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Automated quantification of cerebral edema following hemispheric infarction : Application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs / Chen, Yasheng (Department of Neurology, Washington University, St. Louis, MO 63110, USA) ; Dhar, Rajat (Department of Neurology, Washington University, St. Louis, MO 63110, USA) ; Heitsch, Laura (Emergency Medicine, Washington University, St. Louis, MO 63110, USA) ; Ford, Andria (Department of Neurology, Washington University, St. Louis, MO 63110, USA) ; Fernandez-Cadenas, Israel (Hospital Universitari Vall d'Hebron. Institut de Recerca) ; Carrera, Caty (Hospital Universitari Vall d'Hebron. Institut de Recerca) ; Montaner, Joan (Hospital Universitari Vall d'Hebron. Institut de Recerca) ; Lin, Weili (Dept. of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA) ; Shen, Dinggang (Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea) ; An, Hongyu (Radiology, Washington University, St. Louis, MO 63110, USA) ; Lee, Jin-Moo (Biomedical Engineering, Washington University, St. Louis, MO 63110, USA) ; Universitat Autònoma de Barcelona
Although cerebral edema is a major cause of death and deterioration following hemispheric stroke, there remains no validated biomarker that captures the full spectrum of this critical complication. We recently demonstrated that reduction in intracranial cerebrospinal fluid (CSF) volume (∆ CSF) on serial computed tomography (CT) scans provides an accurate measure of cerebral edema severity, which may aid in early triaging of stroke patients for craniectomy. [...]
2016 - DOI (Digital Object Identifier)

10.1016/j.nicl.2016.09.018
NeuroImage, Vol. 12 (september 2016) , p. 673-680

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