A quantitative symmetry-based analysis of hyperacute ischemic stroke lesions in noncontrast computed tomography
Language English Country United States Media print-electronic
Document type Journal Article
Grant support
U01 CA160045
NCI NIH HHS - United States
PubMed
28066898
PubMed Central
PMC5339891
DOI
10.1002/mp.12015
Knihovny.cz E-resources
- Keywords
- hyperacute ischemic stroke, image registration, noncontrast computed tomography (ncCT) classification, texture analysis,
- MeSH
- Stroke complications diagnostic imaging MeSH
- Brain Ischemia complications MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Tomography, X-Ray Computed * MeSH
- Image Processing, Computer-Assisted methods MeSH
- Decision Trees MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Sensitivity and Specificity MeSH
- Support Vector Machine MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
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.
Department of Neurology Mayo Clinic 200 1st Street SW Rochester MN 55905 USA
Department of Radiology Mayo Clinic 200 1st Street SW Rochester MN 55905 USA
See more in PubMed
Zoppo GJd, Saver JL, Jauch EC, Adams HP. Expansion of the time window for treatment of acute ischemic stroke with intravenous tissue plasminogen activator a science advisory from the American Heart Association/American Stroke Association. Stroke. 2009;40:2945–2948. PubMed PMC
Hacke W, Kaste M, Bluhmki E, et al. Thrombolysis with Alteplase 3 to 4.5 hours after acute ischemic stroke. N Engl J Med. 2008;359:1317–1329. PubMed
Schwamm LH, Koroshetz WJ, Sorensen AG, et al. Time course of lesion development in patients with acute stroke serial diffusion‐ and hemodynamic‐weighted magnetic resonance imaging. Stroke. 1998;29:2268–2276. PubMed
Wardlaw JM, von Kummer R, Farrall AJ, Chappell FM, Hill M, Perry D. A large web‐based observer reliability study of early ischaemic signs on computed tomography. The Acute Cerebral CT Evaluation of Stroke Study (ACCESS). PLoS One. 2010;5:e15757. PubMed PMC
Srinivasan A, Goyal M, Azri FA, Lum C. State‐of‐the‐art imaging of acute stroke. Radiographics. 2006;26:S75–S95. PubMed
Khalaf HS, Ahmed SY, Kurman AJ. Caudate body (CB) sign: new early CT sign of hyperacute anterior cerebral circulation infarction. Emerg Radiol. 2011;18:533–538. PubMed
Nowinski WL, Gupta V, Qian G, et al. Automatic detection, localization, and volume estimation of ischemic infarcts in noncontrast computed tomographic scans: method and preliminary results. Invest Radiol. 2013;48:661–670. PubMed
Takahashi N, Lee Y, Tsai D‐Y, et al. Improvement of detection of hypoattenuation in acute ischemic stroke in unenhanced computed tomography using an adaptive smoothing filter. Acta Radiologica. 2008;49:816–826. PubMed
Przelaskowski A, Sklinda K, Bargieł P, Walecki J, Biesiadko‐Matuszewska M, Kazubek M. Improved early stroke detection: wavelet‐based perception enhancement of computerized tomography exams. Comput Biol Med. 2007;37:524–533. PubMed
Takahashi N, Tsai D‐Y, Lee Y, Kinoshita T, Ishii K. Z‐score mapping method for extracting hypoattenuation areas of hyperacute stroke in unenhanced CT. Acad Radiol. 2010;17:84–92. PubMed
Gillebert CR, Humphreys GW, Mantini D. Automated delineation of stroke lesions using brain CT images. NeuroImage Clin. 2014;4:540–548. PubMed PMC
Takahashi N, Tsai D‐Y, Lee Y, et al. Usefulness of z‐score mapping for quantification of extent of hypoattenuation regions of hyperacute stroke in unenhanced computed tomography: analysis of radiologists’ performance. J Comput Assist Tomogr. 2010;34:751–756. PubMed
Takahashi N, Lee Y, Tsai D‐Y, Kinoshita T, Ouchi N, Ishii K. Computer‐aided detection scheme for identification of hypoattenuation of acute stroke in unenhanced CT. Radiol Phys Technol. 2012;5:98–104. PubMed
Tang F‐H, Ng DKS, Chow DHK. An image feature approach for computer‐aided detection of ischemic stroke. Comput Biol Med. 2011;41:529–536. PubMed
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage. 2011;54:2033–2044. PubMed PMC
Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Trans Sys Man Cybern. 1973;SMC‐3:610–621.
Galloway MM. Texture analysis using gray level run lengths. Comput Graphics and Image Process. 1975;4:172–179.
Robert JG, Paul EK, Hedvig H. Radiomics: images are more than pictures, they are data. Radiol. 2016;278:563–577. PubMed PMC
Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev. 2016;1:207–226. PubMed PMC
Cortes C, Vapnik V. Support‐vector networks. Mach Learn. 1995;20:273–297.
Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Ann Stat. 2000;28:337–407.
Rokach L, Maimon O. Top‐down induction of decision trees classifiers – a survey. IEEE Trans Sys, Man Cybern, Part C (Applications and Reviews). 2005;35:476–487.
Straka M, Albers GW, Bammer R. Real‐time diffusion‐perfusion mismatch analysis in acute stroke. J Magn Reson Imaging: JMRI. 2010;32:1024–1037. PubMed PMC
Rorden C, Bonilha L, Fridriksson J, Bender B, Karnath H‐O. Age‐specific CT and MRI templates for spatial normalization. NeuroImage. 2012;61:957–965. PubMed PMC
Xiaoou T. Texture information in run‐length matrices. Trans Img Proc. 1998;7:1602–1609. PubMed
Loh HH, Leu JG, Luo RC. The analysis of natural textures using run length features. IEEE Trans Industr Electron. 1988;35:323–328.
Vapnik VN. The Nature of Statistical Learning Theory. New York: Springer‐Verlag, Inc.; 1995.
Chen S, Zhou S, Yin F‐F, Marks LB, Das SK. Investigation of the support vector machine algorithm to predict lung radiation‐induced pneumonitis. Med Phys. 2007;34:3808–3814. PubMed PMC
Keerthi SS. Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Trans Neural Networks. 2002;13:1225–1229. PubMed
Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 1997;30:1145–1159.
DeLong ER, DeLong DM, Clarke‐Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biom. 1988;44:837–845. PubMed
Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans Evol Comput. 1997;1:67–82.
Varma S, Simon R. Bias in error estimation when using cross‐validation for model selection. BMC Bioinformatics. 2006;7:91. PubMed PMC
Axer H, Gräβel D, Brämer D, et al. Time course of diffusion imaging in acute brainstem infarcts. J Magn Reson Imaging. 2007;26:905–912. PubMed