FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3168456197> ?p ?o ?g. }

• W3168456197 endingPage "118234" @default.
• W3168456197 startingPage "118234" @default.
• W3168456197 abstract "Neurite Orientation Dispersion and Density Imaging (NODDI) and Bingham-NODDI diffusion MRI models are nowadays very well-known models in the field of diffusion MRI as they represent powerful tools for the estimation of brain microstructure. In order to efficiently translate NODDI imaging findings into the diagnostic clinical practice, a test-retest approach would be useful to assess reproducibility and reliability of NODDI biomarkers, thus providing validation on precision of different fitting toolboxes. In this context, we conducted a test-retest study with the aim to assess the effects of different factors (i.e. fitting algorithms, multiband acceleration, shell configuration, age of subject and hemispheric side) on diffusion models reliability, assessed in terms of Intra-class Correlation Coefficient (ICC) and Variation Factor (VF). To this purpose, data from pediatric and adult subjects were acquired with Simultaneous-MultiSlice (SMS) imaging method with two different acceleration factor (AF) and four b-values, subsequently combined in seven shell configurations. Data were then fitted with two different GPU-based algorithms to speed up the analysis. Results show that each factor investigated had a significant effect on reliability of several diffusion parameters. Particularly, both datasets reveal very good ICC values for higher AF, suggesting that faster acquisitions do not jeopardize the reliability and are useful to decrease motion artifacts. Although very small reliability differences appear when comparing shell configurations, more extensive diffusion parameters variability results when considering shell configuration with lower b-values, especially for simple model like NODDI. Also fitting tools have a significant effect on reliability, but their difference occurs in both datasets and AF, so it appears to be independent from either misalignment and motion artifacts, or noise and SNR. The main achievement of the present study is to show how 10 min multi-shell diffusion MRI acquisition for NODDI acquisition can have reliable results in WM. More complex models do not appear to be more prone to less data acquisition as well as noisier data thus stressing the idea of Bingham-NODDI having greater sensitivity to true subject variability." @default.
• W3168456197 created "2021-06-22" @default.
• W3168456197 creator A5016965442 @default.
• W3168456197 creator A5028231630 @default.
• W3168456197 creator A5049751421 @default.
• W3168456197 creator A5058512355 @default.
• W3168456197 creator A5058773364 @default.
• W3168456197 creator A5068415839 @default.
• W3168456197 creator A5073262191 @default.
• W3168456197 date "2021-09-01" @default.
• W3168456197 modified "2023-10-12" @default.
• W3168456197 title "Reliability on multiband diffusion NODDI models: A test retest study on children and adults" @default.
• W3168456197 cites W1601441088 @default.
• W3168456197 cites W1970713332 @default.
• W3168456197 cites W1972336188 @default.
• W3168456197 cites W1984453610 @default.
• W3168456197 cites W2006096283 @default.
• W3168456197 cites W2006894862 @default.
• W3168456197 cites W2011015491 @default.
• W3168456197 cites W2027094605 @default.
• W3168456197 cites W2032254014 @default.
• W3168456197 cites W2036146354 @default.
• W3168456197 cites W2041706109 @default.
• W3168456197 cites W2057591576 @default.
• W3168456197 cites W2067724039 @default.
• W3168456197 cites W2077526668 @default.
• W3168456197 cites W2077559791 @default.
• W3168456197 cites W2099769099 @default.
• W3168456197 cites W2141403362 @default.
• W3168456197 cites W2150667092 @default.
• W3168456197 cites W2162723250 @default.
• W3168456197 cites W2177917702 @default.
• W3168456197 cites W2198243321 @default.
• W3168456197 cites W2283761130 @default.
• W3168456197 cites W2327037637 @default.
• W3168456197 cites W2397498493 @default.
• W3168456197 cites W2500850421 @default.
• W3168456197 cites W2508881670 @default.
• W3168456197 cites W2508982726 @default.
• W3168456197 cites W2559605011 @default.
• W3168456197 cites W2563519255 @default.
• W3168456197 cites W2607969057 @default.
• W3168456197 cites W2614895229 @default.
• W3168456197 cites W2734984108 @default.
• W3168456197 cites W2745316278 @default.
• W3168456197 cites W2745760221 @default.
• W3168456197 cites W2760933032 @default.
• W3168456197 cites W2792306715 @default.
• W3168456197 cites W2884183601 @default.
• W3168456197 cites W2887241590 @default.
• W3168456197 cites W2894044595 @default.
• W3168456197 cites W2901466843 @default.
• W3168456197 cites W2911870465 @default.
• W3168456197 cites W2913612087 @default.
• W3168456197 cites W2942862165 @default.
• W3168456197 cites W2955932350 @default.
• W3168456197 cites W2956846297 @default.
• W3168456197 cites W2964773824 @default.
• W3168456197 cites W2975734873 @default.
• W3168456197 cites W2996863565 @default.
• W3168456197 cites W3007946690 @default.
• W3168456197 cites W3043095919 @default.
• W3168456197 cites W3047684611 @default.
• W3168456197 cites W3049304068 @default.
• W3168456197 doi "https://doi.org/10.1016/j.neuroimage.2021.118234" @default.
• W3168456197 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34091031" @default.
• W3168456197 hasPublicationYear "2021" @default.
• W3168456197 type Work @default.
• W3168456197 sameAs 3168456197 @default.
• W3168456197 citedByCount "10" @default.
• W3168456197 countsByYear W31684561972021 @default.
• W3168456197 countsByYear W31684561972022 @default.
• W3168456197 countsByYear W31684561972023 @default.
• W3168456197 crossrefType "journal-article" @default.
• W3168456197 hasAuthorship W3168456197A5016965442 @default.
• W3168456197 hasAuthorship W3168456197A5028231630 @default.
• W3168456197 hasAuthorship W3168456197A5049751421 @default.
• W3168456197 hasAuthorship W3168456197A5058512355 @default.
• W3168456197 hasAuthorship W3168456197A5058773364 @default.
• W3168456197 hasAuthorship W3168456197A5068415839 @default.
• W3168456197 hasAuthorship W3168456197A5073262191 @default.
• W3168456197 hasBestOaLocation W31684561971 @default.
• W3168456197 hasConcept C105795698 @default.
• W3168456197 hasConcept C121332964 @default.
• W3168456197 hasConcept C151730666 @default.
• W3168456197 hasConcept C154945302 @default.
• W3168456197 hasConcept C163258240 @default.
• W3168456197 hasConcept C2779343474 @default.
• W3168456197 hasConcept C33923547 @default.
• W3168456197 hasConcept C41008148 @default.
• W3168456197 hasConcept C43214815 @default.
• W3168456197 hasConcept C62520636 @default.
• W3168456197 hasConcept C69357855 @default.
• W3168456197 hasConcept C86803240 @default.
• W3168456197 hasConcept C97355855 @default.
• W3168456197 hasConcept C9893847 @default.
• W3168456197 hasConceptScore W3168456197C105795698 @default.
• W3168456197 hasConceptScore W3168456197C121332964 @default.

• next