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

Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as aground truth tool for the validation of microstructure models forDiffusion-Weighted MRI. However, methodological pitfalls in the design of thebiomimicking geometrical configurations and the simulation parameters can leadto approximation biases. Such pitfalls affect the reliability of the estimatedsignal, as well as its validity and reproducibility as ground truth data. Inthis work, we first present a set of experiments in order to study threecritical pitfalls encountered in the design of MCDS in the literature, namely,the number of simulated particles and time steps, simplifications in theintra-axonal substrate representation, and the impact of the substrate's sizeon the signal stemming from the extra-axonal space. The results obtained showimportant changes in the simulated signals and the recovered microstructurefeatures when changes in those parameters are introduced. Thereupon, driven byour findings from the first studies, we outline a general framework able togenerate complex substrates. We show the framework's capability to overcome theaforementioned simplifications by generating a complex crossing substrate,which preserves the volume in the crossing area and achieves a high packingdensity. The results presented in this work,along with the simulator developed,pave the way towards more realistic and reproducible Monte-Carlo simulationsfor Diffusion-Weighted MRI.

Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the substrate complexity and parameter choice on the reproducibility of results