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

Many real-world signal sources are complex-valued, having real and imaginarycomponents. However, the vast majority of existing deep learning platforms andnetwork architectures do not support the use of complex-valued data. MRI datais inherently complex-valued, so existing approaches discard the richeralgebraic structure of the complex data. In this work, we investigateend-to-end complex-valued convolutional neural networks - specifically, forimage reconstruction in lieu of two-channel real-valued networks. We apply thisto magnetic resonance imaging reconstruction for the purpose of acceleratingscan times and determine the performance of various promising complex-valuedactivation functions. We find that complex-valued CNNs with complex-valuedconvolutions provide superior reconstructions compared to real-valuedconvolutions with the same number of trainable parameters, over a variety ofnetwork architectures and datasets.

Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction