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The Impact of MRI T1 Hypointense Brain Lesions on Cerebral Deep Gray Matter Volume Measures in Multiple Sclerosis

机译:MRI T1低血压脑病变对多发性硬化症脑深灰质体积措施的影响

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ABSTRACT BACKGROUND AND PURPOSE Deep gray matter (DGM) atrophy has been shown at early stages of multiple sclerosis (MS) and reported as an informative marker of cognitive dysfunction and clinical progression. Therefore, accurate measurement of DGM structure volume is a key priority in MS research. Findings from prior studies have shown that hypointense T1 lesions may impact the accuracy of global brain volume measures; however, literature on the effects of hypointense T1 lesions on DGM structure volumes is sparse. METHODS We explored the effects of hypointense T1 lesions on data from 54 relapsing remitting MS patients. Lesions were segmented both manually and with a freely available automatic lesion segmentation/in‐painting algorithm (Lesion Segmentation Tool‐LST). Volumes of 14 DGM structures were calculated from non‐in‐painted and in‐painted images and compared via paired t ‐tests, intraclass correlation coefficient, and Dice similarity coefficient. RESULTS There were no significant differences in DGM structural volumes between non‐in‐painted and in‐painted images. Automatic lesion‐segmentation/in‐painting tool provided similar results to manual segmentation/in‐painting. CONCLUSIONS Our results suggest that lesion in‐painting has a negligible impact on DGM structure volume measurement although some regions are more vulnerable to the impact of lesions than others. Furthermore, manual lesion segmentation/in‐painting can be replaced by an automatic segmentation/in‐painting process.
机译:摘要背景和目的深灰质(DGM)萎缩已在多发性硬化症(MS)的早期阶段显示,并作为认知功能障碍和临床进展的信息性标记。因此,准确测量DGM结构体积是MS研究中的关键优先级。现有研究的结果表明,低音瘤T1病变可能会影响全球脑体积措施的准确性;然而,对DGM结构体积对低音阵T1病变的影响的文献稀疏。方法我们探讨了低温T1病变对54次重复剩余的MS患者的影响。病变手动和自由可用的自动病变分割/内绘算法(病变分段工具-LST)进行分割。从非涂覆的和绘制的图像计算14个DGM结构的体积,并通过配对的T -Tests,腹部相关系数和骰子相似度系数进行比较。结果非涂层图像和涂在一起的图像之间的DGM结构体积没有显着差异。自动Lesion-semonation / In-Plays工具与手动分段/内绘提供类似结果。结论我们的结果表明,病变绘画对DGM结构体积测量的影响可忽略不计,尽管一些地区更容易受到病变的影响而不是其他区域。此外,可以通过自动分段/内绘制过程所取代手动病变分割/内绘。

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