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Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level
Published on: January 24, 2025
Fast nosological imaging using canonical correlation analysis of brain data obtained by two-dimensional turbo
Teresa Laudadio1, M Carmen Martínez-Bisbal, Bernardo Celda
1Department of Electrical Engineering, Division ESAT-SCD, Katholieke Universiteit Leuven, Leuven-Heverlee, Belgium. laudadio@esat.kuleuven.be
Abstract:
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.
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