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/

Zendy Plus

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

Zendy Tools

Zendy Open

Gating is commonly used in PET imaging to reduce respiratory motion blurringand facilitate more sophisticated motion correction methods. In theapplications of low dose PET, however, reducing injection dose causes increasednoise and reduces signal-to-noise ratio (SNR), subsequently corrupting themotion estimation/correction steps, causing inferior image quality. To tacklethese issues, we first propose a Siamese adversarial network (SAN) that canefficiently recover high dose gated image volume from low dose gated imagevolume. To ensure the appearance consistency between the recovered gatedvolumes, we then utilize a pre-trained motion estimation network incorporatedinto SAN that enables the constraint of gate-to-gate (G2G) consistency. Withhigh-quality recovered gated volumes, gate-to-gate motion vectors can besimultaneously outputted from the motion estimation network. Comprehensiveevaluations on a low dose gated PET dataset of 29 subjects demonstrate that ourmethod can effectively recover the low dose gated PET volumes, with an averagePSNR of 37.16 and SSIM of 0.97, and simultaneously generate robust motionestimation that could benefit subsequent motion corrections.

Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning