0000000000414712
AUTHOR
Teresa Laudadio
Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI
Brain metastases and glioblastoma multiforme are the most aggressive and common brain tumours in adults and they require a different clinical management. Anatomical magnetic resonance imaging (MRI) or clinical history, cannot always clearly distinguish between them. This study describes and verifies the use of magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging (MRSI) in combination with MRI for differential diagnosis of glioblastomas and metastases. Feature selection methods are applied to the magnetic resonance (MR) spectra of 121 patients and relevant features are detected. Different classification methods are used to distinguish glioblastoma multiforme and…
Fusingin vivoandex vivoNMR sources of information for brain tumor classification
In this study we classify short echo-time brain magnetic resonance spectroscopic imaging (MRSI) data by applying a model-based canonical correlation analyses algorithm and by using, as prior knowledge, multimodal sources of information coming from high-resolution magic angle spinning (HR-MAS), MRSI and magnetic resonance imaging. The potential and limitations of fusing in vivo and ex vivo nuclear magnetic resonance sources to detect brain tumors is investigated. We present various modalities for multimodal data fusion, study the effect and the impact of using multimodal information for classifying MRSI brain glial tumors data and analyze which parameters influence the classification results…
Fast nosological imaging using canonical correlation analysis of brain data obtained by two-dimensional turbo spectroscopic imaging.
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 s…
Incorporating in vivo and ex vivo NMR sources of information for modeling robust brain tumor classifiers
The purpose of this paper is to investigate the potential and limitations of using multimodal sources of information coming from in vivo NMR and ex vivo NMR data for detecting brain tumors. Supervised pattern recognition methods, whose performance directly depends on the prior available observations used in building them, are proposed. We show that high resolution magic angle spinning (HR-MAS) data act as complementary information for classifying magnetic resonance spectroscopic imaging (MRSI) data. In particularly, when considering rare brain tumors, since it is unlikely to acquire sufficient cases to define their metabolite profiles using only in vivo NMR information, HR-MAS can support t…
Combining HR-MAS and In Vivo MRI and MRSI Information for Robust Brain Tumor Recognition
In this study we propose to classify short echotime brain MRSI data by using multimodal information coming from magnetic resonance imaging (MRI), magnetic resonance spectroscopic imaging (MRSI) and high resolution magic angle spinning (HR-MAS), and to develop an advanced pattern recognition method that could help clinicians in diagnosing brain tumors. We study the impact of using HR-MAS information in combination with in vivo information for classifying brain tumors and we investigate which parameters influence our classification results.