Ridgway Gerard R, Omar Rohani, Ourselin Sébastien, Hill Derek L G, Warren Jason D, Fox Nick C
Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, WC1E 6BT, London, UK.
Neuroimage. 2009 Jan 1;44(1):99-111. doi: 10.1016/j.neuroimage.2008.08.045. Epub 2008 Sep 20.
There is great interest in using automatic computational neuroanatomy tools to study ageing and neurodegenerative disease. Voxel-based morphometry (VBM) is one of the most widely used of such techniques. VBM performs voxel-wise statistical analysis of smoothed spatially normalised segmented Magnetic Resonance Images. There are several reasons why the analysis should include only voxels within a certain mask. We show that one of the most commonly used strategies for defining this mask runs a major risk of excluding from the analysis precisely those voxels where the subjects' brains were most vulnerable to atrophy. We investigate the issues related to mask construction, and recommend the use of alternative strategies which greatly decrease this danger of false negatives.
人们对使用自动计算神经解剖学工具来研究衰老和神经退行性疾病有着浓厚的兴趣。基于体素的形态计量学（VBM）是此类技术中使用最广泛的技术之一。VBM对平滑后的空间归一化分割磁共振图像进行逐体素统计分析。分析应仅包括特定掩膜内的体素，原因有几个。我们表明，定义此掩膜最常用的策略之一存在一个重大风险，即恰好将受试者大脑最易萎缩的那些体素排除在分析之外。我们研究了与掩膜构建相关的问题，并建议使用替代策略，以大大降低这种假阴性的风险。
