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Abstract #2982

Hybrid Parallel Tempering and Levenberg-Marquardt method for efficient and stable fitting of noisy MRI dataset

Marco Palombo 1,2 , Matthias Vandesquille 1,2 , and Julien Valette 1,2

1 CEA/DSV/I2BM/MIRCen, Fontenay-aux-Roses, France, France, 2 CEA-CNRS URA 2210, Fontenay-aux-Roses, France, France

Here we propose a hybrid scheme of Parallel Tempering (PT) and Levenberg-Marquardt (LM) approaches, named PT/LM, for optimization problems of non-linear models. The aim is to design an efficient and stable pipeline for non-Gaussian diffusion metrics estimation from noisy diffusion-weighted MRI data. Diffusional kurtosis (K) and stretched exponential ( lower case Greek gamma ) models were investigated. Our numerical and experimental results, performed on ex-vivo healthy mouse brain at 11.7T, demonstrate that the proposed novel hybrid scheme improves the efficiency and stability of conventional LM based pipelines, providing less grainy non-Gaussian K- and lower case Greek gamma -maps, with higher contrast-to-noise ratio.

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