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 displacement distribution of a water molecular is characterizedmathematically as Gaussianity without considering potential diffusion barriersand compartments. However, this is not true in real scenario: most biologicaltissues are comprised of cell membranes, various intracellular andextracellular spaces, and of other compartments, where the water diffusion isreferred to have a non-Gaussian distribution. Diffusion kurtosis imaging (DKI),recently considered to be one sensitive biomarker, is an extension of diffusiontensor imaging, which quantifies the degree of non-Gaussianity of thediffusion. This work proposes an efficient scheme of maximum likelihoodestimation (MLE) in DKI: we start from the Rician noise model of the signalintensities. By augmenting a Von-Mises distributed latent phase variable, theRician likelihood is transformed to a tractable joint density without loss ofgenerality. A fast computational method, an expectation-maximization (EM)algorithm for MLE is proposed in DKI. To guarantee the physical relevance ofthe diffusion kurtosis we apply the ternary quartic (TQ) parametrization toutilize its positivity, which imposes the upper bound to the kurtosis. AFisher-scoring method is used for achieving fast convergence of the individualdiffusion compartments. In addition, we use the barrier method to constrain thelower bound to the kurtosis. The proposed estimation scheme is conducted onboth synthetic and real data with an objective of healthy human brain. Wecompared the method with the other popular ones with promising performanceshown in the results.

An improved EM algorithm for solving MLE in constrained diffusion kurtosis imaging of human brain