Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, USA.
J Magn Reson Imaging. 2013 Feb;37(2):372-81. doi: 10.1002/jmri.23842. Epub 2012 Oct 3.
To investigate the effect of standardized and study-specific human brain diffusion tensor templates on the accuracy of spatial normalization, without ignoring the important roles of data quality and registration algorithm effectiveness.
Two groups of diffusion tensor imaging (DTI) datasets, with and without visible artifacts, were normalized to two standardized diffusion tensor templates (IIT2, ICBM81) as well as study-specific templates, using three registration approaches. The accuracy of inter-subject spatial normalization was compared across templates, using the most effective registration technique for each template and group of data.
It was demonstrated that, for DTI data with visible artifacts, the study-specific template resulted in significantly higher spatial normalization accuracy than standardized templates. However, for data without visible artifacts, the study-specific template and the standardized template of higher quality (IIT2) resulted in similar normalization accuracy.
For DTI data with visible artifacts, a carefully constructed study-specific template may achieve higher normalization accuracy than that of standardized templates. However, as DTI data quality improves, a high-quality standardized template may be more advantageous than a study-specific template, because in addition to high normalization accuracy, it provides a standard reference across studies, as well as automated localization/segmentation when accompanied by anatomical labels.
在不忽略数据质量和配准算法有效性等重要因素的情况下,研究标准化和特定于研究的人脑弥散张量模板对空间归一化准确性的影响。
将两组具有和不具有可见伪影的弥散张量成像(DTI)数据集分别归一化为两个标准化弥散张量模板(IIT2、ICBM81)和特定于研究的模板,并使用三种配准方法。对于每组数据和模板,使用最有效的配准技术来比较跨模板的受试者间空间归一化准确性。
结果表明,对于具有可见伪影的 DTI 数据,特定于研究的模板导致的空间归一化准确性明显高于标准化模板。然而,对于没有可见伪影的数据,高质量的特定于研究的模板(IIT2)和标准化模板导致的归一化准确性相似。
对于具有可见伪影的 DTI 数据,精心构建的特定于研究的模板可能比标准化模板实现更高的归一化准确性。然而,随着 DTI 数据质量的提高,高质量的标准化模板可能比特定于研究的模板更有优势,因为除了高归一化准确性之外,它还提供了跨研究的标准参考,以及在伴有解剖学标签时的自动定位/分割。