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Kinetic Compressive Sensing
[article]
2018
arXiv
pre-print
Parametric images provide insight into the spatial distribution of physiological parameters, but they are often extremely noisy, due to low SNR of tomographic data. Direct estimation from projections allows accurate noise modeling, improving the results of post-reconstruction fitting. We propose a method, which we name kinetic compressive sensing (KCS), based on a hierarchical Bayesian model and on a novel reconstruction algorithm, that encodes sparsity of kinetic parameters. Parametric maps
arXiv:1803.10045v1
fatcat:e5p6u4wfbzhmnbinkr5s3d7eqq