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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In dynamic MRI, sufficient time resolution can often only be obtained usingimaging protocols which produce undersampled data for each image in the timeseries. This has led to the popularity of compressed sensing (CS) based imagereconstruction approaches. One of the problems in CS approaches is determiningthe regularization parameters, which control the balance between data fidelitythe spatial and temporal regularization terms. A data-driven approach isproposed for the total variation regularization parameter selection such thatthe reconstructions yield expected sparsity levels in the regularizationdomains. The expected sparsity levels are obtained from the measurement datafor the temporal regularization and from a reference image for the spatialregularization. Two formulations are proposed. The first is a 2D search for aparameter pair which produces expected sparsity in both the temporal andspatial regularization domains. In the second approach, the sparsity-basedparameter selection is split to two 1D searches using the S-curve method. Theapproaches are evaluated using simulated and experimental DCE-MRI. In thesimulated test case, both proposed methods produce a parameter pair that isclose to the RMSE optimal pair, and the reconstruction error is also close tominimum. In the experimental test case, the methods produce almost similarparameter selection, and the reconstructions are of high perceived quality.Both approaches lead to a highly feasible selection of the temporal and spatialregularization parameters in both the simulated and experimental test caseswhile the sequential method is computationally more efficient.

Data-driven regularization parameter selection in dynamic MRI