Spatial Normalization Method Based on Discrete Cosine Transform

机译：基于离散余弦变换的空间归一化方法

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The signal from the measurement of brain activation using functional resonance imaging (fMRI) is too small [1] to be determined correctly, Therefore, the statistical methods usually used in the data analysis for fMRI with multiple subjects and multiple measurements for each subject. Therefore, it is necessary to make the average for these data from different subjects or one subject in different measurement. Just the anatomical differences between individuals make a certain uncertainty while people want to get the average data from the subjects. To resolve this problem, spatial normalization need to be implemented. This paper introduced some methods of spatial normalization and presented a more efficient one based on discrete cosine transform.

机译：使用功能共振成像（fMRI）进行的大脑激活测量得出的信号太小[1]，无法正确确定。因此，通常用于多对象fMRI的数据分析中的统计方法，并且每个对象进行多次测量。因此，有必要对来自不同对象或不同测量中一个对象的这些数据取平均值。人们希望从受试者身上获得平均数据时，只是个体之间的解剖学差异会带来一定的不确定性。为了解决这个问题，需要实现空间归一化。本文介绍了一些空间归一化方法，并提出了一种基于离散余弦变换的更有效方法。

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来源
《2nd Beijing International Conference On Physics and Engineering of Medical Imaging, Oct 24-28, 2001, Beijing, P.R. China》|2001年|p.49-50|共2页
会议地点 Beijing(CN)
作者
Luo Qingfei; Chen Weijie; Guoyue Chen; Bao Shangiian;
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作者单位

Institute of Heavy Ion Physics, the Research Center for Tumor Physical Diagnosis and Therapeutics, and the Beijing Key Laboratory: the Laboratory of Medical Physics Engineering, Peking University, Beijing 100871;

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原文格式 PDF
正文语种 eng
中图分类基础医学;
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Spatial Normalization Method Based on Discrete Cosine Transform

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