k-space based summary motion detection for functional magnetic resonance imaging
k-space based summary motion detection for functional magnetic resonance imaging
复制标题
DOI：
10.1016/s1053-8119(03)00339-2
复制
发表时间：
2003-10-01
期刊：
影响因子：
5.7
通讯作者：
Ernst, T
中科院分区：
文献类型：
--
作者：
Caparelli, EC;Tomasi, D;Ernst, T
功能性磁共振成像研究对运动非常敏感;头部运动小至I-mm平移或V旋转都可能导致虚假信号。开发了一种使用k空间MRI数据在功能性MRI时间序列期间监测受试者运动的算法。计算初始扫描和后续扫描之间的平方差的k空间加权平均值，其在单个质量参数中总结受试者运动;然而，质量参数不能用于运动校正。该质量参数在整个时间序列中的演变指示头部运动是否在预定限度内。使用统计参数映射（SPM 99）包中的六个刚体配准参数（三个平移和三个旋转）作为参考，使用五十项功能性MRI研究来校准算法的灵敏度。新质量参数与SPM参考值的平均相关系数为0.84。简单的算法以90%的准确率正确地分类了可接受或过度的运动，剩下的10%是边界情况。这种方法可以在扫描后几秒钟内评估大脑运动，并决定是否需要重复研究。(C)2003年爱思唯尔公司All rights reserved.
Functional MRI studies are very sensitive to motion; head movements of as little as I-mm translations or V rotations may cause spurious signals. An algorithm was developed that uses k-space MRI data to monitor subject motion during functional MRI time series. A k-space weighted average of squared difference between the initial scan and subsequent scans is calculated, which summarizes subject motion in a single quality parameter; however, the quality parameter cannot be used for motion correction. The evolution of this quality parameter throughout a time series indicates whether head motion is within a predetermined limit. Fifty functional MRI studies were used to calibrate the sensitivity of the algorithm, using the six rigid-body registration parameters (three translations and three rotations) from the statistical parametric mapping (SPM99) package as a reference. The average correlation coefficient between the new quality parameter and the reference value from SPM was 0.84. The simple algorithm correctly classified acceptable or excessive motion with 90% accuracy, with the remaining 10% being borderline cases. This method makes it possible to evaluate brain motion within seconds after a scan and to decide whether a study needs to be repeated. (C) 2003 Elsevier Inc. All rights reserved.