Ischemic stroke enhancement using a variational model and the expectation maximization method
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2018-09-01
Autores
Alves, Allan Felipe Fattori [UNESP]
Jennane, Rachid
de Miranda, José Ricardo Arruda [UNESP]
de Freitas, Carlos Clayton Macedo [UNESP]
Abdala, Nitamar
de Pina, Diana Rodrigues [UNESP]
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Resumo
Objectives: In order to enable less experienced physicians to reliably detect early signs of stroke, A novel approach was proposed to enhance the visual perception of ischemic stroke in non-enhanced CT. Methods: A set of 39 retrospective CT scans were used, divided into 23 cases of acute ischemic stroke and 16 normal patients. Stroke cases were obtained within 4.5 h of symptom onset and with a mean NIHSS of 12.9±7.4. After selection of adjunct slices from the CT exam, image averaging was performed to reduce the noise and redundant information. This was followed by a variational decomposition model to keep the relevant component of the image. The expectation maximization method was applied to generate enhanced images. Results: We determined a test to evaluate the performance of observers in a clinical environment with and without the aid of enhanced images. The overall sensitivity of the observer’s analysis was 64.5 % and increased to 89.6 % and specificity was 83.3 % and increased to 91.7 %. Conclusion: These results show the importance of a computational tool to assist neuroradiology decisions, especially in critical situations such as the diagnosis of ischemic stroke. Key Points: • Diagnosing patients with stroke requires high efficiency to avoid irreversible cerebral damage. • A computational algorithm was proposed to enhance the visual perception of stroke. • Observers’ performance was increased with the aid of enhanced images.
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Algorithms, Brain, Early diagnosis, Stroke, Tomography
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European Radiology, v. 28, n. 9, p. 3936-3942, 2018.