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Fast unsupervised hot-spot detection in 1H-MR spectroscopic imaging data using ICA

: Harz, M.T.; Diehl, V.; Merkel, B.; Terwey, B.; Peitgen, H.O.


Pluim, J.P.W. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical imaging 2009. Image processing. Pt.1 : 8 - 10 February 2009, Lake Buena Vista, Florida, United States
Bellingham, WA: SPIE, 2009 (Proceedings of SPIE 7259)
ISBN: 978-0-8194-7510-7
ISSN: 1605-7422
Paper 72591X
Medical Imaging Conference <2009, Lake Buena Vista/Fla.>
Image Processing Conference <2009, Lake Buena Vista/Fla.>
Conference Paper
Fraunhofer MEVIS ()

Independent Component Analysis (ICA) is a blind source separation technique that has previously been applied to various time-varying signals. It may in particular be utilized to study 1H-MR spectroscopic imaging (MRSI) data. The work presented firstly investigates preprocessing and parameterization for ICA on simulated data to assess different strategies. We then applied ICA processing to 2D/3D brain and prostate MRSI data obtained from two healthy volunteers and 17 patients. We conducted a correlation analysis of the mixing and separating matrices resulting from ICA processing with maps obtained from metabolite quantitations in order to elucidate the relationship between quantitative and ICA results. We found that the mixing matrices corresponding to the estimated independent components highly correlate with the metabolite maps for some cases, and for others differ. We provide explanations and speculations for that and propose a scheme to utilize the knowledge for hot-spot detection. From our experience, ICA is much faster than the calculation of metabolic maps. Additionally, water and lipid contaminations are on the way removed from the data; the user needs not manually exclude spectroscopic voxels from processing or analysis. ICA results show hot spots in the data, even where quantitation-based metabolic maps are difficult to assess due to noisy data or macromolecule distortions.