Fully automated stroke tissue estimation using random forest classifiers (FASTER).

@article{McKinley2017FullyAS,
  title={Fully automated stroke tissue estimation using random forest classifiers (FASTER).},
  author={Richard McKinley and Levin H{\"a}ni and Jan Gralla and M El-Koussy and Stefan Bauer and M Arnold and U Fischer and Simon Jung and Kaspar Mattmann and Mauricio Reyes and Roland Wiest},
  journal={Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism},
  year={2017},
  volume={37 8},
  pages={
          2728-2741
        }
}
Several clinical trials have recently proven the efficacy of mechanical thrombectomy for treating ischemic stroke, within a six-hour window for therapy. To move beyond treatment windows and toward personalized risk assessment, it is essential to accurately identify the extent of tissue-at-risk ("penumbra"). We introduce a fully automated method to estimate the penumbra volume using multimodal MRI (diffusion-weighted imaging, a T2w- and T1w contrast-enhanced sequence, and dynamic susceptibility… CONTINUE READING
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