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Linear signal combination T2 spectroscopy
Institution:1. University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany;2. Siemens Healthcare Pty Ltd, Magnetic Resonance, 160 Herring Road, Macquarie Park NSW, 2113, Australia;1. Department of Medicine–Section of Cardiology, University of Chicago Medical Center, Chicago, IL, USA;2. Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA;3. Department of Radiology, University of Bonn, Bonn, Germany;4. Department of Internal Medicine/Cardiology, German Heart Center Berlin, Berlin, Germany;5. Department of Cardiology, Charité-University-Medicine Berlin, Germany;6. DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Germany;1. Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA;2. Department of Neurology, University of Minnesota, Minneapolis, MN, USA
Abstract:A technique is presented for performing T2 spectroscopy in magnetic resonance imaging (MRI). It is based on a weighted linear combination of T2 decay data. The data is combined in a manner that acts like a filter on the T2 spectrum. The choice of weighting coefficients determines the filter specifications (e.g. passband/stopband locations, stopband suppression factors). To perform spectroscopy, a series of filters are designed with narrow passbands centered about consecutive regions of the T2 spectrum. This provides an estimate of every region of the spectrum. Taken together, an initial estimate of the full T2 spectrum is thus obtained. However, the filtering process causes a distortion of the estimate relative to the true spectrum. To reduce this distortion, deconvolution is performed. The characteristics of the technique are first evaluated through simulation. The technique is then applied to experimental MRI data to demonstrate practical feasibility. T2 spectroscopy falls into a class of problems requiring inverse transformation with a set of exponential basis functions (i.e. the Laplace Transform). It is demonstrated how the present technique may be applied to problems involving non-exponential basis functions as well.
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