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 RecPad th 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)= dB and SNR(ΔM)= dB. -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) ISNR(ΔM)(dB) Mean Error (%) 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