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Impact of truncating diffusion MRI scans on diffusional kurtosis imaging.

Ana R Fouto1, Rafael N Henriques2, Marc Golub3

  • 1Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal. anafouto@tecnico.ulisboa.pt.

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Summary

Optimizing diffusion MRI scan order improves robustness of diffusion kurtosis imaging (DKI) metrics. Strategies like electrostatic repulsion and spherical codes are better than random truncation for shorter scans.

Keywords:
Diffusion MRI (dMRI)Diffusion tensor imaging (DTI)Diffusional kurtosis imaging (DKI)Histogram-metricsSubsampling

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Area of Science:

  • Neuroimaging
  • Quantitative MRI

Background:

  • Diffusional kurtosis imaging (DKI) offers advanced characterization of diffusion compared to diffusion tensor imaging (DTI).
  • DKI requires longer acquisition times, posing challenges for scan robustness and interruptions.
  • Optimizing data acquisition order is crucial for reliable DKI parameter estimation.

Purpose of the Study:

  • To evaluate the impact of reduced diffusion MRI scan durations on histogram-based DKI metrics.
  • To compare the effectiveness of different data acquisition ordering strategies: electrostatic repulsion model (OptEEM), spherical codes (OptSC), and random truncation (RandomTRUNC).

Main Methods:

  • Diffusion multi-shell data from 14 healthy volunteers were reordered using OptEEM, OptSC, and RandomTRUNC strategies.
  • Subsets of varying sizes were generated to simulate shortened scans.
  • Effects on histogram-based DKI metrics were assessed using tract-based spatial statistics (TBSS) skeletonized maps.
  • Simulations and repeated measures ANOVA were used to evaluate subsampling effects on data with varying signal-to-noise ratios (SNRs) and in vivo data.

Main Results:

  • Subsampling effects varied by DKI parameter; fractional anisotropy was most stable (up to 5% error), while radial kurtosis was least stable (up to 26% error).
  • RandomTRUNC performed worst, while OptEEM and OptSC showed comparable results.
  • Histogram peak value was least affected (up to 7% error), while peak height was most affected (up to 11% error).

Conclusions:

  • The impact of scan shortening on DKI metrics depends on the specific metric and histogram characteristic.
  • Employing optimized acquisition order strategies enhances DKI robustness against scan interruptions or shortening.
  • This optimization is advisable for clinical applications requiring efficient MRI acquisition.