Li Yuqian, Pi Yiming, Liu Xin, Liu Yuhan, Van Cauter Sofie
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Department of Radiology and Department of Imaging and Pathology, University Hospitals of Leuven, 3001 Leuven, Belgium.
Biomed Res Int. 2014;2014:762126. doi: 10.1155/2014/762126. Epub 2014 Mar 3.
Glioblastoma multiforme (GBM) is characterized by high infiltration. The interpretation of MRSI data, especially for GBMs, is still challenging. Unsupervised methods based on NMF by Li et al. (2013, NMR in Biomedicine) and Li et al. (2013, IEEE Transactions on Biomedical Engineering) have been proposed for glioma recognition, but the tissue types is still not well interpreted. As an extension of the previous work, a tissue type assignment method is proposed for GBMs based on the analysis of MRSI data and tissue distribution information. The tissue type assignment method uses the values from the distribution maps of all three tissue types to interpret all the information in one new map and color encodes each voxel to indicate the tissue type. Experiments carried out on in vivo MRSI data show the feasibility of the proposed method. This method provides an efficient way for GBM tissue type assignment and helps to display information of MRSI data in a way that is easy to interpret.
多形性胶质母细胞瘤（GBM）的特点是具有高度浸润性。磁共振波谱成像（MRSI）数据的解读，尤其是对于GBM来说，仍然具有挑战性。Li等人（2013年，《NMR in Biomedicine》）和Li等人（2013年，《IEEE Transactions on Biomedical Engineering》）提出了基于非负矩阵分解（NMF）的无监督方法用于胶质瘤识别，但组织类型仍未得到很好的解读。作为先前工作的延伸，基于对MRSI数据和组织分布信息的分析，提出了一种针对GBM的组织类型分配方法。该组织类型分配方法使用来自所有三种组织类型分布图的值，在一个新图中解读所有信息，并对每个体素进行颜色编码以指示组织类型。对体内MRSI数据进行的实验表明了该方法的可行性。此方法为GBM组织类型分配提供了一种有效途径，并有助于以易于解读的方式显示MRSI数据的信息。
