Notes
Outline
Introduction to Perfusion Analysis in MEDx 3.3
Perfusion Package Developers
Evan D. Morris, Ph.D.
Talin A. Tasciyan, Ph.D.
John W. Van Meter, PhD.
Results  Preview
Perfusion - Theory
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.
Each building (pixel) has an inflow and an outflow.  But there are multiple paths through the building.
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
How to understand R(t)?
Thus, R(t) is - in effect - the impulse response as viewed from within the pixel. Recall:
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?
Good Modeling References
What is Volume Fraction, V?
 An analogy to understand CBV as relative capacity.
Consider a multiplex movie theatre
But, all theatres in the multiplex play the same movie.
People spread themselves across all theatres at constant density.
The fraction of patrons that enter a given theatre over all time is a measure of the relative size of that theatre.
V:  Total # people to enter is proportional to capacity
CBV - Assumptions
Getting Started with Perfusion in MEDx
Data Flow
Pre-processing of Perfusion Data - Parameters
Pre-processing of Perfusion Data -Masking
Masking - Why?
Perfusion - Report Setup
Report Setup - Proportionality Constants
Report Setup -
Conversion Factors
Perfusion -
 Arterial Input Function
Arterial Curve - Map Metrics
Hypothesis: the concentration signal from a prototypical “arterial” voxel can be identified by its shape & timing.
Arterial Curve - Map Metrics
Hypothesis: the concentration signal from a prototypical “arterial” voxel can be identified by its shape & timing.
Arterial Curve Selection - Supervised Mode
Arterial Map Metrics -
What do they find?
Auto Arterial Mode -
Scoring the AIFs
Given 3 (smoothed) candidate AIFs - one from each map metric…
Calculate Area under curve
Calculate Mean time of the distribution
Calculate the Variance in time
Calculate the Skew in time.
Assign each curve points as follows...
Arterial Map Metrics
Automatic Mode - Results
Manual Arterial Curve - Pixel Editor
Perfusion - Create Maps
Result Maps         include:
CBV (area under raw or fitted data, steady state ratio)
CBF (via deconvolved, integral of R)
MTT (via deconvolution, time-to-peak of raw data and fitted, area/base, integral of R)
X2 (normalized by square of curve height and number of points.)
Sample Results
Revisit the Data Flow
Create Maps - Parameters
Click on triangle icon to reveal optional parameters
Create Maps - Parameters
Set Take-off threshold
Set Recirculation threshold
Set SVD threshold
Set Temporal smoothing
Why a take-off threshold - 1.
A generalized Gamma-Variate function has 4 (estimatable) parameters t0, K, b, a :
Why a take-off threshold - 2.
Thus, we identify the take-off, t0 , by extrapolating from near-threshold points back to baseline.
Why a Recirculation Threshold ?
CBV- Effect of Recirculation Threshold
Why an SVD threshold? - 1
Why an SVD threshold? - 2
Setting the SVD Threshold - References
Effect of SVD threshold
on CBF and Residue Funct.
Characteristic Times - A Visual Comparison
MTT  = 7.6 + 1.6
       = CBV/CBF
MTT2 = 8.2 + 1.7
       = Area(C)/peak(C)
MTT3 = 6.2 + 1.5
       =  tpeak - t0
MTT4 = 7.1 + 1.6
      = area(FR)/max(FR)
MTTu = 4.0 + 1.0
       = Area/peak
          (raw data)
Characteristic Times - A Statistical Comparison
MTT  = 7.6 + 1.6
       = CBV/CBF
MTT2 = 8.2 + 1.7
       = Area(C)/peak(C)
MTT3 = 6.2 + 1.5
       =  tpeak - t0
MTT4 = 7.1 + 1.6
      = area(FR)/max(FR)
MTTu = 4.0 + 1.0
       = Area/peak
          (raw data)
Goodness of Fit - via Normalized Chi Square
Interactive Temporal Display
Temporal Display - How Good is the Fit?
Take-off time, Peak time, and Recirculation time are indicated on the plot associated with CBV images.
Fit quality can be assessed via the temporal plot and via the normalized Chi Square image.
Temporal Display - Residue Functions
Take-off time, CBF, MTT4 are indicated on the plot associated with CBV images.
Perfusion - Post-process
Normalize by ROI or Constant
Adaptive Median Filter (Spatial)
Talairach Transformation
Automatic Segmentation
Remove Arterial Pixels
Post-Process - Median Filter
Post-Process - Talairach Normalization
Post-Process - Segmentation
End of Presentation

...Additional    
Discussion
Slides
Q. What assumptions do we make in applying our simple model?
1.  Every pixel is supplied directly by the input.
2.  All dispersion of a bolus input is due to multiple path-lengths inside a ‘pixel’
3.  Feeding and draining vessels are ‘outside’ the pixel
What implications are there to our assumptions?
1.  An impulse input at the artery would arrive at the ‘pixel’ as an impulse.
2.  Measured CBF is an upper bound. Thus, MTT = CBV/CBF may be biased downward.
3.  Model is only valid for regions on the order of the size of the capillary bed.  I.e., with its own supplying arteriole and draining venule.
4.  Different tissue types may require different minimum pixels sizes
Consequence of BBB Leakage to Contrast Agent
If contrast agent does NOT stay wholly intravascular (as in case of damage to BBB),
and CBV is overestimated.