This method demonstrates low bias in estimating tissue relaxivity, suggesting improved stability in relaxometry applications.
Motivation: Relaxometry techniques based on RF-phase cycled bSSFP are highly SNR efficient but prone to bias due to their sensitivity to unwanted variation over the course of an experiment. Goal(s): Propose an alternative short-TR relaxometry technique that is motion-robust and drift-insensitive. Approach: An N-periodic SSFP acquisition is used to establish a single pseudo-steady-state, and the magnetisation behaviour is fit to an analytical model to yield tissue relaxivity estimates. This is compared to an established approach Results: The proposed approach shows low bias in simulations across a range of realistic experimental conditions, and its performance is comparable to existing bSSFP approaches in vivo. Impact: We introduce a novel and flexible approach for SSFP-based relaxometry, which shows advantages over existing approaches in its robustness to slow variations over an experiment.
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Beitone et al. (2025) studied this question.
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