Zhang Weihong, Olivi Alessandro, Hertig Samuel J, van Zijl Peter, Mori Susumu
The Russell H. Morgan Department of Radiology and Radiological Science, Division of MRI Research, Neurosection, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Neuroimage. 2008 Aug 15;42(2):771-7. doi: 10.1016/j.neuroimage.2008.04.241. Epub 2008 Apr 30.
Reconstruction of white matter tracts based on diffusion tensor imaging (DTI) is currently widely used in clinical research. This reconstruction allows us to identify coordinates of specific white matter tracts and to investigate their anatomy. Fiber reconstruction, however, relies on manual identification of anatomical landmarks of a tract of interest, which is based on subjective judgment and thus a potential source of experimental variability. Here, an automated tract reconstruction approach is introduced. A set of reference regions of interest (rROIs) known to select a tract of interest was marked in our DTI brain atlas. The atlas was then linearly transformed to each subject, and the rROI set was transferred to the subject for tract reconstruction. Agreement between the automated and manual approaches was measured for 11 tracts in 10 healthy volunteers and found to be excellent (kappa>0.8) and remained high up to 4-5 mm of the linear transformation errors. As a first example, the automated approach was applied to brain tumor patients and strategies to cope with severe anatomical abnormalities are discussed.
基于扩散张量成像(DTI)的白质纤维束重建目前在临床研究中被广泛应用。这种重建使我们能够确定特定白质纤维束的坐标并研究其解剖结构。然而,纤维重建依赖于对感兴趣纤维束的解剖标志进行手动识别,这基于主观判断,因此是实验变异性的一个潜在来源。在此,介绍一种自动纤维束重建方法。在我们的DTI脑图谱中标记了一组已知用于选择感兴趣纤维束的参考感兴趣区域(rROI)。然后将图谱线性变换到每个受试者,并将rROI集转移到受试者用于纤维束重建。对10名健康志愿者的11条纤维束测量了自动和手动方法之间的一致性,发现一致性极佳(kappa>0.8),并且在线性变换误差高达4 - 5毫米时仍保持较高水平。作为第一个例子,将自动方法应用于脑肿瘤患者,并讨论了应对严重解剖异常的策略。