Evaluating and reducing the impact of white matter lesions on brain volume measurements
Evaluating and reducing the impact of white matter lesions on brain volume measurements
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DOI：
10.1002/hbm.21344
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发表时间：
2012-09-01
影响因子：
4.8
通讯作者：
De Stefano, Nicola
中科院分区：
文献类型：
--
作者：
Battaglini, Marco;Jenkinson, Mark;De Stefano, Nicola
基于MR的脑体积测量可能会受到白色物质（WM）病变的影响。在这里，我们评估了不同大小和强度的WM病变可能如何以及在多大程度上发生这种情况。在将不同大小和强度的WM病变插入健康受试者的T1-W脑图像后，我们评估了对两种广泛使用的自动脑体积测量方法（如SIENAX（基于分割）和SIENA（基于配准））的影响。为了探索部分体积（PV）估计的相关性，我们使用两种不同的PV模型进行了实验，这些模型由SIENAX和SIENA的相同分割算法（FAST）实现。最后，我们测试了这个问题的潜在解决方案。WM病变的存在不会使基于配准的方法（如SIENA）的测量产生偏倚。相比之下，WM病变的存在影响了基于分割的脑体积测量，如SIENAx。灰质（GM）和WM体积的错误分类随病变大小和强度而变化，特别是当病变强度与GM/WM界面相似时。WM病变的存在可能影响组织分类指标的程度明显受到所用PV模型的驱动，混合型PV模型在WM病变存在时的误差较低。当它们被掩盖时，由于WM病变而导致的组织错误分类仍然存在。相比之下，用与周围正常出现的WM相匹配的强度重新填充病变确保了准确的组织类测量，因此代表了用于准确的组织分类和脑体积测量的有前途的方法。《脑图谱》33：20622071，2012年。(c)2011 Wiley Periodicals，Inc.
MR-based measurements of brain volumes may be affected by the presence of white matter (WM) lesions. Here, we assessed how and to what extent this may happen for WM lesions of various sizes and intensities. After inserting WM lesions of different sizes and intensities into T1-W brain images of healthy subjects, we assessed the effect on two widely used automatic methods for brain volume measurement such as SIENAX (segmentation-based) and SIENA (registration-based). To explore the relevance of partial volume (PV) estimation, we performed the experiments with two different PV models, implemented by the same segmentation algorithm (FAST) of SIENAX and SIENA. Finally, we tested potential solutions to this issue. The presence of WM lesions did not bias measurements for registration-based method such as SIENA. By contrast, the presence of WM lesions affected segmentation-based brain volume measurements such as SIENAx. The misclassification of both gray matter (GM) and WM volumes varied considerably with lesion size and intensity, especially when the lesion intensity was similar to that of the GM/WM interface. The extent to which the presence of WM lesions could affect tissue-class measures was clearly driven by the PV modeling used, with the mixel-type PV model giving a lower error in the presence of WM lesions. The tissue misclassification due to WM lesions was still present when they were masked out. By contrast, refilling the lesions with intensities matching the surrounding normal-appearing WM ensured accurate tissue-class measurements and thus represents a promising approach for accurate tissue classification and brain volume measurements. Hum Brain Mapp 33:20622071, 2012. (c) 2011 Wiley Periodicals, Inc.