Optimisation of arterial spin labelling using bayesian experimental design

Large-scale neuroimaging studies often use multiple individual imaging contrasts. Due to the finite time available for imaging,there is intense competition for the time allocated to the individual modalities; thus it is crucial to maximise the utility of each method given the resources available. Arterial Spin Labelled (ASL) MRI often forms part of such studies. Measuring perfusion of oxygenated blood in the brain is valuable for several diseases,but quantification using multiple inversion time ASL is time-consuming due to poor SNR and consequently slow acquisitions. Here,we apply Bayesian principles of experimental design to clinical-length ASL acquisitions,resulting in significant improvements to perfusion estimation. Using simulations and experimental data,we validate this approach for a five-minute ASL scan. Our design procedure can be constrained to any chosen scan duration,making it well-suited to improve a variety of ASL implementations. The potential for adaptation to other modalities makes this an attractive method for optimising acquisition in the time-pressured environment of neuroimaging studies