Improved Variational Denoising of Flow Fields with Application to Phase-Contrast MRI Data

We propose a new variational framework for the problem of reconstructing flow fields from noisy measurements. The formalism is based on regularizers penalizing the singular values of the Jacobian of the field. Specifically, we rely on the nuclear norm. Our method is invariant with respect to fundamental transformations and can be efficiently solved. We conduct numerical experiments on several phantom data and report improved performance compared to existing vectorial extensions of total variation and curl-divergence regularizations. Finally, we apply our reconstruction method to an experimentally-acquired phase-contrast MRI recording for enhancing the data visualization.


Published in:
Ieee Signal Processing Letters, 22, 6, 762-766
Year:
2015
Publisher:
Piscataway, Ieee-Inst Electrical Electronics Engineers Inc
ISSN:
1070-9908
Keywords:
Laboratories:




 Record created 2015-02-20, last modified 2018-03-18

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