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

Signal space models in both phase-encode, and frequency-encode directions arepresented for extrapolation of 2D partial kspace. Using the boxcarrepresentation of low-resolution spatial data, and a geometrical representationof signal space vectors in both positive and negative phase-encode directions,a robust predictor is constructed using a series of signal space projections.Compared to some of the existing phase-correction methods that requireacquisition of a pre-determined set of fractional kspace lines, the proposedpredictor is found to be more efficient, due to its capability of exhibiting anequivalent degree of performance using only half the number of fractionallines. Robust filtering of noisy data is achieved using a second signal spacemodel in the frequency-encode direction, bypassing the requirement of a priorhighpass filtering operation. The signal space is constructed from FourierTransformed samples of each row in the low-resolution image. A set of FIRfilters are estimated by fitting a least squares model to this signal space.Partial kspace extrapolation using the FIR filters is shown to result inartifact-free reconstruction, particularly in respect of Gibbs ringing andstreaking type artifacts.

Least-Squares FIR Models of Low-Resolution MR data for Efficient Phase-Error Compensation with Simultaneous Artefact Removal