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

Deformable image registration between Computed Tomography (CT) images andMagnetic Resonance (MR) imaging is essential for many image-guided therapies.In this paper, we propose a novel translation-based unsupervised deformableimage registration method. Distinct from other translation-based methods thatattempt to convert the multimodal problem (e.g., CT-to-MR) into a unimodalproblem (e.g., MR-to-MR) via image-to-image translation, our method leveragesthe deformation fields estimated from both: (i) the translated MR image and(ii) the original CT image in a dual-stream fashion, and automatically learnshow to fuse them to achieve better registration performance. The multimodalregistration network can be effectively trained by computationally efficientsimilarity metrics without any ground-truth deformation. Our method has beenevaluated on two clinical datasets and demonstrates promising results comparedto state-of-the-art traditional and learning-based methods.

Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration