Johnny Suh M.D., Dr. Jacobson M.D., Dr. Pond M.D. Automated Computer Analysis of Core Infarct Size and Perfusion Mismatch Johnny Suh M.D., Dr. Jacobson M.D., Dr. Pond M.D. Loma Linda University Diagnostic Radiology

Background Core infarct size and penumbra are being used in patient selection for intervention. Current method of calculating lesion volume assumes an ellipsoid shape: Lesion size = (A * B *C) / 2 While this method is accurate, it has many limitations including inter- observer variability, complex lesion shapes, and multi-focal lesions. Many recent studies have shown that using the Bayesian estimation algorithm is superior in generating more accurate perfusion images when compared to singular value decomposition. Core infarct size and penumbra are being used in patient selection for intervention. Currently, we assume an ellipsoid shape when calculating the core infarct size. There is frequently inter-observer variability, complex lesions shapes, and the complication of what to do when there are multifocal lesions.

Study Question Can we use OleaSphere software to accurately generate core infarct volume and the volume of hypoperfusion (using the Bayesian method)? so although we would eventually like to get to the point where we can automatically calculate these volumes. we first needed to know (question)

Methods Database including patients who underwent MRI prior to stroke intervention from April 2012 to May 2015, compiled by Dr. Pond Out of 91 patients from the database 18 were selected based on having: Adequate Diffusion Weighted Imaging (DWI) and Perfusion Weighted Imaging (PWI) High Quality Images So Dr. Pond was kind enough to give me his database which included patients who underwent MRI prior to stroke intervention from april 2012 to may 2015. There were 91 patients on the database, and only 18 had adequate imaging and were included in this study.

Methods OleaSphere Software was used for post-processing of both the DWI and PWI. The ADC map was used to calculate the core infarct size using the parameters of 100 x 10 ^-6 to 600 x 10 ^-6 mm^2/s. Bayesian estimation algorithm was used to calculate “Delay” maps with the hypoperfusion being defined as >0 second delay. The computer generated lesion volumes were then compared with verified attending lesion volumes. OleaSphere post-processing software was used to calculate DWI and PWI lesion volumes. The ADC map parameters were subjectively chosen as values from 100-600 mm^2/s, based on an initial run of 7 patients. Bayesian estimation algorithm post processing was used to create delay map images, with hypo perfusion being defined as anything > 0 seconds

Results

ADC So these are representative images on what we were seeing on Oleasphere. On top you have the ADC maps and on bottom you have an overlay of the lesion based on the parameters.

PWI And here we have MTT on top with the second row having an overlay of what it calculated to be the hypoperfused area based on the calculated delay from the bayesian algorithim.

Results - DWI lesion volumes Now graphing out all of the DWI information for all 18 patients we have the attending volume in blue, and computer generated volumes in red. Mean DWI difference 14.2 cc Mean DWI difference (Computer - Attending) 14.2 cc (95% CI 6.0 - 22.3)

Results - PWI lesion volumes This is comparing the PWI lesion volumes. Again we have the attending generated volumes in blue and the computer generated volumes in red. Mean difference is 139.2 Mean PWI difference (Computer - Attending) 139.2 cc (95% CI 73.7 - 204)

Results - DWI Volume Comparison We wanted to see how closely these numbers correlated with each other. Correlation .71 We have a good, not great correlation.

Results - PWI Volume Comparison A very weak correlation between the computer versus attending generated PWI.

Intraclass Correlation Coefficient Results Intraclass Correlation Coefficient 95% Confidence Interval Lower Bound Upper Bound DWI Comparison 0.753 0.223 0.916 PWI Comparison 0.328 -0.102 0.678 While the diffusion weighted images seemed to have agreement within their grouping, the confidence interval seemed to be wide. The perfusion weighted imaging did not seem to show agreement.

Discussion Computer generated DWI appears to be more reliable with higher volume lesions. It consistently overestimates the volume of a small infarct. Including more patients or changing the parameters may help. Computer generated PWI appears to overestimate the apparent perfusion deficit by a random margin. Neither PWI or DWI are reliable enough to use in a clinical setting.

Discussion - Cont. Further work will be done to adjust the parameters and decrease background noise for both DWI and PWI automated calculations. Regarding Bayesian Delay maps: The most sensitive parameter was used by defining delay as > 0 s.

Contributors Dr. Jacobson Dr. Pond Udo Oyoyo Dr. Kim