Efficient and robust estimation of blood oxygenation levels in single cerebral veins

Blood oxygenation level is an important measure that can be used alongside functional magnetic resonance imaging data in order to obtain closer correlates of neuronal activation. A robust estimate of this measure has thus far not been demonstrated. This is mainly due to the lack of knowledge of the underlying parameters which influence the numerical estimates of blood oxygenation. In this paper, we present a systematic analysis of the estimation performance of venous hemoglobin oxygen saturation [Formula: see text] as a function of noise, physiologic and geometric parameters. Furthermore, we present a novel algorithm for estimating [Formula: see text] from the temporal decay of an MR signal. The proposed algorithm incorporates prior information about the functional dependence between [Formula: see text] and relaxation rates. We compare our algorithm to an existing method in the literature and analyze the estimation performance. We show that our proposed algorithm is more efficient, achieving gains in performance as high as 92 %. We also show how our estimation algorithm takes advantage of signal features that are specific to the underlying physiology and geometry. We argue that optimal acquisition sequences, and corresponding estimation methods, should take into account such features in order to obtain robust estimates of blood oxygenation.