1. Variability of Ischemic Core and Penumbra using CT Perfusion in Acute Ischemic Stroke
M. Reddy, A. Livorine, R. Naini, H. Sucharew, A. Vagal
University of Cincinnati Neuroscience Institute
Poster No: EP-65
Control No:

2. Disclosures Mahati Reddy : None Achala Vagal :
CCTST CT2 Research Award
PI, Imaging Core Lab, PRISMS trial,
Genentech, Inc.
Anthony Livorine : None
Rohit Naini : None
Heidi Sucharew : None

3. Purpose
Objectives : To assess the intra-observer variability for quantifying ischemic core and penumbra values using automated, semi-automated or manual post processing techniques To assess the variability for different deconvolution algorithms using a commercially available perfusion package

4. Materials
30 randomly selected ischemic stroke patients from Interventional Management of Stroke (IMS III) trial data set
Post processing techniques using Olea Medical® version 2.3:
Manual
Automated
Semi – Automated
Deconvolution algorithms:
Standard Singular Value Decompostion (sSVD)
Block Circulant Matrix Singular Value Decomposition (cSVD)
Oscillation Index Based Singular Value Decomposition (oSVD)
Bayesian Estimation

5. Methods
CTP analysis was performed by a single observer using a commercially available software
Penumbral Volumes were calculated using a threshold of Tmax > 6
Ischemic Core value was calculated using dual threshold with relative CBF < 30 % and Tmax > 6
MTT
CBF

6. Methods
Single Value Decomposition (SVD) is an algebraic process by which the MTT maps are deconvolved from time-concentration curves for an arterial / venous region of interest. sSVD : measurement of CBF using intravascular tracer bolus passage; sensitive tracer delay effect cSVD : estimates perfusion parameters independent of delay of contrast in the AIF and arrival of contrast in the tissue oSVD : iterative method which repeats the cSVD process until oscillation in the residue function is below a threshold Bayesian estimation directly estimates residue function of brain tissues by applying Bayesian probability theory on the intravascular tracer model. It is inherently tracer-delay insensitive.
Sasaki, Makoto, et al. "Assessment of the accuracy of a Bayesian estimation algorithm for perfusion CT by using a digital phantom." Neuroradiology 55.10 (2013):
Bjørnerud, Atle, and Kyrre E. Emblem. "A fully automated method for quantitative cerebral hemodynamic analysis using DSC–MRI." Journal of Cerebral Blood Flow & Metabolism 30.5 (2010):

7. Methods
Volumes calculated using three different post processing techniques
Manual = manual selection of arterial input function (AIF) and venous input function (VOF)
Semi-automated = allows user adjustment of AIF and VOF when deemed appropriate
Automated = automatic selection of AIF and VOF
AIF
VOF

8. Methods + 5 patients excluded due to : motion artifact
Truncated Time Curve
5 patients excluded due to :
motion artifact
significantly truncated time curves + =
Motion Artifact

9. Methods Bland – Altman Analysis
Quantified variability with Bland-Altman analysis1 using repeatability coefficient and coefficient of variation
Bland – Altman Analysis
Identify relative difference between 2 observations by:
Plotting difference between the numerical value vs the numerical mean of the 2 values 2
1. Bland, J. Martin, and DouglasG Altman. "Statistical methods for assessing agreement between two methods of clinical measurement." The lancet (1986):
2. Waaijer, A., et al. "Reproducibility of quantitative CT brain perfusion measurements in patients with symptomatic unilateral carotid artery stenosis."American journal of neuroradiology 28.5 (2007):

10. Lower Repeatability Coefficient Lower Coefficient of Variation
Methods
Repeatability Coefficient = When comparing 2 methods, repeatability coefficient is a threshold within which 95 % of the absolute difference values lie.
Lower Repeatability Coefficient
Better Agreement
Coefficient of Variation = When comparing 2 methods, coefficient of variation is the ratio of repeatability coefficient over mean of the two values.
Lower Coefficient of Variation
Better Agreement
1. Bland, J. Martin, and DouglasG Altman. "Statistical methods for assessing agreement between two methods of clinical measurement." The lancet (1986):
2. Soares, Bruno P., et al. "Automated versus manual post-processing of perfusion-CT data in patients with acute cerebral ischemia: influence on interobserver variability." Neuroradiology 51.7 (2009):

