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-65Control No: 1041

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

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

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

MethodsCTP 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

CBF MTT

MethodsSingle 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): 1197-1203.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): 1066-1078.

MethodsVolumes 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

Methods

5 patients excluded due to : motion artifactsignificantly truncated time

curves

Truncated Time Curve

+=

Motion Artifact

MethodsQuantified variability with Bland-Altman analysis1 using repeatability coefficient and coefficient of variation

Identify relative difference between 2 observations by:

Plotting difference between the numerical value vs the numerical

mean of the 2 values

Bland – Altman Analysis

1. Bland, J. Martin, and DouglasG Altman. "Statistical methods for assessing agreement between two methods of clinical measurement." The lancet327.8476 (1986): 307-310.2. 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): 927-932.

2

MethodsRepeatability Coefficient = When comparing 2 methods, repeatability coefficient is a threshold within which 95 % of the absolute difference values lie.

1. Bland, J. Martin, and DouglasG Altman. "Statistical methods for assessing agreement between two methods of clinical measurement." The lancet327.8476 (1986): 307-310.2. 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): 445-451.

Coefficient of Variation = When comparing 2 methods, coefficient of variation is the ratio of repeatability coefficient over mean of the two values.

Lower Repeatability Coefficient

Better Agreement

Better Agreement

Lower Coefficient of Variation

ResultsVERY 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%

Results

 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 35.63% 17.06%

Variability in PENUMBRAL volumes is LOWER than variability in CORE volumes with greater agreement in Automated technique.

ResultsVERY HIGH VARIABILITY was observed in ischemic core quantification comparing various deconvolution methods

  Sample MeanStandard Deviation

Repeatability VariabilityVariability at

80% CIoSVD vs

sSVD10.14 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%

ResultsVariability in PENUMBRAL volumes is LOWER than variability in CORE volumes comparing different deconvolution methods.

  Sample MeanStandard Deviation

Repeatability VariabilityVariability at

80% CIoSVD vs

sSVD50.33 33.53 11.72 23.29% 15.25%

oSVD vs cSVD

53.18 35.77 12.42 23.36% 12.28%

oSVD vs Bayesian

53.18 35.77 12.43 23.36% 12.28%

sSVD vs cSVD

51.00 34.46 20.11 39.42% 15.45%

sSVD vs Bayesian

51.00 34.46 20.11 39.42% 15.47%

cSVD vs Bayesian

53.86 36.71 0.01 0.01% 0%

ResultsBland – 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.

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.

ConclusionsThere 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.