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Abstract #3112

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Erick Jorge Canales-Rodríguez1,2,3, Jean-Philippe Thiran1,2, and Alessandro Daducci1,2,4

1Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, 2Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 3FIDMAG Germanes Hospitaláries, Barcelona, Spain, 4Computer Science Department, University of Verona, Verona, Italy

In this study we developed a robust inversion algorithm to estimate the Neurite Orientation Dispersion and Density Imaging (NODDI) model. It is based on the Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework. However, in contrast to AMICO, the proposed method relies on realistic MRI noise models. Moreover, it allows to take into account the underlying spatial continuity of the brain image by including a total variation regularization term. In simulated data the new method was effective in reducing the outliers, producing results more close to the ground-truth and with lower variability. The method was also evaluated on real data.

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