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

ABSTRACT: 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 contrast perfusion MRI). The method estimates tissue-at-risk by predicting tissue damage in the case of both persistent occlusion and of complete recanalization. When applied to 19 test cases with a thrombolysis in cerebral infarction grading of 1-2a, mean overestimation of final lesion volume was 30

SUBMITTER: McKinley R 

PROVIDER: S-EPMC5536784 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature