Reducing the Impact of White Matter Lesions on Automated Measures of Brain Gray and White Matter Volumes
Reducing the Impact of White Matter Lesions on Automated Measures of Brain Gray and White Matter Volumes
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DOI：
10.1002/jmri.22214
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发表时间：
2010-07-01
影响因子：
4.4
通讯作者：
Wheeler-Kingshott, Claudia A. M.
中科院分区：
文献类型：
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作者：
Chard, Declan T.;Jackson, Jonathan S.;Wheeler-Kingshott, Claudia A. M.
目的：开发一种自动病灶填充技术（LEAP；LEsion 自动预处理），以减少与病灶相关的脑组织分割偏差（已知会影响多发性硬化症患者的自动脑灰质 [GM] 和白质 [WM] 组织分割），以及一种 WM 病灶模拟工具，用于避免这种情况。 材料和方法：将具有不同体积和信号强度的模拟病灶添加到来自三名健康受试者和然后自动填充接近正常 WM 的值。我们测试了模拟病变和 LEAP 病变填充校正对 SPM 衍生组织体积估计的影响。结果：GM 和 WM 组织体积估计受到 WM 病变存在的影响。当模拟病灶体积为 15 mL，正常 WM 强度的 70% 时，效果是将 GM 分数体积（相对于颅内）体积增加约 2.3%，并将 WM 分数减少约 3.6%。病变填充减少了这些错误，得出结论：WM 病变对自动 GM 和 WM 体积测量的影响可能相当大，从而掩盖了真正的疾病介导的体积变化。使用接近正常 WM 的值填充病灶可以实现更准确的 GM 和 WM 体积测量，并且应该适用于独立于用于分割的软件的结构扫描。
Purpose: To develop an automated lesion-filling technique (LEAP; LEsion Automated Preprocessing) that would reduce lesion-associated brain tissue segmentation bias (which is known to affect automated brain gray [GM] and white matter [WM] tissue segmentations in people who have multiple sclerosis), and a WM lesion simulation tool with which to Lest it.Materials and Methods: Simulated lesions with differing volumes and signal intensities were added to volumetric brain images from three healthy subjects and then automatically filled with values approximating normal WM. We tested the effects of simulated lesions and lesion-filling correction with LEAP on SPM-derived tissue volume estimates.Results: GM and WM tissue volume estimates were affected by the presence of WM lesions. With simulated lesion volumes of 15 mL at 70% of normal WM intensity, the effect was to increase GM fractional (relative to intracranial) volumes by approximate to 2.3%, and reduce WM fractions by approximate to 3.6%. Lesion filling reduced these errors toConclusion: The effect of WM lesions on automated GM and WM volume measures may be considerable and thereby obscure real disease-mediated volume changes. Lesion filling with values approximating normal WM enables more accurate GM and WM volume measures and should be applicable to structural scans independently of the software used for the segmentation.