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Abstract #0731

Optimal experimental design in multidimensional diffusion MRI for parameter estimation of biophysical tissue models

Santiago Coelho1,2, Jose M Pozo1, Sune N Jespersen2,3,4, Alejandro F Frangi1, Dmitry S Novikov2, and Els Fieremans2
1Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB), School of Computing and School of Medicine, University of Leeds, Leeds, United Kingdom, 2Radiology, School of Medicine, New York University, New York City, NY, United States, 3Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark, 4Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark

It was recently shown that multidimensional diffusion MRI enables well-posed estimation of the Standard Model (SM) for diffusion in white matter. However, various multidimensional acquisitions can achieve this, and there are currently no criteria for efficient data acquisition for SM. We propose an optimal experiment design framework based on Cramér-Rao bounds to maximise accuracy and precision of SM parameter estimation. We explore the high-dimensional continuous acquisition space and identify the optimal combination of b-tensors that minimises estimation error. Simulations and in vivo experiments demonstrate that our optimised acquisition has a reduced estimation error on all SM microstructural parameters.

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