Autor(es):
Fouto, Ana R. ; Henriques, Rafael N. ; Golub, Marc ; Freitas, Andreia C. ; Ruiz-Tagle, Amparo ; Esteves, Inês ; Gil-Gouveia, Raquel ; Silva, Nuno A. ; Vilela, Pedro ; Figueiredo, Patrícia ; Nunes, Rita G.
Data: 2024
Identificador Persistente: http://hdl.handle.net/10400.14/44421
Origem: Veritati - Repositório Institucional da Universidade Católica Portuguesa
Assunto(s): Diffusion MRI (dMRI); Diffusion tensor imaging (DTI); Diffusional kurtosis imaging (DKI); Histogram-metrics; Subsampling
Descrição
Objective Difusional kurtosis imaging (DKI) extends difusion tensor imaging (DTI), characterizing non-Gaussian difusion efects but requires longer acquisition times. To ensure the robustness of DKI parameters, data acquisition ordering should be optimized allowing for scan interruptions or shortening. Three methodologies were used to examine how reduced difusion MRI scans impact DKI histogram-metrics: 1) the electrostatic repulsion model (OptEEM); 2) spherical codes (OptSC); 3) random (RandomTRUNC). Materials and methods Pre-acquired difusion multi-shell data from 14 female healthy volunteers (29±5 years) were used to generate reordered data. For each strategy, subsets containing diferent amounts of the full dataset were generated. The subsampling efects were assessed on histogram-based DKI metrics from tract-based spatial statistics (TBSS) skeletonized maps. To evaluate each subsampling method on simulated data at diferent SNRs and the infuence of subsampling on in vivo data, we used a 3-way and 2-way repeated measures ANOVA, respectively. Results Simulations showed that subsampling had diferent efects depending on DKI parameter, with fractional anisotropy the most stable (up to 5% error) and radial kurtosis the least stable (up to 26% error). RandomTRUNC performed the worst while the others showed comparable results. Furthermore, the impact of subsampling varied across distinct histogram characteristics, the peak value the least afected (OptEEM: up to 5% error; OptSC: up to 7% error) and peak height (OptEEM: up to 8% error; OptSC: up to 11% error) the most afected. Conclusion The impact of truncation depends on specifc histogram-based DKI metrics. The use of a strategy for optimizing the acquisition order is advisable to improve DKI robustness to exam interruptions.
