Impact of Bayesian-Inference vs Least-Squares Fitting on Multi-Delay Arterial Spin Labelling Parameter Estimation

Motivation: Multi-delay Arterial Spin Labelling has application across multiple patient groups, but accurate quantification remains difficult, particularly for prolonged transit times and noisy data. Goal(s): Compare least-squares and Bayesian-inference model fitting for perfusion estimate accuracy. Approach: Least-squares and Bayesian-inference, specifically BASIL, pipelines were run on simulated and in-vivo ASL data with different SNR with three choices of prior/initial value for arterial transit time (ATT). The resulting cerebral blood flow (CBF) and ATT maps were compared. Results: ATT quantification is impacted by ATT prior/initial value in Bayesian-inference fitting more than least-squares fitting. Least-squares fitting is more susceptible to CBF overestimation at lower SNR. Impact: MD-ASL analysis method can impact ATT accuracy. Bayesian-inference fitting is better for lower SNR data when CBF is the primary interest. Least-squares fitting is better for higher SNR data, when prior/estimate is not well known, and for accurate ATT estimation.