Introduction to Perfusion Analysis in MEDx 3.3
Lets examine the ‘Perfusion’ of this system. What’s the ‘model’?
Q. What is the ‘perfusion’ of people within a single region (i.e., building)?
Lets examine this single region in detail.
Method: Inject an “impulse” of runners into the system, then monitor their arrival(s) downstream.
Lets further idealize the picture
What is the impulse response, h(t)?
Where to make our observations?
But, consider our actual observation points...
Consequence of Observer Location
Practically, we image a convo-lution of the Residue function.
What’s in a shape? What does the shape of R(t) mean?
Q. How do our observations
relate to the
histogram of transit times, h(t)?
What is MTT in terms of the residue function, R(t)? - 1.
What is MTT in terms of the residue function, R(t)? - 2.
What is MTT in terms of the residue function, R(t)? - 3.
Why is the Output Equation Scaled by the Flow Arriving at the Pixel?
An analogy to understand CBV as relative capacity.
V: Total # people to enter is proportional to capacity
Getting Started with Perfusion in MEDx
Pre-processing of Perfusion Data - Parameters
Pre-processing of Perfusion Data -Masking
Report Setup - Proportionality Constants
Report Setup -
Conversion Factors
Perfusion -
Arterial
Input Function
Arterial Curve Selection - Supervised Mode
Arterial Map Metrics -
What do they find?
Auto Arterial Mode
-
Scoring the AIFs
Arterial Map Metrics
Automatic Mode - Results
Manual Arterial Curve - Pixel Editor
Why a Recirculation Threshold ?
CBV- Effect of Recirculation Threshold
Setting the SVD Threshold - References
Effect of SVD
threshold
on CBF and Residue Funct.
Characteristic Times - A Visual Comparison
Characteristic Times - A Statistical Comparison
Goodness of Fit - via Normalized Chi Square
Temporal Display - How Good is the Fit?
Temporal Display - Residue Functions
Post-Process - Talairach Normalization
End of
Presentation
...Additional
Discussion
Slides
Q. What assumptions do we make in applying our simple model?