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A New Approach to Multi-exponential Relaxometry

23 January 2020

Markus Zimmermann, Ana-Maria Oros-Peusquens, Elene Iordanishvili, Seonyeong Shin, Seong Dae Yun, Zaheer Abbas, N. Jon Shah

The relaxation times of water molecules are dependent on the microstructural environment and, as such, can provide vital information about tissue damage in various neurological pathologies, such as multiple sclerosis, epilepsy, psychotic disorders, dementia, and traumatic brain injury.


Multi-exponential relaxometry is used to analyse the myelin water fraction and can help to detect related diseases. However, the methods currently used are extremely sensitive to noise and measurement imperfections, which can lead to less precise and more biased parameter estimates.


In this work, a novel regularized iterative multi-voxel NNLS approach for multi-exponential relaxometry, MERLIN, is presented. The proposed method improves the accuracy and precision of the estimated distributions by enforcing sparsity and spatial consistency.


Here, the proposed method is validated in simulations and in vivo experiments using a multi-echo adient-echo (MEGE) sequence at 3 T. MERLIN is compared to the conventional single-voxel L2-regularized NNLS (rNNLS) and a multi- voxel extension with spatial priors (rNNLS + SP), where it consistently showed lower root mean squared errors of up to 70 percent for all parameters of interest in these simulations.


In addition to improvements in accuracy, MERLIN offers a “plug‘n’play” approach for robust multi-exponential analysis as it is fully automated compared to previously published multi-voxel approaches.

Original publication:

Multi-Exponential Relaxometry Using ℓ1 -Regularized Iterative NNLS (MERLIN) With Application to Myelin Water Fraction Imaging


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