Wu Minjie, Rosano Caterina, Butters Meryl, Whyte Ellen, Nable Megan, Crooks Ryan, Meltzer Carolyn C, Reynolds Charles F, Aizenstein Howard J
Department of Electrical and Computer Engineering, University of Pittsburgh, USA.
Psychiatry Res. 2006 Dec 1;148(2-3):133-42. doi: 10.1016/j.pscychresns.2006.09.003. Epub 2006 Nov 13.
White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy-connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies (e.g. [Taylor, W.D., MacFall, J.R., Steffens, D.C., Payne, M.E., Provenzale, J.M., Krishnan, K.R., 2003. Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539-544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large NeuroImage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.
白质高信号（WMH）常见于老年人的T2加权液体衰减反转恢复序列脑磁共振成像中，与多种神经精神疾病相关，包括血管性痴呆、阿尔茨海默病和老年期抑郁症。以往关于WMH的磁共振成像研究主要依赖于WMH等级的主观和整体（即全脑）评分。在本研究中，我们实施并验证了一种用于量化和定位WMH的自动化方法。我们采用模糊连接算法来自动分割WMH，并使用基于 demons 的图像配准，通过约翰霍普金斯大学白质图谱自动对WMH进行解剖定位。该方法通过从11名患有迟发性老年期抑郁症（LLD）的老年受试者和8名老年对照者获取的脑磁共振图像进行验证。选择该数据集是因为已知LLD受试者有显著的WMH负荷。将我们自动化方法中识别出的WMH体积与公认的金标准（手动评分）进行比较。发现自动化方法与手动评分之间存在显著相关性（P<0.0001），从而证明两种方法对WMH的量化相似。正如其他研究（例如[泰勒，W.D.，麦克福尔，J.R.，斯特芬斯，D.C.，佩恩，M.E.，普罗文扎勒，J.M.，克里希南，K.R.，2003年。老年期抑郁症中与年龄相关的白质高信号的定位。神经精神药理学与生物精神病学进展。27（3），539 - 544。]）所示，我们发现无论是手动方法还是自动化方法，LLD受试者的WMH负荷均显著高于对照组。自动化方法的效应量更大，表明它是一种更具特异性的测量方法。此外，我们描述了LLD受试者以及对照受试者中WMH的解剖定位，并检测出LLD患者WMH负荷特有的感兴趣区域（ROI）。鉴于大型神经影像数据库的出现，本文所述的技术将有助于更好地理解WMH与神经精神疾病之间的关系。
