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

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 ( ) 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 -maps, with higher contrast-to-noise ratio.

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