11. Automated vs Semi-Automated Manual vs Semi-Automated
Results
VERY HIGH VARIABILITY was observed in ischemic core quantification in all three post processing techniques (manual, semi-automated and automated)
Sample Mean
Standard Deviation
Repeatability
Variability
Variability at 80 % CI
Automated vs Manual
7.02
8.40
8.76
124.74%
27.39%
Automated vs Semi-Automated
7.46
9.13
12.14
162.79%
37.37%
Manual vs Semi-Automated
7.01
8.33
10.02
143.02%
34.38%

12. Automated vs Semi-Automated Manual vs Semi-Automated
Results
Variability in PENUMBRAL volumes is LOWER than variability in CORE volumes with greater agreement in Automated technique.
Sample Mean
Standard Deviation
Repeatability
Variability
Variability at 80 % CI
Automated vs Manual
51.88
35.16
18.49
35.63%
17.76%
Automated vs Semi-Automated
52.28
34.40
5.11
9.77%
4.77%
Manual vs Semi-Automated
52.11
35.59
18.57
17.06%

13. Results
VERY HIGH VARIABILITY was observed in ischemic core quantification comparing various deconvolution methods
Sample Mean
Standard Deviation
Repeatability
Variability
Variability at 80% CI
oSVD vs sSVD
10.14
10.18
11.92
117.57%
83.25%
oSVD vs cSVD
6.22
7.98
10.47
168.26%
40.20%
oSVD vs Bayesian
15.63
13.37
24.78
158.58%
117.46%
sSVD vs cSVD
8.92
9.11
13.13
147.21%
91.28%
sSVD vs Bayesian
18.33
14.50
18.35
100.11%
61.04%
cSVD vs Bayesian
14.41
12.29
28.37
196.89%
125.46%

14. Results
Variability in PENUMBRAL volumes is LOWER than variability in CORE volumes comparing different deconvolution methods.
Sample Mean
Standard Deviation
Repeatability
Variability
Variability at 80% CI
oSVD vs sSVD
50.33
33.53
11.72
23.29%
15.25%
oSVD vs cSVD
53.18
35.77
12.42
23.36%
12.28%
oSVD vs Bayesian
12.43
sSVD vs cSVD
51.00
34.46
20.11
39.42%
15.45%
sSVD vs Bayesian
15.47%
cSVD vs Bayesian
53.86
36.71
0.01
0.01% 0%

15. Results
Bland – Altman plots were generated for comparison of each variable. It is to be noted that 20 % or less of the data contributes to a majority of the variability. For example, Bland-Altman plot for measurement of ischemic core using manual vs automated post-processing technique is shown below:
These two outliers significantly increase the calculated variability between two post processing techniques. Therefore, variability at 80% Confidence Interval (CI) was calculated to reduce the effect of these outliers.

16. Limitations Our data is limited by a small sample size.
Intra-observer variability was measured using a single post-processing software. Caution should be exercised before results are directly extended to other vendors / softwares.
Calculated variability in core values may appear worse than penumbral values due to relative small volumes, therefore exaggerating the effects of small errors.
Ideal threshold of Tmax used to differentiate penumbral versus benign oligemic volume is lacking. Changing the Tmax threshold could affect variability.

17. Conclusions
There is high variability in CTP parameters, particularly ischemic core measurements among various post processing techniques (manual, semi-automated and automated).
There is high variability in CTP parameters among various deconvolution algorithms used in perfusion post processing.
The study highlights the challenges for using CTP as a decision-making tool in acute stroke and emphasizes a critical need for standardization of CTP analysis before it can be integrated in routine clinical practice.