Faculty Collaboration Database

Optimization of q-space sampling for mean apparent propagator MRI metrics using a genetic algorithm. Neuroimage 2019 Oct 01;199:237-244

Date

06/05/2019

Pubmed ID

31163267

DOI

10.1016/j.neuroimage.2019.05.078

Scopus ID

2-s2.0-85066738169 (requires institutional sign-in at Scopus site)   10 Citations

Abstract

Mean Apparent Propagator (MAP) MRI is a recently introduced technique to estimate the diffusion probability density function (PDF) robustly. Using the estimated PDF, MAP MRI then calculates zero-displacement and non-Gaussianity metrics, which might better characterize tissue microstructure compared to diffusion tensor imaging or diffusion kurtosis imaging. However, intensive q-space sampling required for MAP MRI limits its widespread adoption. A reduced q-space sampling scheme that maintains the accuracy of the derived metrics would make it more practical. A heuristic approach for acquiring MAP MRI with fewer q-space samples has been introduced earlier with scan duration of less than 10 minutes. However, the sampling scheme was not optimized systematically to preserve the accuracy of the model metrics. In this work, a genetic algorithm is implemented to determine optimal q-space subsampling schemes for MAP MRI that will keep total scan time under 10 min. Results show that the metrics derived from the optimized schemes more closely match those computed from the full set, especially in dense fiber tracts such as the corpus callosum.

Author List

Olson DV, Arpinar VE, Muftuler LT

Author

Lutfi Tugan Muftuler PhD Professor in the Neurosurgery department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Adult
Algorithms
Brain
Computational Biology
Data Interpretation, Statistical
Humans
Magnetic Resonance Imaging
Male
Models, Biological
Neuroimaging