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

This paper proposes a novel, unsupervised super-resolution (SR) approach forperforming the SR of a clinical CT into the resolution level of a micro CT($\mu$CT). The precise non-invasive diagnosis of lung cancer typically utilizesclinical CT data. Due to the resolution limitations of clinical CT (about $0.5\times 0.5 \times 0.5$ mm$^3$), it is difficult to obtain enough pathologicalinformation such as the invasion area at alveoli level. On the other hand,$\mu$CT scanning allows the acquisition of volumes of lung specimens with muchhigher resolution ($50 \times 50 \times 50 \mu {\rm m}^3$ or higher). Thus,super-resolution of clinical CT volume may be helpful for diagnosis of lungcancer. Typical SR methods require aligned pairs of low-resolution (LR) andhigh-resolution (HR) images for training. Unfortunately, obtaining pairedclinical CT and $\mu$CT volumes of human lung tissues is infeasible.Unsupervised SR methods are required that do not need paired LR and HR images.In this paper, we create corresponding clinical CT-$\mu$CT pairs by simulatingclinical CT images from $\mu$CT images by modified CycleGAN. After this, we usesimulated clinical CT-$\mu$CT image pairs to train an SR network based onSRGAN. Finally, we use the trained SR network to perform SR of the clinical CTimages. We compare our proposed method with another unsupervised SR method forclinical CT images named SR-CycleGAN. Experimental results demonstrate that theproposed method can successfully perform SR of clinical CT images of lungcancer patients with $\mu$CT level resolution, and quantitatively andqualitatively outperformed conventional method (SR-CycleGAN), improving theSSIM (structure similarity) form 0.40 to 0.51.

Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset