Impact of truncating diffusion MRI scans on diffusional kurtosis imaging.

Autor: Fouto AR; Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal. anafouto@tecnico.ulisboa.pt., Henriques RN; Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal., Golub M; Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal., Freitas AC; Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal., Ruiz-Tagle A; Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal., Esteves I; Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal., Gil-Gouveia R; Neurology Department, Hospital da Luz, Lisbon, Portugal.; Center for Interdisciplinary Research in Health, Universidade Católica Portuguesa, Lisbon, Portugal., Silva NA; Learning Health, Hospital da Luz, Lisbon, Portugal., Vilela P; Imaging Department, Hospital da Luz, Lisbon, Portugal., Figueiredo P; Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal., Nunes RG; Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
Jazyk: angličtina
Zdroj: Magma (New York, N.Y.) [MAGMA] 2024 Oct; Vol. 37 (5), pp. 859-872. Date of Electronic Publication: 2024 Feb 23.
DOI: 10.1007/s10334-024-01153-y
Abstrakt: Objective: Diffusional kurtosis imaging (DKI) extends diffusion tensor imaging (DTI), characterizing non-Gaussian diffusion effects 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 diffusion MRI scans impact DKI histogram-metrics: 1) the electrostatic repulsion model (Opt EEM ); 2) spherical codes (Opt SC ); 3) random (Random TRUNC ).
Materials and Methods: Pre-acquired diffusion multi-shell data from 14 female healthy volunteers (29±5 years) were used to generate reordered data. For each strategy, subsets containing different amounts of the full dataset were generated. The subsampling effects were assessed on histogram-based DKI metrics from tract-based spatial statistics (TBSS) skeletonized maps. To evaluate each subsampling method on simulated data at different SNRs and the influence of subsampling on in vivo data, we used a 3-way and 2-way repeated measures ANOVA, respectively.
Results: Simulations showed that subsampling had different effects depending on DKI parameter, with fractional anisotropy the most stable (up to 5% error) and radial kurtosis the least stable (up to 26% error). Random TRUNC performed the worst while the others showed comparable results. Furthermore, the impact of subsampling varied across distinct histogram characteristics, the peak value the least affected (Opt EEM : up to 5% error; Opt SC : up to 7% error) and peak height (Opt EEM : up to 8% error; Opt SC : up to 11% error) the most affected.
Conclusion: The impact of truncation depends on specific histogram-based DKI metrics. The use of a strategy for optimizing the acquisition order is advisable to improve DKI robustness to exam interruptions.
(© 2024. The Author(s).)
Databáze: MEDLINE
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