Introduction

1. Bayesian optimization of perfusion and transit time estimation in PASL-MRI N.J. Santos1,2, J.M. Sanches1, P. Figueiredo1 1Institute for Systems and Robotics / Instituto Superior Técnico; Lisboa, Portugal 2Siemens, S.A. – HealthCare Sector; Freixieiro, Portugal Methods Monte Carlo Simulations were performed in order to test the performance of the proposed algorithm in the estimation of the parameters f and Δt with: • Introduction • Pulsed Arterial Spin Labeling (PASL) techniques potentially allow the measurement of brain perfusion and arterial transit time (ATT), by fitting a kinetic two-compartment model to the data acquired at multiple inversion time points. • A Bayesian estimation method based on a Maximum a Posteriori (MAP) criterion is proposed to estimate perfusion and ATT, using a priori information. Level of Noise β (%) = [10, 50, 75, 100, 125, 150] Problem Formulation Let y = [y1,…,yN] be a set of observations A Bayesian framework based on the MAP criterion is proposed to determine the optimal parameters θ: The energy function is given as: The optimization procedure is accomplished by the Levenberg-Marquardt algorithm. Sampling Strategies PASL signal ΔM as a function of TI, according to the two compartment kinetic model. Estimation methods Least Squares (LS); our proposed Bayesian method. Results data fidelity term prior term Estimated Values of of f (top) and Δt (bottom), with LS method. Estimated Values of of f (top) and Δt (bottom), with Bayesian method. Mean Square estimation error of f (top) and Δt (middle), and ISNR (bottom). Conclusion Our results obtained from Monte Carlo simulations indicate that PASL perfusion and ATT measurements would benefit from a Bayesian approach on the optimization of sampling strategies and estimation methods. RecPad2010 - 16th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th