Abstract #3440

Multicomponent Diffusion Analysis using L1-norm Regularized NNLS for an Accurate and Robust Detection of Alternations in Spinal Cord

Jin Gao^1,2, Weiguo Li^2,3, Richard Magin³, and Danilo Erricolo^1,3

¹Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, United States, ²Research Resources Center, University of Illinois at Chicago, Chicago, IL, United States, ³Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States

Multiple components analysis of nuclear magnetic resonance (MR) relaxation data using L₂-norm regularized non-negative least squares (NNLS) method has been widely used in myelin imaging for neurological diseases. When this analysis is applied to diffusion-weighted MR imaging to investigate water diffusion properties of biological tissues, noise corruption becomes a major problem which affects the accuracy and robustness of results. In this study, a L₁-norm regularized method was developed to process diffusion-weighted MRI data from spinal cords of amyotrophic lateral sclerosis affected mice.

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