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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