Variability of Ischemic Core and Penumbra using CT Perfusion in Acute Ischemic Stroke M. Reddy, A....
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Transcript of Variability of Ischemic Core and Penumbra using CT Perfusion in Acute Ischemic Stroke M. Reddy, A....
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.