Regional Prediction of Tissue Fate in Acute Ischemic Stroke
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
Early and accurate prediction of tissue outcome is essential to the clinical decision-making process in acute ischemic stroke. We present a quantitative predictive model of tissue fate that combines regional imaging features available after onset. A key component is the use of cuboids randomly sampled during the learning process. Models trained with time-to-maximum feature (Tmax) computed from perfusion weighted images (PWI) are compared to the ones obtained from the apparent diffusion coefficient (ADC). The prediction task is formalized as a regression problem where the inputs are the local cuboids extracted from Tmax or ADC images at onset, and the output is the segmented FLAIR intensity of the tissue 4 days after intervention. Experiments on 25 acute stroke patients demonstrate the effectiveness of the proposed approach in predicting tissue fate. Results on our dataset show the superiority of the regional model vs. a single-voxel-based approach, indicate that PWI regional models outperform ADC models, and demonstrates that a nonlinear regression model significantly improves the results in comparison to a linear model.
- Brown, M., G. Hua, and S. Winder. Discriminative learning of local image descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 33(1):43–57, 2011. CrossRef
- Brown, M., R. Szeliski, and S. Winder. Multi-image matching using multi-scale oriented patches. CVPR 1:510–517, 2005.
- Cai, D., X. He, and J. Han. Spectral regression for efficient regularized subspace learning. In: ICCV, 2007.
- Calamante, F., S. Christensen, P. M. Desmond, L. Ostergaard, S. M. Davis, and A. Connelly. The physiological significance of the time-to-maximum (Tmax) parameter in perfusion MRI. Stroke 41:1169–1174, 2010. CrossRef
- Chatterjee, S. and A. S. Hadi. Influential observations, high leverage points and outliers in linear regression. Stat. Sci. 1:379–393, 1986. CrossRef
- DeLong, E. R., D. M. DeLong, and D. L. Clarke-Pearson. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845, 1988. CrossRef
- Duchon, C. E. Lanczos filtering in one and two dimensions. J. Appl. Meteorol. 18(8):1016–1022, 1979. CrossRef
- Huang, S., Q. Shen, and T. Q. Duong. Artificial neural network prediction of ischemic tissue fate in acute stroke imaging. J. Cereb. Blood Flow Metab. 39:1661–1670, 2010.
- Jonsdottir, K., L. Ostergaard, and K. Mouridsen. Predicting tissue outcome from acute stroke magnetic resonance imaging: improving model performance by optimal sampling of training data. Stroke 40:3006–3011, 2009. CrossRef
- Mikolajczyk, K. and C. Schmid. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27:1615–1630, 2005. CrossRef
- Nguyen, V., H. Pien, N. Menenzes, C. Lopez, C. Melinosky, O. Wu, A. Sorensen, G. Cooperman, H. Ay, W. Koroshetz, Y. Liu, J. Nuutinen, H. Aronen, and J. Karonen. Stroke tissue outcome prediction using a spatially-correlated model. In: PPIC, 2008.
- Olivot, J., M. Mlynash, V. Thijs, A. Purushotham, S. Kemp, M. Lansberg, L. Wechsler, G. Gold, R. Bammer, M. Marks, and G. Albers. Geography, structure, and evolution of diffusion and perfusion lesions in Diffusion and perfusion imaging Evaluation For Understanding Stroke Evolution (DEFUSE). Stroke 40(10):3245–3251, 2009. CrossRef
- Olivot, J. M., M. Mlynash, G. Zaharchuk, M. Straka, R. Bammer, N. Schwartz, M. G. Lansberg, M. E. Moseley, and G. W. Albers. Perfusion MRI (Tmax and MTT) correlation with xenon CT cerebral blood flow in stroke patients. Neurology 72:1140–1145, 2009. CrossRef
- Rose, S., J. Chalk, M. Griffin, A. Janke, F. Chen, G. McLachan, D. Peel, F. Zelaya, H. Markus, D. Jones, A. Simmons, M. O’Sullivan, J. Jarosz, W. Strugnell, D. Doddrell, and J. Semple. MRI based diffusion and perfusion predictive model to estimate stroke evolution. JMRI 19(8):1043–1053, 2001.
- Scalzo, F., Q. Hao, J. Alger, X. Hu, and D. Liebeskind. Tissue fate prediction in acute ischemic stroke using cuboid models. ISVC 6454:292–301, 2010.
- Scalzo, F., P. Xu, S. Asgari, M. Bergsneider, and X. Hu. Regression analysis for peak designation in pulsatile pressure signals. Med. Biol. Eng. Comput. 47:967–977, 2009. CrossRef
- Shen, Q., and T. Duong. Quantitative prediction of ischemic stroke tissue fate. NMR Biomed. 21:839–848, 2008. CrossRef
- Shen, Q., H. Ren, M. Fisher, and T. Duong. Statistical prediction of tissue fate in acute ischemic brain injury. J. Cereb. Blood Flow Metab. 25:1336–1345, 2005. CrossRef
- Siegel, S., and N. Castellan. Nonparametric Statistics for the Behavioral Sciences, 2nd ed. McGraw–Hill, Inc., Boston, 1988.
- Smith, S. Fast robust automated brain extraction. Hum. Brain Mapp. 17(3):143–155, 2002. CrossRef
- Smith, W. S., G. Sung, J. Saver, R. Budzik, G. Duckwiler, D. S. Liebeskind, et al. Mechanical thrombectomy for acute ischemic stroke: final results of the Multi MERCI trial. Stroke 39:1205–1212, 2008. CrossRef
- Wu, O., W. Koroshetz, L. Ostergaard, F. Buonanno, W. Copen, R. Gonzalez, G. Rordorf, B. Rosen, L. Schwamm, R. Weisskoff, and A. Sorensen. Predicting tissue outcome in acute human cerebral ischemia using combined diffusion- and perfusion-weighted MR imaging. Stroke 32(4):933–942, 2001. CrossRef
- Wu, O., T. Sumii, M. Asahi, M. Sasamata, L. Ostergaard, B. Rosen, E. Lo, and R. Dijkhuizen. Infarct prediction and treatment assessment with MRI-based algorithms in experimental stroke models. J. Cereb. Blood Flow Metab. 27:196–204, 2007. CrossRef
- Yoo, A. J., E. R. Barak, W. A. Copen, S. Kamalian, L. R. Gharai, M. A. Pervez, L. H. Schwamm, R. G. Gonzalez, and P. W. Schaefer. Combining acute diffusion-weighted imaging and mean transmit time lesion volumes with NIHSSS improves the prediction of acute stroke outcome. Stroke 41:1728–1735, 2010. CrossRef
- Regional Prediction of Tissue Fate in Acute Ischemic Stroke
Annals of Biomedical Engineering
Volume 40, Issue 10 , pp 2177-2187
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Brain ischemia
- Acute stroke diagnostic
- Perfusion weighted images
- Lesion growth
- Industry Sectors