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Abstract #3383

Double Diffusion Encoding vs Single Diffusion Encoding in Parameter Estimation of Biophysical Models in Diffusion-Weighted MRI

Santiago Coelho1, Leandro Beltrachini1,2,3, Jose M. Pozo1, and Alejandro F. Frangi1

1Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB), Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, United Kingdom, 2School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom, 3Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom

Biophysical tissue models are a solid tool for obtaining specific biomarkers with diffusion MRI. However, the assumptions they rely on are sometimes inaccurate and may lead to erroneous results. Some limitations of the Neurite Orientation Dispersion and Density Imaging (NODDI) model are tackled by NODDIDA (NODDI with Diffusivities Added), at the cost of an extended acquisition protocol. Here we adapt NODDIDA to a Double Diffusion Encoding scheme to improve the parameter estimation for reduced acquisition protocols. We demonstrate through in silico experiments that under similar experimental conditions, this novel approach increases both the accuracy and precision of the parameter estimates.

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