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Computational Analysis: A Bridge to Translational Stroke Treatment

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Translational Stroke Research

Part of the book series: Springer Series in Translational Stroke Research ((SSTSR))

Abstract

Objective rapid quantification of injury using computational methods can improve the assessment of the degree of stroke injury, aid in the selection of patients for early or specific treatments, and monitor the evolution of injury and recovery. In this chapter, we use neonatal ischemia as a case-study of the application of several computational methods that in fact are generic and applicable across the age and disease spectrum. We provide a summary of current computational approaches used for injury detection, including Gaussian mixture models (GMM), Markov random fields (MRFs), normalized graph cut, and K-means clustering. We also describe more recent automated approaches to segment the region(s) of ischemic injury including hierarchical region splitting, support vector machine, a brain symmetry/asymmetry integrated model, and a watershed method that are robust at different developmental stages. We conclude with our assessment of probable future research directions in the field of computational noninvasive stroke analysis such as automated detection of the ischemic core and penumbra, monitoring of implanted neuronal stem cells in the ischemic brain, injury localization specific to different brain anatomical regions, and quantification of stroke evolution, recovery and spatiotemporal interactions between injury volume/severity and treatment. Computational analysis is expected to open a new horizon in current clinical and translational stroke research by exploratory data mining that is not detectable using the standard “methods” of visual assessment of imaging data.

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Acknowledgments

part of this work has been funded by NIH NINDS 1R01NS059770-01A2, National Medical Test Bed (NMTB), LLU Pediatric Research Fund, and an anonymous donation to the Loma Linda University School of Medicine. Part of the research by Drs. Bhanu and Sun has been funded by NSF grants 0641076, 0727129, and 0903667. We are grateful to Dr. Samuel Barnes (LLU) for SVM results, Beatriz Tone and Dr. Hui Rou Tian (LLU) for surgical procedures, Kamal Ambadipudi and Sonny Kim (LLU) for technical assistance with MRI acquisition, Dr. Jerome Badaut (LLU) for use of histochemical equipment, Dr. Evan Y. Snyder (Sanford-Burnham Medical Research Institute, La Jolla, CA, USA) for iron-labeled stem cells, Dr. Ivo Dinov and Dr. Alen Zamanyan (Laboratory of Neuroimaging, UCLA, Los Angeles, USA) for assistance on using LONI Pipeline and brain parsing.

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Authors and Affiliations

  1. Department of Pediatrics, Loma Linda University, Loma Linda, CA, USA

    Nirmalya Ghosh, Christine Turenius PhD, Andre Obenaus PhD & Stephen Ashwal MD

  2. Center for Research in Intelligent Systems, University of California, Riverside, CA, USA

    Yu Sun PhD & Bir Bhanu PhD

  3. Department of Radiology, Loma Linda University, Loma Linda, CA, USA

    Andre Obenaus PhD

  4. Department of Radiation Medicine, Loma Linda University, Loma Linda, CA, USA

    Andre Obenaus PhD

  5. Department of Biophysics and Bioengineering, Loma Linda University, Loma Linda, CA, USA

    Andre Obenaus PhD

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  1. Nirmalya Ghosh
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  2. Yu Sun PhD
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  4. Bir Bhanu PhD
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  6. Stephen Ashwal MD
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Correspondence to Nirmalya Ghosh .

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Editors and Affiliations

  1. , Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, 90048, California, USA

    Paul A. Lapchak

  2. Loma Linda University, 11234 Anderson Street, Room 2562B, Loma Linda, 92354, California, USA

    John H. Zhang

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Ghosh, N., Sun, Y., Turenius, C., Bhanu, B., Obenaus, A., Ashwal, S. (2012). Computational Analysis: A Bridge to Translational Stroke Treatment. In: Lapchak, P., Zhang, J. (eds) Translational Stroke Research. Springer Series in Translational Stroke Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9530-8_42

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