We introduce a novel data fitting procedure of multi compartmentmodels of the brain white matter for diffusion MRI (dMRI) data. These biophysical models aim to characterize important micro-structure quantities like axonal radius, density and orientations. In order to describe the underlying tissue properties, a variety of models for intra-/extra-axonal diffusion signals have been proposed. Combinations of these analytic models are used to predict the diffusion MRI signal in multi-compartment settings. However, parameter estimation from these multicompartment models is an ill-posed problem. Consequently, many existing fitting algorithms either rely on an initial grid search to find a good start point, or have strong assumptions like single fiber orientation to estimate some of these parameters from simpler models like the diffusion tensor (DT). In both cases, there is a tradeoff between computational complexity and accuracy of the estimated parameters. Here, we describe a novel algorithm based on the separation of the Nonlinear Least Squares (NLLS) fitting problem, via Variable Projection Method, to search for nonlinearly and linearly entering parameters independently. We use stochastic global search algorithms to find a global minimum, while estimating non-linearly entering parameters. The approach is independent of any starting point, and does not rely on estimates from simpler models. We show that the suggested algorithm is faster than algorithms involving grid search, and its greater accuracy and robustness are demonstrated on synthetic as well as ex-/in-vivo data.
|Original language||English (US)|
|Title of host publication||Computational Diffusion MRI - MICCAI Workshop, 2015|
|Editors||Yogesh Rathi, Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Marco Reisert|
|Number of pages||10|
|State||Published - 2016|
|Event||Workshop on Computational Diffusion MRI, MICCAI 2015 - Munich, Germany|
Duration: Oct 9 2015 → Oct 9 2015
|Name||Mathematics and Visualization|
|Other||Workshop on Computational Diffusion MRI, MICCAI 2015|
|Period||10/9/15 → 10/9/15|
Bibliographical noteFunding Information:
Work partly supported by NIH grants P41 EB015894, P30 NS076408, R01 EB008432, Human Connectome Project (U54 MH091657) and Fulbright Program.
© Springer International Publishing Switzerland 2016.