Laudadio Teresa, Martínez-Bisbal M Carmen, Celda Bernardo, Van Huffel Sabine
Department of Electrical Engineering, Division ESAT-SCD, Katholieke Universiteit Leuven, Leuven-Heverlee, Belgium.
NMR Biomed. 2008 May;21(4):311-21. doi: 10.1002/nbm.1190.
A new fast and accurate tissue typing technique has recently been successfully applied to prostate MR spectroscopic imaging (MRSI) data. This technique is based on canonical correlation analysis (CCA), a statistical method able to simultaneously exploit the spectral and spatial information characterizing the MRSI data. Here, the performance of CCA is further investigated by using brain data obtained by two-dimensional turbo spectroscopic imaging (2DTSI) from patients affected by glioblastoma. The purpose of this study is to investigate the applicability of CCA when typing tissues of heterogeneous tumors. The performance of CCA is also compared with that of ordinary correlation analysis on simulated as well as in vivo data. The results show that CCA outperforms ordinary correlation analysis in terms of robustness and accuracy.
一种新的快速且准确的组织分型技术最近已成功应用于前列腺磁共振波谱成像（MRSI）数据。该技术基于典型相关分析（CCA），这是一种能够同时利用表征MRSI数据的光谱和空间信息的统计方法。在此，通过使用来自胶质母细胞瘤患者的二维涡轮光谱成像（2DTSI）获得的脑数据，进一步研究了CCA的性能。本研究的目的是调查CCA在对异质性肿瘤组织进行分型时的适用性。还将CCA的性能与普通相关分析在模拟数据和体内数据上的性能进行了比较。结果表明，在稳健性和准确性方面，CCA优于普通相关分析。
