Abstract

1. Bayesian perfusion estimation with PASL-MRI M. L. Rodrigues, P. Figueiredo and J.Miguel Sanches Institute for Systems and Robotics / Instituto Superior Técnico Lisboa, Portugal • 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. Figure 2: Sampling strategy of PASL • 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, ΔM(gray matter)=0.5 and ΔM(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. Figure 1: Schematic of the Arterial Spin Labeling procedure • Problem Formulation • The algorithm proposed is designed in a Bayesian framework, with the following observation model: • -Y:3 D matrix (nxmxl) (a stack of l images of n x m pixels); • - F (nxm) is the base morphological MRI image; • - D (nxmxl) represents the Drift; • - ΔM(nxm): magnetization difference measured; • -Γ~N(0,σy2)(nxmxl)Additive White Gaussian Noise (AWGN); • - v(l x 1) contains the labeling marks indicating which image among the sequence is labeled. • -The estimation of ΔM 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 RecPad2010 - 16th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th