To improve the fitting performance of a UTE relaxometry model, we proposed an iterative decomposition method of multi-compartment relaxometry with least square estimations (IDMCR), based on Gauss-Newton estimations and incorporating spatial constraints. Monte Carlo simulation showed that IDMCR provided robust and unbiased estimation of ultra-short T2* myelin fraction, which was further improved by adding spatial constraints. We examined the fitting algorithm in one healthy volunteer UTE relaxometry data and showed clear contrast of gray and white matter on the fitted myelin fraction map. The framework of IDMCR could also be easily adapted for other nonlinear parameter fitting problems.
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