资助项目.

摘要:

在大脑磁共振成像(MRI)影像学的数据采集中,通常先扫描一幅定位图像,并根据解剖学先验知识手动调整合适的扫描定位参数,再进行后续的正式扫描.该文实现了一种直接以大脑模板为参照的自动定位的方法:首先采集一幅中等分辨率的快速三维定位图像,然后通过与模板的配准确定定位参数,并应用到后续序列的扫描,以保证不同被试在图像采集时采用与模板一致的空间定位.该方法一方面便于不同被试的图像数据之间进行系统性比较与参照,帮助诊断者快速定位病灶,也可在后续常用的基于体素分析过程最大化数据的利用效率.另一方面,针对单个体多次扫描之间的自动定位,该文进一步使用迭代方法,通过多次“扫描、配准、自动定位”步骤,逐步减小图像配准算法的误差.实验证明,该文基于大脑模板的自动定位方法能够确保不同被试之间和同一被试之内在图像数据采集时的空间定位高度一致性,其中同一被试内多次扫描的空间定位误差<1.0 mm和1.0º.

关键词: 磁共振成像(MRI), 自动定位, 配准, 模板, 基于体素分析

Abstract:

Acquisition of brain magnetic resonance imaging (MRI) data usually starts with a localizer for properly positioning the field of view based on a prior knowledge of brain anatomy and setting corresponding localization parameters for subsequent scans. We propose an automatic localization method that references directly to the brain atlas. The procedure first quickly acquires a 3D localization image at a median spatial resolution, and then calculates its registration parameter to the atlas and uses these parameters to position the subsequent scans, which therefore ensures the scanning configurations for different subjects are consistent with the atlas. The proposed method benefits inter-subject comparisons and referencing, in that it can help investigators locating abnormal structure, tumors or other regions-of-interest more quickly and easily, and therefore using the data in voxel based analysis more efficiently. We also propose an iterative method for automatic localizing individual subject in multiple independent follow-up scans. By iterating “scan, registration, automatic localization” steps several passes, it progressively minimizes the error of the image registration algorithm. Experiments showed that our atlas-based automatic localization method achieved high consistency of spatial location both in imaging data acquired from different subjects and in multiple separate scans from a single subject, and the localization error between multiple scans of a single subject was less than 1.0 mm and 1.0 degree.

Key words: MRI, automatic localization, registration, atlas, voxel based analysis

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