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

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasivemeasurement technique capable of quantifying blood flow across thecardiovascular system. While practical use is limited by spatial resolution andimage noise, incorporation of trained super-resolution (SR) networks haspotential to enhance image quality post-scan. However, these efforts havepredominantly been restricted to narrowly defined cardiovascular domains, withlimited exploration of how SR performance extends across the cardiovascularsystem; a task aggravated by contrasting hemodynamic conditions apparent acrossthe cardiovasculature. The aim of our study was to explore the generalizabilityof SR 4D Flow MRI using a combination of heterogeneous training sets anddedicated ensemble learning. With synthetic training data generated acrossthree disparate domains (cardiac, aortic, cerebrovascular), varyingconvolutional base and ensemble learners were evaluated as a function of domainand architecture, quantifying performance on both in-silico and acquiredin-vivo data from the same three domains. Results show that both bagging andstacking ensembling enhance SR performance across domains, accuratelypredicting high-resolution velocities from low-resolution input data in-silico.Likewise, optimized networks successfully recover native resolution velocitiesfrom downsampled in-vivo data, as well as show qualitative potential ingenerating denoised SR-images from clinical level input data. In conclusion,our work presents a viable approach for generalized SR 4D Flow MRI, withensemble learning extending utility across various clinical areas of interest.

Generalized super-resolution 4D Flow MRI $\unicode{x2013}$ using ensemble learning to extend across the cardiovascular system