Wetter Nathan C, Hubbard Elizabeth A, Motl Robert W, Sutton Bradley P
Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIllinois; Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana Illinois.
Department of Kinesiology and Community Health University of Illinois at Urbana-Champaign Urbana Illinois.
Brain Behav. 2016 Jan 28;6(3):e00440. doi: 10.1002/brb3.440. eCollection 2016 Mar.
T2 Lesion Volume (T2LV) has been an important biomarker for multiple sclerosis (MS). Current methods available to quantify lesions from MR images generally require manual adjustments or multiple images with different contrasts. Further, implementations are often not easily or openly accessible.
We created a fully unsupervised, single T2 FLAIR image T2LV quantification package based on the popular open-source imaging toolkit FSL.
By scripting various processing tools in FSL, we developed an image processing pipeline that distinguishes normal brain tissue from CSF and lesions. We validated our method by hierarchical multiple regression (HMR) with a preliminary study to see if our T2LVs correlate with clinical disability measures in MS when controlled for other variables.
Pearson correlations between T2LV and Expanded Disability Status Scale (EDSS: r = 0.344, P = 0.013), Six-Minute Walk (6MW: r = -0.513, P = 0.000), Timed 25-Foot Walk (T25FW: r = -0.438, P = .000), and Symbol Digit Modalities Test (SDMT: r = -0.499, P = 0.000) were all significant. Partial correlations controlling for age were significant between T2LV and 6MW (r = -0.433, P = 0.002), T25FW (r = -0.392, P = 0.004), and SDMT (r = -0.450, P = 0.001). In HMR, T2LV explained significant additional variance in 6MW (R(2) change = 0.082, P = 0.020), after controlling for confounding variables such as age, white matter volume (WMV), and gray matter volume (GMV).
Our T2LV quantification software produces T2LVs from a single FLAIR image that correlate with physical disability in MS and is freely available as open-source software.
T2病灶体积（T2LV）一直是多发性硬化症（MS）的重要生物标志物。目前可用于从磁共振图像中量化病灶的方法通常需要人工调整或使用具有不同对比度的多幅图像。此外，这些方法的实现往往不容易获取或无法公开获取。
我们基于流行的开源成像工具包FSL创建了一个完全无监督的、单幅T2液体衰减反转恢复（FLAIR）图像T2LV量化软件包。
通过编写FSL中的各种处理工具脚本，我们开发了一种图像处理流程，可将正常脑组织与脑脊液和病灶区分开来。我们通过分层多元回归（HMR）对我们的方法进行了验证，在一项初步研究中，观察在控制其他变量的情况下，我们的T2LV是否与MS中的临床残疾指标相关。
T2LV与扩展残疾状态量表（EDSS：r = 0.344，P = 0.013）、6分钟步行试验（6MW：r = -0.513，P = 0.000）、25英尺定时步行试验（T25FW：r = -0.438，P = 0.000）以及符号数字模态测验（SDMT：r = -0.499，P = 0.000）之间的Pearson相关性均具有显著性。在控制年龄后，T2LV与6MW（r = -0.433，P = 0.002）、T25FW（r = -0.392，P = 0.004）和SDMT（r = -0.450，P = 0.001）之间的偏相关性具有显著性。在HMR中，在控制年龄、白质体积（WMV）和灰质体积（GMV）等混杂变量后，T2LV在6MW中解释了显著的额外方差（R(2)变化 = 0.082，P = 0.020）。
我们的T2LV量化软件可从单幅FLAIR图像生成T2LV，这些T2LV与MS中的身体残疾相关，并且作为开源软件可免费获取。
