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Abstract #4092

Iterative decomposition of multi-compartment relaxometry with least square estimations (IDMCR) for UTE relaxometry in brain

Jingwen Yao1, Nikhil Deveshwar1,2, and Peder E. Z. Larson1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 2Graduate Program in Bioengineering, UCSF/UC Berkeley, San Francisco, CA, United States


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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