Abstract
A three-pool model was used to improve white-matter T2 relaxometry in low signal-to-noise (SNR) data. To verify the model very high SNR T2 relaxometry experiments were performed on myelinated tissue samples and three-pool fractions were consistently found. Relaxation curves based on the in vitro results were simulated with multiple SNRs and fit using the three-pool model and three less constraining nonnegative least squares-based methods. All methods performed well with noiseless data. At lower SNR values the three-pool model was superior, primarily due to the fact that the other methods often could not unambiguously calculate pool fractions.
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