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…
Effects of MRI Contrast Agents on the Stem Cell Phenotype
The ultimate therapy for ischemic stroke is restoration of blood supply in the ischemic region and regeneration of lost neural cells. This might be achieved by transplanting cells that differentiate into vascular or neuronal cell types, or secrete trophic factors that enhance self-renewal, recruitment, long-term survival and functional integration of endogenous stem/progenitor cells. Experimental stroke models have been developed to determine potential beneficial effect of stem/progenitor cell based therapies. To follow the fate of grafted cells in vivo, a number of non-invasive imaging approaches have been developed. Magnetic Resonance Imaging (MRI) is a high resolution, clinically relevan…
Quantifying brain tumor tissue abundance in HR-MAS spectra using non-negative blind source separation techniques
Given high-resolution magic angle spinning (HR-MAS) spectra from several glial tumor subjects, our goal is to differentiate between tumor tissue types by separating the different sources that contribute to the profile of each spectrum. Blind source separation techniques are applied for obtaining characteristic profiles for necrosis, highly cellular tumor and border tumor tissue and providing the contribution (abundance) of each of these tumor tissue types to the profile of each spectrum. The problem is formulated as a non-negative source separation problem. Non-negative matrix factorization, convex analysis of non-negative sources and non-negative independent component analysis methods are …