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Abstract #3494

Optimal Kinetic PASL Design and CBF Estimation with Low SNR and Rician Noise

Li Zhao1, Craig Meyer1, 2

1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States; 2Radiology, University of Virginia, Charlottesville, VA, United States


By designing optimal observation times (TI) in dynamic PASL, we can achieve more accurate estimation of CBF. Here, we compare optimal designs for the high/low SNR case, White Gaussian/Rician noise model, and the results from L1/L2 norm estimation. The results show a) optimal sampling design gives accurate estimation, b) low SNR, Rician noise could result biased estimation.

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