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.
We present a novel software assistant for the analysis of multi-voxel 2D or 3D in-vivo-spectroscopy signals based on the
rapid-prototyping platform MeVisLab. Magnetic Resonance Spectroscopy (MRS) is a valuable in-vivo metabolic
window into tissue regions of interest, such as the brain, breast or prostate. With this method, the metabolic state can be
investigated non-invasively. Different pathologies evoke characteristically different MRS signals, e.g., in prostate cancer,
choline levels increase while citrate levels decrease compared to benign tissue. Concerning the majority of processing
steps, available MRS tools lack performance in terms of speed. Our goal is to support clinicians in a fast and robust
interpretation of MRS signals and to enable them to interactively work with large volumetric data sets. These data sets
consist of 3D spatially resolved measurements of metabolite signals. The software assistant provides standard analysis
methods for MRS data including data import and filtering, spatio-temporal Fourier transformation, and basic calculation
of peak areas and spectroscopic metabolic maps. Visualization relies on the facilities of MeVisLab, a platform for
developing clinically applicable software assistants. It is augmented by special-purpose viewing extensions and offers
synchronized 1D, 2D, and 3D views of spectra and metabolic maps. A novelty in MRS processing tools is the side-by-side
viewing ability of standard FT processed spectra with the results of time-domain frequency analysis algorithms like
Linear Prediction and the Matrix Pencil Method. This enables research into the optimal toolset and workflow required to
avoid misinterpretation and misapplication.
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