1. By: Tracy Ssali Medical Biophysics 3970z April 4 th 2012 Supervisors: Keith St. Lawrence, PhD Udunna Anazodo, PhD candidate

2.  Brain Tissue consists of Grey Matter (GM) White Matter (WM) and Cerebral Spinal Fluid (CSF)  Perfusion in the brain is indicative of function  Irregular flow in grey matter is indicative of disease state [1]  Arterial Spin Labeling is a novel technique used to measure perfusion in the brain

3.  Established techniques require radioactive exogenous tracers which cannot be used on certain patient populations, and require long clearance times.[2]  ASL MRI uses magnetized water molecules in arterial blood as a tracer to measure tissue perfusion non-invasively

4.  Creating the control image  Creating the tagged image  Arterial blood is magnetically labeled using radiofrequency pulses  A delay time is allowed for the blood to reach the brain  When the labeled water interacts with the magnetic field, it affects the signal being produced

5. ASL Image ≈ CBF Subtract Adapted from Wolf and Detre Neurother Vol. 4, 346–359, July 2007

6.  Perfusion images are taken in quick succession  Information from the labeled blood must be captured before it relaxes  Blood water has a half life of around 1-2s[3] Pettersen Br J Radiology 2006

7.  Point spread blurring  Resolution is not fine enough to resolve GM, WM and CSF  Voxel size is approximately 3 x 3 x 3mm [2]

8.  Partial Volume Effects (PVE) correction  Estimates the partial signal contribution based on the contrast information from anatomical MRI image volume

9.  Kernel Regression Algorithm  Based on the size of the kernel the algorithm assesses a radius around the centre point to reassign a partial volume

10.  Signal – Mean signal of GM, WM or CSF  Noise – Standard deviation 20 40 Deibler et al AJNR march 2008

11.  To measure the signal to noise ratio before and after the partial volume effects correction

12.  5 Chronic Regional Pain Syndrome Patients (CRPS)  Image Preprocessing  Remove the pixels representing the skull  Motion correction Wolf and Detre Neurother Vol. 4, 346–359, July 2007

13.  PVE correction  Implemented an In-house written MATLAB code created by Asllani et al. [2]  Images from 5CRPS were processed using a kernel size of 5 and 9  Kernel filter  Adjust the kernel size from to 5, 7, 9, 11 and 15  1 patient’s data

14. K = 5 Results & Discussion  Significant decrease (P<0.05) in the SNR  Variation in the voxels due to the noise could have prevented the code from working as intended

15. K = 9 Results & Discussion  Inconsistent Results  There is no significant difference (P>0.05)  It is likely that the algorithm is not functioning as intended

16. Subject 5Results & Discussion  Larger kernel have a greater SNR  Small kernel sizes are sensitive to noise and variance

17.  The SNR decreased after the PVE correction  Kernel size needs to be chosen carefully  Our in-house implementation of the PVE correction needs to be fine tuned

18.  [1] Tracy, Melzer R. "Arterial Spin Labelling Reveals an Abnormal Cerebral Perfusion Pattern in Parkinson’s Disease." Brain 134.3 (2011): 845-55.  [2] Asllani, Iris, Ajna Borogovac, and Truman R. Brown. "Regression Algorithm Correcting for Partial Volume Effects in Arterial Spin Labeling MRI." Magnetic Resonance in Medicine 60.6 (2008): 1362-371.  [3] Xu, Guofan, Howard A. Rowley, Gaohong Wu, David C. Alsop, Ajit Shankaranarayanan, Maritza Dowling, Bradley T. Christian, Terrence R. Oakes, and Sterling C. Johnson. "Reliability and Precision of Pseudo- continuous Arterial Spin Labeling Perfusion MRI on 3.0 T and Comparison with 15O-water PET in Elderly Subjects at Risk for Alzheimer’s Disease." NMR in Biomedicine 23.3 (2010): 286-93. Print.

19. Questions?