Gupta Aditya, Toews Matthew, Janardhana Ravikiran, Rathi Yogesh, Gilmore John, Escolar Maria, Styner Martin
Dept Pediatrics, University of Pittsburgh, PA, USA ; Dept Psychiatry, University of North Carolina, Chapel Hill, NC.
Harvard Medical School, Boston MA.
Proc SPIE Int Soc Opt Eng. 2013 Mar 13;8669:866907-. doi: 10.1117/12.2006977.
This paper presents a novel pipeline for the registration of diffusion tensor images (DTI) with large pathological variations to normal controls based on the use of a novel feature map derived from white matter (WM) fiber tracts. The research presented aims towards an atlas based DTI analysis of subjects with considerable brain pathologies such as tumors or hydrocephalus. In this paper, we propose a novel feature map that is robust against variations in WM fiber tract integrity and use these feature maps to determine a landmark correspondence using a 3D point correspondence algorithm. This correspondence drives a deformation field computed using Gaussian radial basis functions(RBF). This field is employed as an initialization to a standard deformable registration method like demons. We present early preliminary results on the registration of a normal control dataset to a dataset with abnormally enlarged lateral ventricles affected by fatal demyelinating Krabbe disease. The results are analyzed based on a regional tensor matching criterion and a visual assessment of overlap of major WM fiber tracts. While further evaluation and improvements are necessary, the results presented in this paper highlight the potential of our method in handling registration of subjects with severe WM pathology.
本文提出了一种新颖的流程，用于基于从白质（WM）纤维束导出的新型特征图，将具有较大病理变化的扩散张量图像（DTI）与正常对照进行配准。所呈现的研究旨在对患有诸如肿瘤或脑积水等严重脑部疾病的受试者进行基于图谱的DTI分析。在本文中，我们提出了一种对WM纤维束完整性变化具有鲁棒性的新型特征图，并使用这些特征图通过3D点对应算法来确定地标对应关系。这种对应关系驱动使用高斯径向基函数（RBF）计算的变形场。该场被用作像demons这样的标准可变形配准方法的初始化。我们展示了将正常对照数据集与受致命脱髓鞘性克拉伯病影响的侧脑室异常扩大的数据集进行配准的早期初步结果。基于区域张量匹配标准和主要WM纤维束重叠的视觉评估对结果进行了分析。虽然还需要进一步的评估和改进，但本文呈现的结果突出了我们的方法在处理具有严重WM病理的受试者配准方面的潜力。
