Diapositivo 1

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 valuess1, F1000, ?M(gray
  matter)0.5 and ?M (white matter)1
• The values obtained pre-processing were
  SNR(F)80.0228dB and SNR(?M) -2.20135dB.
• -The results reveal a major improvement in both
  the final SNR of the image (3dB) and the mean
  error ( 8).

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(?M)(dB) 12.221 12.2796 15.6200
ISNR(?M)(dB) 14.4231 14.4810 17.8214
Mean Error () 23.40 23.07 15.12
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 (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
• - ?M (n x m) magnetization difference measured
  
• -GN(0,sy2)(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 ?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
RecPad2010 - 16th edition of the Portuguese
Conference on Pattern Recognition, UTAD
University, Vila Real city, October 29th