English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

A crucial issue in two-dimensional Nuclear Magnetic Resonance (NMR) is thespeed and accuracy of the data inversion. This paper proposes a multi-penaltymethod with locally adapted regularization parameters for fast and accurateinversion of 2DNMR data.  The method solves an unconstrained optimization problem whose objectivecontains a data-fitting term, a single $L1$ penalty parameter and a multipleparameter $L2$ penalty. We propose an adaptation of the Fast IterativeShrinkage and Thresholding (FISTA) method to solve the multi-penaltyminimization problem, and an automatic procedure to compute all the penaltyparameters. This procedure generalizes the Uniform Penalty principle introducedin [Bortolotti et al., \emph{Inverse Problems}, 33(1), 2016].  The proposed approach allows us to obtain accurate relaxation timedistributions while keeping short the computation time. Results of numericalexperiments on synthetic and real data prove that the proposed method isefficient and effective in reconstructing the peaks and the flat regions thatusually characterize NMR relaxation time distributions.

2DNMR data inversion using locally adapted multi-penalty regularization