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Abstract #3363

Rapid and Accurate NODDI Parameter Estimation with the Spherical Mean Technique

Ryan P Cabeen1, Farshid Sepehrband1, and Arthur W Toga1

1Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States

Neurite orientation dispersion and density imaging (NODDI) is a widely used tool for modeling microstructure using diffusion MRI, but its computational cost can be prohibitively expensive. This work investigates the efficacy of integrating the spherical mean technique (SMT) into a non-linear optimization framework to improve NODDI parameter estimation. Through quantitative simulation, comparative, and reliability analyses, we found that integrating SMT into more traditional non-linear optimization enables rapid, accurate, and reliable estimation of neurite density and dispersion compared to other approaches.

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