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Abstract #1255

Multi-Regularization Reconstruction of One-Dimensional $$$ T_2$$$ Distributions

Chuan Bi1, Miao-Jung Yvonne Ou1, Wenshu Qian2, You Zhuo2, and Richard G Spencer2

1Department of Mathematical Sciences, Uiversity of Delaware, Newark, DE, United States, 2National Institute on Aging, National Institutes of Health, Baltimore, MD, United States

Tikhonov regularization and related methods are widely used in recovering relaxation time distributions in magnetic resonance relaxometry. Regularization optimization methods such as the L-curve and generalized cross-validation (GCV) identify a single optimized solution as the best approximation to the underlying distribution. In contrast, we propose a new reconstruction method, Multi-Reg, incorporating a range of regularized solutions. Multi-Reg is based on a dictionary of noise-corrupted regularized reconstructions of distribution basis functions. We demonstrate that Multi-Reg can out-perform L-curve or GCV analyses in simulation analyses of Gaussian distribution components, and with experimental results on mouse spinal cord and human muscle.

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