Quantitative single point imaging with compressed sensing |
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| Authors: | P. Parasoglou D. Malioutov A.J. Sederman J. Rasburn H. Powell L.F. Gladden A. Blake M.L. Johns |
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| Affiliation: | aDepartment of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site Pembroke Street, Cambridge CB2 3RA, UK;bMicrosoft Research Ltd., 7 J J Thompson Ave., Cambridge CB3 0FB, UK;cNestec York Ltd., Nestlé Product Technology Centre, Haxby Road, P.O. Box 204, York YO91 1XY, UK |
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| Abstract: | A novel approach with respect to single point imaging (SPI), compressed sensing, is presented here that is shown to significantly reduce the loss of accuracy of reconstructed images from under-sampled acquisition data. SPI complements compressed sensing extremely well as it allows unconstrained selection of sampling trajectories. Dynamic processes featuring short  NMR signal can thus be more rapidly imaged, in our case the absorption of moisture by a cereal-based wafer material, with minimal loss of image quantification. The absolute moisture content distribution is recovered via a series of images acquired with variable phase encoding times allowing extrapolation to time zero for each image pixel and the effective removal of  contrast. |
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| Keywords: | Compressed sensing SPI Under-sampling k-space |
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