Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters

Ryan A. Rava; Alexander R. Podgorsak; Mohammad Waqas; Kenneth V. Snyder; Elad I. Levy; Jason M. Davies; Adnan H. Siddiqui; Ciprian N. Ionita

doi:10.1117/12.2579753

15 February 2021 Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters

Ryan A. Rava, Alexander R. Podgorsak, Mohammad Waqas, Kenneth V. Snyder, Elad I. Levy, Jason M. Davies, Adnan H. Siddiqui, Ciprian N. Ionita

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159611 (2021) https://doi.org/10.1117/12.2579753
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Purpose: Computed tomography perfusion (CTP) is used to diagnose ischemic strokes through contralateral hemisphere comparisons of various perfusion parameters. Various perfusion parameter thresholds have been utilized to segment infarct tissue due to differences in CTP software and patient baseline hemodynamics. This study utilized a convolutional neural network (CNN) to eliminate the need for non-universal parameter thresholds to segment infarct tissue. Methods: CTP data from 63 ischemic stroke patients was retrospectively collected and perfusion parameter maps were generated using Vitrea CTP software. Infarct ground truth labels were segmented from diffusion-weighted imaging (DWI) and CTP and DWI volumes were registered. A U-net based CNN was trained and tested five separate times using each CTP parameter (cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak (TTP), mean-transit-time (MTT), delay time). 8,352 infarct slices were utilized with a 60:30:10 training:testing:validation split and Monte Carlo crossvalidation was conducted using 20 iterations. Infarct volumes were reconstructed following segmentation from each CTP slice. Infarct spatial and volumetric agreement was compared between each CTP parameter and DWI. Results: Spatial agreement metrics (Dice coefficient, positive predictive value) for each CTP parameter in predicting infarct volumes are: CBF=(0.67, 0.76), CBV=(0.44, 0.62), TTP=(0.60, 0.67), MTT=(0.58, 0.62), delay time=(0.57, 0.60). 95% confidence intervals for volume differences with DWI infarct are: CBF=14.3±11.5 mL, CBV=29.6±21.2 mL, TTP=7.7±15.2 mL, MTT=-10.7±18.6 mL, delay time=-5.7±23.6 mL. Conclusions: CBF is the most accurate CTP parameter in segmenting infarct tissue. Segmentation of infarct using a CNN has the potential to eliminate non-universal CTP contralateral hemisphere comparison thresholds.

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Ryan A. Rava, Alexander R. Podgorsak, Mohammad Waqas, Kenneth V. Snyder, Elad I. Levy, Jason M. Davies, Adnan H. Siddiqui, and Ciprian N. Ionita "Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159611 (15 February 2021); https://doi.org/10.1117/12.2579753

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