Visser Martin, Petr Jan, Müller Domenique M J, Eijgelaar Roelant S, Hendriks Eef J, Witte Marnix, Barkhof Frederik, van Herk Marcel, Mutsaerts Henk J M M, Vrenken Hugo, de Munck Jan C, De Witt Hamer Philip C
Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands.
Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
Front Neurosci. 2020 Jun 5;14:585. doi: 10.3389/fnins.2020.00585. eCollection 2020.
To summarize the distribution of glioma location within a patient population, registration of individual MR images to anatomical reference space is required. In this study, we quantified the accuracy of MR image registration to anatomical reference space with linear and non-linear transformations using estimated tumor targets of glioblastoma and lower-grade glioma, and anatomical landmarks at pre- and post-operative time-points using six commonly used registration packages (FSL, SPM5, DARTEL, ANTs, Elastix, and NiftyReg). Routine clinical pre- and post-operative, post-contrast T1-weighted images of 20 patients with glioblastoma and 20 with lower-grade glioma were collected. The 2009a Montreal Neurological Institute brain template was used as anatomical reference space. Tumors were manually segmented in the patient space and corresponding healthy tissue was delineated as a target volume in the anatomical reference space. Accuracy of the tumor alignment was quantified using the Dice score and the Hausdorff distance. To measure the accuracy of general brain alignment, anatomical landmarks were placed in patient and in anatomical reference space, and the landmark distance after registration was quantified. Lower-grade gliomas were registered more accurately than glioblastoma. Registration accuracy for pre- and post-operative MR images did not differ. SPM5 and DARTEL registered tumors most accurate, and FSL least accurate. Non-linear transformations resulted in more accurate general brain alignment than linear transformations, but tumor alignment was similar between linear and non-linear transformation. We conclude that linear transformation suffices to summarize glioma locations in anatomical reference space.
为总结胶质瘤在患者群体中的位置分布，需要将个体磁共振成像（MR）配准到解剖学参考空间。在本研究中，我们使用胶质母细胞瘤和低级别胶质瘤的估计肿瘤靶点以及术前和术后时间点的解剖标志点，通过线性和非线性变换，使用六个常用的配准软件包（FSL、SPM5、DARTEL、ANTs、Elastix和NiftyReg），对MR图像到解剖学参考空间的配准准确性进行了量化。收集了20例胶质母细胞瘤患者和20例低级别胶质瘤患者的常规临床术前和术后增强T1加权图像。使用2009a蒙特利尔神经病学研究所脑模板作为解剖学参考空间。在患者空间中手动分割肿瘤，并将相应的健康组织在解剖学参考空间中划定为目标体积。使用Dice分数和豪斯多夫距离对肿瘤配准的准确性进行量化。为测量整体脑配准的准确性，在患者和解剖学参考空间中放置解剖标志点，并对配准后的标志点距离进行量化。低级别胶质瘤的配准比胶质母细胞瘤更准确。术前和术后MR图像的配准准确性没有差异。SPM5和DARTEL对肿瘤的配准最准确，而FSL最不准确。非线性变换导致的整体脑配准比线性变换更准确，但线性和非线性变换之间的肿瘤配准相似。我们得出结论，线性变换足以在解剖学参考空间中总结胶质瘤的位置。
