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Optimization of q-space sampling for mean apparent propagator MRI metrics using a genetic algorithm.

Daniel V Olson1, Volkan E Arpinar2, L Tugan Muftuler3

  • 1Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA; Magnetic Resonance Imaging, GE Healthcare, Waukesha, WI, USA.

Neuroimage
|June 5, 2019
PubMed
Summary

Optimizing Mean Apparent Propagator (MAP) MRI acquisition with a genetic algorithm significantly reduces scan time to under 10 minutes. This new method accurately captures tissue microstructure metrics, enhancing its clinical practicality.

Keywords:
Genetic algorithmMean apparent propagator (MAP)Optimizationq-space sampling

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Medical Physics

Background:

  • Mean Apparent Propagator (MAP) MRI robustly estimates the diffusion probability density function (PDF).
  • MAP MRI metrics may offer superior tissue microstructure characterization compared to diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI).
  • Current MAP MRI requires intensive q-space sampling, limiting its clinical application.

Purpose of the Study:

  • To develop and validate an optimized, reduced q-space sampling scheme for MAP MRI.
  • To maintain the accuracy of MAP MRI-derived metrics with significantly reduced scan times.
  • To enable practical, widespread adoption of MAP MRI in clinical settings.

Main Methods:

  • Implementation of a genetic algorithm to systematically determine optimal q-space subsampling schemes.
  • Focus on maintaining total scan duration under 10 minutes.
  • Comparison of metrics derived from optimized subsampling with those from full q-space sampling.

Main Results:

  • Optimized q-space subsampling schemes were identified using a genetic algorithm.
  • MAP MRI scans were successfully acquired with total durations under 10 minutes.
  • Metrics derived from optimized schemes closely matched those from full sampling, particularly in dense white matter tracts like the corpus callosum.

Conclusions:

  • A genetic algorithm can optimize MAP MRI q-space sampling for reduced scan times.
  • Optimized MAP MRI protocols maintain high accuracy in characterizing tissue microstructure.
  • This approach enhances the clinical feasibility and accessibility of advanced diffusion MRI techniques.