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An Analysis Tool to Calculate Permeability Based on the Patlak Method

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Abstract

Strokes are commonly diagnosed by utilizing images obtained from magnetic resonance imaging (MRI) technology. Nowadays, computer software can play a large role in analyzing these images and arriving at diagnoses quickly and accurately. Additionally, this software can reduce workload for medical personnel and lower misdiagnoses. In this paper a flexible permeability calculation tool called PCT based on the Patlak plot method is presented. Using the PCT we can calculate the permeability co-efficient of the Blood-Brain Barrier (BBB) function. The PCT tool offers both manual and automatic options for diagnosing the regions of the brain affected by stroke. Moreover, the PCT tool supports various extensions such as dicom, nifty and analyze.

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References

  1. Stroke Center, 2010 http://www.strokecenter.org/patients/about.htm

  2. Zhou, Y., Ye, W., Brasic, J. R., and Wong, D. F., Multi-graphical analysis of dynamic PET. Neuroimage 49:2947–2957, 2010.

    Article  Google Scholar 

  3. Gjedde, A., High- and low-affinity transport of D-Glucose from blood to brain. J. Neurochem. 36:1463–1471, 1981.

    Article  Google Scholar 

  4. Patlak, C. S., and Blasberg, R. G., Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. J. Cereb. Blood Flow Metab. 5:584–590, 1985.

    Article  Google Scholar 

  5. Patlak, C. S., Blasberg, R. G., and Fenstermacher, J. D., Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J. Cereb. Blood Flow Metab. 3(1):1–7, 1983.

    Article  Google Scholar 

  6. Wong, D. F., Gjedde, A., and Wagner, H. N., Jr., Quantification of neuroreceptors in the living human brain. I. Irreversible binding of ligands. J. Cereb. Blood Flow Metab. 6:137–146, 1986.

    Article  Google Scholar 

  7. Finn Arup Nielsen, Technical University of Denmark, http://www2.imm.dtu.dk/~fn/ps/Nielsen2004Tools_slide.pdf, 2004.

  8. Gold, S., Christian, B., Arndt, S., Zeien, G., Cizadlo, T., Johnson, D. L., Flaum, M., and Andreasen, N. C., Functional MRI statistical software packages: a comparative analysis. Hum. Brain Mapp. 6:73–84, 1998.

    Article  Google Scholar 

  9. Fillard, P., Gerig, G., Analysis tool for diffusion tensor MRI, Lecture Notes in Computer Science, 967–968, Vol. 2879/2003.

  10. Taheri, S., and Sood, R., Kalman filtering for reliable estimation of BBB permeability. Magn. Reson. Imaging 24:1039–1049, 2006.

    Article  Google Scholar 

  11. Abo-Ramadan, U., Durukan, A., Pitkonen, M., Marinkovic, I., Tatlisumak, E., Pedrono, E., Soinne, L., Strbian, D., and Tatlisumak, T., Post-ischemic leakiness of the blood–brain barrier: a quantitative and systematic assessment by Patlak plots. Exp. Neurol. 219:328–333, 2009.

    Article  Google Scholar 

  12. Odaka, T., Takahama, T., Wagatsuma, H., Shimada, K., and Ogura, H., A visual data analysis system for the medical image processing. J. Med. Syst. 18(3):151–157, 1994.

    Article  Google Scholar 

  13. Yoder, J. W., Schultz, D. E., and Williams, B. T., The MEDIGATE graphical user interface for entry of physical findings: design principles and implementation. J. Med. Syst. 22(5):325–337, 1998.

    Article  Google Scholar 

  14. Kopec, D., Kabir, M. H., Reinharth, D., Rothschild, O., and Castiglione, J. A., Human errors in medical practice: systematic classification and reduction with automated information systems. J. Med. Syst. 27(4):297–313, 2003.

    Article  Google Scholar 

  15. Liu, W., Sood, R., Chen, Q., Sakoglu, U., Hendren, J., Çetin, Ö., and Liu, K. J., Normobaric hyperoxia inhibits NADPH oxidase-mediated matrix metalloproteinase-9 induction in cerebral microvessels in experimental stroke. J Neurochem 107(5):1196–1205, 2008.

    Article  Google Scholar 

  16. Taheri, S., and Sood, R., Partial volume effect compensation for improved reliability of quantitative blood–brain barrier permeability. Magn. Reson. Imaging 25:613–625, 2007.

    Article  Google Scholar 

  17. Patlak, C. S., Blasberg, R. G., and Fenstermacher, J. D., Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J. Cereb. Blood Flow Metab. 3(1):1–7, 1983.

    Article  Google Scholar 

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Acknowledgments

The author would like to thank Dr. Rohid Sood and Dr. Unal Sakoglu for their contributions and cooperation.

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Correspondence to Özdemir Çetin.

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Çetin, Ö. An Analysis Tool to Calculate Permeability Based on the Patlak Method. J Med Syst 36, 1317–1326 (2012). https://doi.org/10.1007/s10916-010-9592-y

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  • DOI: https://doi.org/10.1007/s10916-010-9592-y

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