Optimal PLD Design and Maximum Likelihood CBF Estimation for Dynamic PCASL with Rician Noise

Li Zhao¹, Craig H. Meyer¹, ²

Noise in low SNR ASL images is more accurately modeled as Rician rather than Gaussian. Least squares estimation is typically used in ASL, but this results in a biased estimate with Rician noise. This work describes a new maximum likelihood (ML) estimator and an optimal post-label delay (PLD) design for dynamic ASL assuming Rician noise. To verify the performance of CBF estimation, a simulation is performed based on low SNR dynamic ASL signal. The results show that the new ML estimator provides unbiased estimation and that optimal PLD design can reduce the variance of CBF estimation significantly.