Abstract
This work concerns reduction of the MRI scan time through optimal sampling. We derive optimal sample positions from Cramér-Rao theory. These positions are nonuniformly distributed, which hampers Fourier transformation to the image domain. With the aid of Bayesian formalism we estimate an image that satisfies prior knowledge while its inverse Fourier transform is compatible with the acquired samples. The new technique is applied successfully to a real-world MRI scan of a human brain.
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© 1996 Kluwer Academic Publishers
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Marseille, G.J., de Beer, R., Fuderer, M., Mehlkopf, A.F., van Ormondt, D. (1996). Bayesian Estimation of MR Images from Incomplete Raw Data. In: Skilling, J., Sibisi, S. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 70. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0107-0_2
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DOI: https://doi.org/10.1007/978-94-009-0107-0_2
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6534-4
Online ISBN: 978-94-009-0107-0
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