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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

CT images are widely used in pathology detection and follow-up treatmentprocedures. Accurate identification of pathological features requiresdiagnostic quality CT images with minimal noise and artifact variation. In thiswork, a novel Fourier-transform based metric for image quality (IQ) estimationis presented that correlates to additive CT image noise. In the proposedmethod, two windowed CT image subset regions are analyzed together to identifythe extent of variation in the corresponding Fourier-domain spectrum. The twosquare windows are chosen such that their center pixels coincide and one windowis a subset of the other. The Fourier-domain spectral difference between thesetwo sub-sampled windows is then used to isolate spatial regions-of-interest(ROI) with low signal variation (ROI-LV) and high signal variation (ROI-HV),respectively. Finally, the spatial variance ($var$), standard deviation($std$), coefficient of variance ($cov$) and the fraction of abdominal ROIpixels in ROI-LV ($\nu'(q)$), are analyzed with respect to CT image noise. Forthe phantom CT images, $var$ and $std$ correlate to CT image noise ($|r|>0.76$($p\ll0.001$)), though not as well as $\nu'(q)$ ($r=0.96$ ($p\ll0.001$)).However, for the combined phantom and patient CT images, $var$ and $std$ do notcorrelate well with CT image noise ($|r|<0.46$ ($p\ll0.001$)) as compared to$\nu'(q)$ ($r=0.95$ ($p\ll0.001$)). Thus, the proposed method and the metric,$\nu'(q)$, can be useful to quantitatively estimate CT image noise.

Automated Selection of Uniform Regions for CT Image Quality Detection