1. Abstract  Arterial Spin Labeling (ASL) is a noninvasive method for quantifying Cerebral Blood Flow (CBF).  The most common approach is to alternate between tagged and non tagged MRI images.  Averaging is then performed, in order to detect weak magnetization differences among control and labeled images.  A new method is proposed, in which the magnetization estimation problem is formulated in a Bayesian framework.  Spatial-temporal priors are used to deal with the ill-posed nature of the reconstruction task.  The rigid alternating tagging strategy constraint imposed in the traditional ASL methods is no longer needed.  Tested with synthetic and real data, the algorithm proposed has shown to outperform the traditional averaging methods used. Bayesian perfusion estimation with PASL-MRI RecPad2010 - 16th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th M. L. Rodrigues, P. Figueiredo and J.Miguel Sanches Institute for Systems and Robotics / Instituto Superior Técnico Lisboa, Portugal Experimental Results - To test the algorithm, a mask was created, similar to the human brain, with two different regions (white and gray matter). -A sequence of Monte-Carlo tests was performed, with the following values:σ=1, F =1000, Δ M (gray matter)=0.5 and Δ M (white matter)=1; -The values obtained pre-processing were: SNR(F)=80.0228dB and SNR(ΔM)= -2.20135dB. -The results reveal a major improvement in both the final SNR of the image (≈3dB) and the mean error ( 8%). -Applied to real data, images revealed less influence of noise and smoothing of areas of the same intensity. Also, better definition on the contours. -These are important results, that allow the decrease of long acquisition times necessary to acquire at multiple TI, without compromising image quality. Introduction Arterial spin labeling: 1.Arterial blood passing through the carotid is labeled with an inversion pulse; 2. After an Inversion Time (TI), the image is acquired; 3. The procedure is repeated, only this time no inversion pulse is applied. 4. Control image is acquired; 5. Subtracting the control and labeled images, a difference of magnetization is obtained, which is an indicator of CBF. Results – Comparison of the 3 methods Pair Wise subtraction Surround Subtraction Algorithm SNR(ΔM)(dB)12.22112.279615.6200 ISNR(ΔM)(dB)14.423114.481017.8214 Mean Error (%)23.4023.0715.12 Problem Formulation -The algorithm proposed is designed in a Bayesian framework, with the following observation model: - Y :3 D matrix ( n x m x l ) (a stack of l images of n x m pixels); - F ( n x m) is the base morphological MRI image; - D ( n x m x l ) represents the Drift; - Δ M ( n x m) : magnetization difference measured; -Γ~N(0,σ y 2 )( n x m x l )Additive White Gaussian Noise (AWGN); - v ( l x 1) contains the labeling marks indicating which image among the sequence is labeled. -The estimation of Δ M given the observations Y and the vector v is a ill-posed problem and prior information is needed to regularize the solution. -The Maximum A Posteriori (MAP) estimation problem can be formulated as: Figure 3: Processed images of synthetic data using the three methods Figure 4: Processed images of real data, using the three different methods: Top left - Pair-wise subtraction; Top right - surround subtraction; Bottom three - algorithm using different parameters Figure 1: Schematic of the Arterial Spin Labeling procedure Figure 2: Sampling strategy of PASL