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 Imaging (MRI) offers high-resolution \emph{in vivo}imaging and rich functional and anatomical multimodality tissue contrast. Inpractice, however, there are challenges associated with considerations ofscanning costs, patient comfort, and scanning time that constrain how much datacan be acquired in clinical or research studies. In this paper, we explore thepossibility of generating high-resolution and multimodal images fromlow-resolution single-modality imagery. We propose the weakly-supervised jointconvolutional sparse coding to simultaneously solve the problems ofsuper-resolution (SR) and cross-modality image synthesis. The learning processrequires only a few registered multimodal image pairs as the training set.Additionally, the quality of the joint dictionary learning can be improvedusing a larger set of unpaired images. To combine unpaired data from differentimage resolutions/modalities, a hetero-domain image alignment term is proposed.Local image neighborhoods are naturally preserved by operating on the wholeimage domain (as opposed to image patches) and using joint convolutional sparsecoding. The paired images are enhanced in the joint learning process withunpaired data and an additional maximum mean discrepancy term, which minimizesthe dissimilarity between their feature distributions. Experiments show thatthe proposed method outperforms state-of-the-art techniques on both SRreconstruction and simultaneous SR and cross-modality synthesis.

Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding