IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7515, USA.
Hum Brain Mapp. 2011 Mar;32(3):382-96. doi: 10.1002/hbm.21023.
The acquisition of high-quality magnetic resonance (MR) images of neonatal brains is largely hampered by their characteristically small head size and insufficient tissue contrast. As a result, subsequent image processing and analysis, especially brain tissue segmentation, are often affected. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by augmenting signal-to-noise ratio and spatial resolution without lengthening data acquisition time. In addition, a specialized hybrid atlas-based tissue segmentation algorithm is developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in the to-be-segmented neonatal image with a Hessian filter for generation of a cortical GM confidence map. A neonatal population atlas is then generated by averaging the presegmented images of a population, weighted by their cortical GM similarity with respect to the to-be-segmented image. Finally, the neonatal population atlas is combined with the GM confidence map, and the resulting enhanced tissue probability maps for each tissue form a hybrid atlas is used for atlas-based segmentation. Various experiments are conducted to compare the segmentations of the proposed method with manual segmentation (on both images acquired with a dedicated phased array coil and a conventional volume coil), as well as with the segmentations of two population-atlas-based methods. Results show the proposed method is capable of segmenting the neonatal brain with the best accuracy, and also preserving the most structural details in the cortical regions.
获取新生儿大脑的高质量磁共振（MR）图像在很大程度上受到其头部尺寸小和组织对比度不足的限制。因此，后续的图像处理和分析，特别是脑组织分割，往往受到影响。为了解决这个问题，使用专用的相控阵新生儿头部线圈来提高 MR 图像质量，在不增加数据采集时间的情况下提高信噪比和空间分辨率。此外，还开发了一种专门的基于混合图谱的组织分割算法，用于描绘获取的新生儿脑 MR 图像中的精细结构。所提出的组织分割方法首先使用 Hessian 滤波器增强待分割新生儿图像中的片状皮质灰质（GM）结构，以生成皮质 GM 置信图。然后通过对人群的预分割图像进行平均来生成新生儿人群图谱，并根据与待分割图像的皮质 GM 相似性对其进行加权。最后，将新生儿人群图谱与 GM 置信图相结合，得到的增强组织概率图为每个组织形式形成混合图谱，用于基于图谱的分割。进行了各种实验来比较所提出的方法与手动分割（专用相控阵线圈和常规容积线圈采集的图像）以及两种基于人群图谱的方法的分割结果。结果表明，所提出的方法能够以最佳的准确性分割新生儿大脑，并保留皮质区域的最多结构细节。
