Automated detection of white matter signal abnormality using T2 relaxometry: application to brain segmentation on term MRI in very preterm infants.
Automated detection of white matter signal abnormality using T2 relaxometry: application to brain segmentation on term MRI in very preterm infants.
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DOI：
10.1016/j.neuroimage.2012.08.081
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发表时间：
2013-01-01
期刊：
影响因子：
5.7
通讯作者：
Parikh NA
中科院分区：
文献类型：
--
作者：
He L;Parikh NA
高信号白色信号异常，也称为弥漫性过高信号强度（DEHSI），在足月相当年龄的T2加权MRI扫描中观察到高达80%的极早产儿。DEHSI可能代表发育阶段或弥漫性微结构白色物质异常。DEHSI严重程度的自动定量评估可能有助于解决这一争论，并改善新生儿脑组织分割。在无液体衰减的T2加权序列中，DEHSI的信号强度分布与脑脊液（CSF）的信号强度分布有很大重叠，使其难以检出。此外，T2加权图像的信号强度易受磁场不均匀性的影响。由场不均匀性引起的信号强度增加可能与DEHSI混淆。为了克服这些挑战，我们提出了一种算法来检测DEHSI使用T2弛豫，其反映的自由水含量的快速变化提供了改进的区别CSF和DEHSI之间的传统T2加权成像。此外，参数横向弛豫时间T2不受磁场不均匀性的影响。我们进行了计算机模拟，以选择一个最佳的检测参数，并验证所提出的方法。我们还表明，脑组织分割进一步增强，通过将DEHSI检测模拟早产儿脑图像和在体内非常早产儿成像在足月等效年龄。
Hyperintense white matter signal abnormalities, also called diffuse excessive high signal intensity (DEHSI), are observed in up to 80% of very preterm infants on T2-weighted MRI scans at term-equivalent age. DEHSI may represent a developmental stage or diffuse microstructural white matter abnormalities. Automated quantitative assessment of DEHSI severity may help resolve this debate and improve neonatal brain tissue segmentation. For T2-weighted sequence without fluid attenuation, the signal intensity distribution of DEHSI greatly overlaps with that of cerebrospinal fluid (CSF) making its detection difficult. Furthermore, signal intensities of T2-weighted images are susceptible to magnetic field inhomogeneity. Increased signal intensities caused by field inhomogeneity may be confused with DEHSI. To overcome these challenges, we propose an algorithm to detect DEHSI using T2 relaxometry, whose reflection of the rapid changes in free water content provides improved distinction between CSF and DEHSI over that of conventional T2-weighted imaging. Moreover, the parametric transverse relaxation time T2 is invulnerable to magnetic field inhomogeneity. We conducted computer simulations to select an optimal detection parameter and to validate the proposed method. We also demonstrated that brain tissue segmentation is further enhanced by incorporating DEHSI detection for both simulated preterm infant brain images and in vivo in very preterm infants imaged at term-equivalent age.
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