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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on asequence of very highly undersampled images. In order to be able to perform MRFreconstruction, state-of-the-art MRF methods rely on priors such as the MRphysics (Bloch equations) and might also use some additional low-rank orspatial regularization. However to our knowledge these three regularizationsare not applied together in a joint reconstruction. The reason is that it isindeed challenging to incorporate effectively multiple regularizations in asingle MRF optimization algorithm. As a result most of these methods are notrobust to noise especially when the sequence length is short. In this paper, wepropose a family of new methods where spatial and low-rank regularizations, inaddition to the Bloch manifold regularization, are applied on the images. Weshow on digital phantom and NIST phantom scans, as well as volunteer scans thatthe proposed methods bring significant improvement in the quality of theestimated tissue maps.

GFB-MRF: A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting