A multi-scanner neuroimaging data harmonization using RAVEL and ComBat.
A multi-scanner neuroimaging data harmonization using RAVEL and ComBat.
复制标题
使用Ravel和Fighting的多扫描仪神经成像数据协调。
DOI：
10.1016/j.neuroimage.2021.118703
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发表时间：
2021-12-15
期刊：
影响因子：
5.7
通讯作者：
Tudorascu DL
中科院分区：
文献类型：
--
作者：
Eshaghzadeh Torbati M;Minhas DS;Ahmad G;O'Connor EE;Muschelli J;Laymon CM;Yang Z;Cohen AD;Aizenstein HJ;Klunk WE;Christian BT;Hwang SJ;Crainiceanu CM;Tudorascu DL
现代神经影像学研究经常结合联合收割机从多个扫描仪和实验条件收集的数据。这些数据通常包含与图像强度标度（不同图像中的图像强度标度不相同）和扫描仪效应（从不同扫描仪获得的图像包含大量技术偏差）相关的大量技术可变性。在这里，我们评估和比较数据分析方法的结果，没有任何数据转换（RAW），强度归一化使用RAVEL，区域协调方法使用ComBat，以及RAVEL和ComBat的组合。方法进行了评估的一个独特的样本16名研究参与者谁扫描1.5T和3 T扫描仪相隔几个月。对与阿尔茨海默病（AD）相关的7个不同感兴趣区（ROI）进行神经放射学评价。皮质测量和结果表明：（1）RAVEL显著改善了图像强度的再现性;（2）由于受试者和图像衍生测量之间的协调性更一致，ComBat在区域水平协调性方面优于RAVEL和RAVEL-ComBat组合;（3）与RAW图像分析相比，RAVEL和ComBat显著降低了偏倚，但RAVEL也导致更大的方差; RAVEL的均方根偏差（RMSD）大于ComBat的主要原因是RAVEL的方差较大。
Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images obtained from different scanners contain substantial technical biases). Here we evaluate and compare results of data analysis methods without any data transformation (RAW), with intensity normalization using RAVEL, with regional harmonization methods using ComBat, and a combination of RAVEL and ComBat. Methods are evaluated on a unique sample of 16 study participants who were scanned on both 1.5T and 3T scanners a few months apart. Neuroradiological evaluation was conducted for 7 different regions of interest (ROI’s) pertinent to Alzheimer’s disease (AD). Cortical measures and results indicate that: (1) RAVEL substantially improved the reproducibility of image intensities; (2) ComBat is preferred over RAVEL and the RAVEL-ComBat combination in terms of regional level harmonization due to more consistent harmonization across subjects and image-derived measures; (3) RAVEL and ComBat substantially reduced bias compared to analysis of RAW images, but RAVEL also resulted in larger variance; and (4) the larger root mean square deviation (RMSD) of RAVEL compared to ComBat is due mainly to its larger variance.
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影响因子：
5.7
作者：
Jovicich, Jorge;Marizzoni, Moira;Frisoni, Giovanni B.
通讯作者：
Frisoni, Giovanni B.
通过替代变量分析捕获基因表达研究中的异质性。
影响因子：
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作者：
Leek, Jeffrey T.;Storey, John D.
通讯作者：
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影响因子：
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作者：
Frisoni, Giovanni B.;Fox, Nick C.;Jack, Clifford R., Jr.;Scheltens, Philip;Thompson, Paul M.
通讯作者：
Thompson, Paul M.
DOI：
10.1007/978-3-030-87199-4_30
发表时间：
2021-09
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
Liu, Mengting;Maiti, Piyush;Thomopoulos, Sophia;Zhu, Alyssa;Chai, Yaqiong;Kim, Hosung;Jahanshad, Neda
通讯作者：
Jahanshad, Neda
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce