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A quantitative symmetry‐based analysis of hyperacute ischemic stroke lesions in noncontrast computed tomography
Author(s) -
Peter Roman,
Korfiatis Panagiotis,
Blezek Daniel,
Oscar Beitia A.,
StepanBuksakowska Irena,
Horinek Daniel,
Flemming Kelly D.,
Erickson Bradley J.
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12015
Subject(s) - medicine , stroke (engine) , ischemic stroke , magnetic resonance imaging , radiology , receiver operating characteristic , texture (cosmology) , ischemia , artificial intelligence , cardiology , computer science , image (mathematics) , mechanical engineering , engineering
Purpose Early identification of ischemic stroke plays a significant role in treatment and potential recovery of damaged brain tissue. In noncontrast CT (ncCT), the differences between ischemic changes and healthy tissue are usually very subtle during the hyperacute phase (< 8 h from the stroke onset). Therefore, visual comparison of both hemispheres is an important step in clinical assessment. A quantitative symmetry‐based analysis of texture features of ischemic lesions in noncontrast CT images may provide an important information for differentiation of ischemic and healthy brain tissue in this phase. Methods One hundred thirty‐nine ( 139) ncCT scans of hyperacute ischemic stroke with follow‐up magnetic resonance diffusion‐weighted (MR‐DW) images were collected. The regions of stroke were identified in the MR‐DW images, which were spatially aligned to corresponding ncCT images. A state‐of‐the‐art symmetric diffeomorphic image registration was utilized for the alignment of CT and MR‐DW, for identification of individual brain hemispheres, and for localization of the region representing healthy tissue contralateral to the stroke cores. Texture analysis included extraction and classification of co‐occurrence and run‐length texture‐based image features in the regions of ischemic stroke and their contralateral regions. Results The classification schemes achieved area under the receiver operating characteristic [Az] ≈ 0.82 for the whole dataset. There was no statistically significant difference in the performance of classifiers for the data sets with time between 2 and 8 hours from symptom onset. The performance of the classifiers did not depend on the size of the stroke regions. Conclusions The results provide a set of optimal texture features which are suitable for distinguishing between hyperacute ischemic lesions and their corresponding contralateral brain tissue in noncontrast CT. This work is an initial step toward development of an automated decision support system for detection of hyperacute ischemic stroke lesions on noncontrast CT of the brain.