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

The resurgence of deep neural networks has created an alternative pathway forlow-dose computed tomography denoising by learning a nonlinear transformationfunction between low-dose CT (LDCT) and normal-dose CT (NDCT) image pairs.However, those paired LDCT and NDCT images are rarely available in the clinicalenvironment, making deep neural network deployment infeasible. This studyproposes a novel method for self-supervised low-dose CT denoising to alleviatethe requirement of paired LDCT and NDCT images. Specifically, we have trainedan invertible neural network to minimize the pixel-based mean square distancebetween a noisy slice and the average of its two immediate adjacent noisyslices. We have shown the aforementioned is similar to training a neuralnetwork to minimize the distance between clean NDCT and noisy LDCT image pairs.Again, during the reverse mapping of the invertible network, the output imageis mapped to the original input image, similar to cycle consistency loss.Finally, the trained invertible network's forward mapping is used for denoisingLDCT images. Extensive experiments on two publicly available datasets showedthat our method performs favourably against other existing unsupervisedmethods.

Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence