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多分辨率聚焦法:一种应用于脑磁图数据的源成像技术。

Multi-Resolution FOCUSS: a source imaging technique applied to MEG data.

作者信息

Moran J E, Bowyer S M, Tepley N

机构信息

Henry Ford Hospital, Detroit, Michigan 48202-2689, USA.

出版信息

Brain Topogr. 2005 Fall;18(1):1-17. doi: 10.1007/s10548-005-7896-x. Epub 2005 Sep 16.

DOI:10.1007/s10548-005-7896-x
PMID:16193262
Abstract

A variety of techniques are available for imaging magnetoencephalographic (MEG) data to the corresponding cortical structures. Each performs a functional optimization that includes mathematical and physical restrictions on source activity. Unlike other imaging techniques, MR-FOCUSS (Multi-Resolution FOCal Underdetermined System Solution) utilizes a wavelet statistical operator that allows spatial resolution to be chosen appropriately for focal or extended sources. Control of focal imaging properties is achieved by specifying P in an l(P) norm distribution template used to construct the wavelets. In addition, incorporation of a multi-resolution wavelet operator desensitizes the mathematical algorithm to noise, (regularization). Like the FOCUSS imaging technique, an initial estimate of cortical activity is recursively enhanced to obtain the final high resolution imaging results. Studies of model MEG data representing all regions of a realistic cortical model are performed to quantify MR-FOCUSS imaging properties. These modeled data studies included single and multiple dipole sources as well as an extended source model. Thus, MR-FOCUSS is found to be very effective for imaging language processing for pre-surgical planning and provides a high-resolution method to image sequential activation of multiple correlated sources involved in language processing.

摘要

有多种技术可用于将脑磁图(MEG)数据成像到相应的皮质结构。每种技术都进行功能优化,包括对源活动的数学和物理限制。与其他成像技术不同,MR-FOCUSS(多分辨率聚焦欠定系统解决方案)利用小波统计算子,允许为局灶性或扩展性源适当选择空间分辨率。通过在用于构建小波的l(P)范数分布模板中指定P来实现对局灶成像特性的控制。此外,多分辨率小波算子的纳入使数学算法对噪声不敏感(正则化)。与FOCUSS成像技术一样,皮质活动的初始估计会被递归增强以获得最终的高分辨率成像结果。对代表真实皮质模型所有区域的模型MEG数据进行研究,以量化MR-FOCUSS成像特性。这些模型数据研究包括单偶极子源和多偶极子源以及扩展性源模型。因此,发现MR-FOCUSS在术前规划的语言处理成像方面非常有效,并提供了一种高分辨率方法来成像语言处理中涉及的多个相关源的顺序激活。

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