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• W2947022164 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." @default.
• W2947022164 created "2019-06-07" @default.
• W2947022164 creator A5044066676 @default.
• W2947022164 creator A5087571672 @default.
• W2947022164 creator A5091206345 @default.
• W2947022164 date "2019-10-01" @default.
• W2947022164 modified "2023-09-25" @default.
• W2947022164 title "Optimization of q-space sampling for mean apparent propagator MRI metrics using a genetic algorithm" @default.
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• W2947022164 doi "https://doi.org/10.1016/j.neuroimage.2019.05.078" @default.
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