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

Ultra sparse-view computed tomography (CT) algorithms can reduce radiationexposure of patients, but those algorithms lack an explicit cycle consistencyloss minimization and an explicit log-likelihood maximization in testing. Here,we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of athree-dimensional (3D) chest CT image from a single or a few two-dimensional(2D) projection images using a progressive flow-based deep generative model,especially for ultra low-dose protocols. The MAP reconstruction cansimultaneously optimize the cycle consistency loss and the log-likelihood. Theproposed algorithm is built upon a newly developed progressive flow-based deepgenerative model, which is featured with exact log-likelihood estimation,efficient sampling, and progressive learning. We applied X2CT-FLOW toreconstruction of 3D chest CT images from biplanar projection images withoutnoise contamination (assuming a standard-dose protocol) and with strong noisecontamination (assuming an ultra low-dose protocol). With the standard-doseprotocol, our images reconstructed from 2D projected images and 3D ground-truthCT images showed good agreement in terms of structural similarity (SSIM, 0.7675on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), meanabsolute error (MAE, 0.02364 on average), and normalized root mean square error(NRMSE, 0.05731 on average). Moreover, with the ultra low-dose protocol, ourimages reconstructed from 2D projected images and the 3D ground-truth CT imagesalso showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dBon average), MAE (0.02991 on average), and NRMSE (0.07349 on average).

X2CT-FLOW: Maximum a posteriori reconstruction using a progressive  flow-based deep generative model for ultra sparse-view computed tomography in  ultra low-dose protocols