Correction of Partial Volume Effects in Arterial Spin Labeling MRI

By: Tracy SsaliMedical Biophysics 3970zApril 4th 2012Supervisors: Keith St. Lawrence, PhD

Udunna Anazodo, PhD candidate

Introduction

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

Theory – Why ASL MRI?

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

Theory - ASL MRI

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

ASL Image ≈ CBF

Subtract

Adapted from Wolf and Detre Neurother Vol. 4, 346–359, July 2007

Theory – How ASL MRI Works

Theory - Partial Volume Effects 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

Theory – Partial Volume Effects (Cont’d)

Point spread blurring

Resolution is not fine enough to resolve GM, WM and CSF Voxel size is

approximately 3 x 3 x 3mm [2]

Theory – Separating the Signals

Partial Volume Effects (PVE) correction Estimates the partial signal contribution

based on the contrast information from anatomical MRI image volume

Theory – Partial Volume Effects Correction

Kernel Regression Algorithm Based on the size of the kernel the

algorithm assesses a radius around the centre point to reassign a partial volume

Theory – Signal to Noise Ratio (SNR)

Signal – Mean signal of GM, WM or CSF

Noise – Standard deviation

20

40

Deibler et al AJNR march 2008

Objective

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

Methods

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

Methods (Cont’d)

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

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

Subject 1

Subject 2

Subject 3

Subject 4

Subject 5

0

0.5

1

1.5

2

SNR Before and After PVE correction

Uncorrected SNR PVE corrected SNR

CRPS Patient

Sig

nal to

Nois

e R

ati

o

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

Subject 1

Subject 2

Subject 3

Subject 4

Subject 5

00.5

11.5

22.5

3

SNR Before and After PVE correction

Uncorrected SNR PV corrected SNR

CRPS Patient

Sig

nal to

Nois

e R

ati

o

Subject 5 Results & Discussion

5 7 9 11 150

0.5

1

1.5

2

2.5

3

3.5

Kernel Size vs SNR

Uncorrected Signal Corrected Signal

Kernel Size

Sig

nal to

Nois

e R

ati

o

Larger kernel have a greater SNR

Small kernel sizes are sensitive to noise and variance

Conclusion

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

References

[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.

Thank youQuestions?