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

Positron emission tomography (PET) is a cornerstone of modern radiology. Theability to detect cancer and metastases in whole body scans fundamentallychanged cancer diagnosis and treatment. One of the main bottlenecks in theclinical application is the time it takes to reconstruct the anatomical imagefrom the deluge of data in PET imaging. State-of-the art methods based onexpectation maximization can take hours for a single patient and depend onmanual fine-tuning. This results not only in financial burden for hospitals butmore importantly leads to less efficient patient handling, evaluation, andultimately diagnosis and treatment for patients. To overcome this problem wepresent a novel PET image reconstruction technique based on a deepconvolutional encoder-decoder network, that takes PET sinogram data as inputand directly outputs full PET images. Using realistic simulated data, wedemonstrate that our network is able to reconstruct images >100 times faster,and with comparable image quality (in terms of root mean squared error)relative to conventional iterative reconstruction techniques.

DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem