Impact of perfusion ROI detection to the quality of CBV perfusion map

Authors

DOI:

https://doi.org/10.15587/2312-8372.2019.182789

Keywords:

dynamic susceptibility contrast magnetic resonance imaging, cerebral blood volume, region of interest, thresholding

Abstract

The object of research in this study is quality of CBV perfusion map, considering detection of perfusion ROI as a key component in processing of dynamic susceptibility contrast magnetic resonance images of a human head. CBV map is generally accepted to be the best among others to evaluate location and size of stroke lesions and angiogenesis of brain tumors. Its poor accuracy can cause failed results for both quantitative measurements and visual assessment of cerebral blood volume.

The impact of perfusion ROI detection on the quality of maps was analyzed through comparison of maps produced from threshold and reference images of the same datasets from 12 patients with cerebrovascular disease. Brain perfusion ROI was placed to exclude low intensity (air and non-brain tissues regions) and high intensity (cerebrospinal fluid regions) pixels. Maps were produced using area under the curve and deconvolution methods.

For both methods compared maps were primarily correlational according to Pearson correlation analysis: r=0.8752 and r=0.8706 for area under the curve and deconvolution, respectively, p<2.2·10-16. In spite of this, for both methods scatter plots had data points associated with missed blood regions and regression lines indicated presence of scale and offset errors for maps produced from threshold images.

Obtained results indicate that thresholding is an ineffective way to detect brain perfusion ROI, which usage can cause degradation of CBV map quality. Perfusion ROI detection should be standardized and accepted into validation protocols of new systems for perfusion data analysis.

Author Biography

Svitlana Alkhimova, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute», 37, Peremohy ave., Kyiv, Ukraine, 03056

PhD

Department of Biomedical Cybernetics

References

  1. Lanzman, B., Heit, J. J. (2017). Advanced MRI Measures of Cerebral Perfusion and Their Clinical Applications. Topics in Magnetic Resonance Imaging, 26 (2), 83–90. doi: http://doi.org/10.1097/rmr.0000000000000120
  2. Welker, K., Boxerman, J., Kalnin, A., Kaufmann, T., Shiroishi, M., Wintermark, M. (2015). ASFNR Recommendations for Clinical Performance of MR Dynamic Susceptibility Contrast Perfusion Imaging of the Brain. American Journal of Neuroradiology, 36 (6), E41–E51. doi: http://doi.org/10.3174/ajnr.a4341
  3. Jahng, G.-H., Li, K.-L., Ostergaard, L., Calamante, F. (2014). Perfusion Magnetic Resonance Imaging: A Comprehensive Update on Principles and Techniques. Korean Journal of Radiology, 15 (5), 554–577. doi: http://doi.org/10.3348/kjr.2014.15.5.554
  4. Kudo, K., Christensen, S., Sasaki, M., Østergaard, L., Shirato, H., Ogasawara, K. et. al. (2013). Accuracy and Reliability Assessment of CT and MR Perfusion Analysis Software Using a Digital Phantom. Radiology, 267 (1), 201–211. doi: http://doi.org/10.1148/radiol.12112618
  5. Kudo, K., Uwano, I., Hirai, T., Murakami, R., Nakamura, H., Fujima, N. et. al. (2017). Comparison of Different Post-Processing Algorithms for Dynamic Susceptibility Contrast Perfusion Imaging of Cerebral Gliomas. Magnetic Resonance in Medical Sciences, 16 (2), 129–136. doi: http://doi.org/10.2463/mrms.mp.2016-0036
  6. Alkhimova, S. M., Slusar, S. V. (2019). Bottlenecks in validation of algorithms for perfusion image processing. SCIENCE, RESEARCH, DEVELOPMENT. Technics and Technology. Warsaw: Diamond trading tour, 25–27.
  7. Galinovic, I., Ostwaldt, A.-C., Soemmer, C., Bros, H., Hotter, B., Brunecker, P., Fiebach, J. B. (2012). Automated vs manual delineations of regions of interest- a comparison in commercially available perfusion MRI software. BMC Medical Imaging, 12 (1). doi: http://doi.org/10.1186/1471-2342-12-16
  8. Alkhimova, S. M. (2018). Detection of perfusion ROI as a quality control in perfusion analysis. SCIENCE, RESEARCH, DEVELOPMENT. Technics and Technology. Warsaw: Diamond trading tour, 57–59.
  9. Despotović, I., Goossens, B., Philips, W. (2015). MRI Segmentation of the Human Brain: Challenges, Methods, and Applications. Computational and Mathematical Methods in Medicine, 2015, 1–23. doi: http://doi.org/10.1155/2015/450341
  10. Kalavathi, P., Prasath, V. B. S. (2015). Methods on Skull Stripping of MRI Head Scan Images – a Review. Journal of Digital Imaging, 29 (3), 365–379. doi: http://doi.org/10.1007/s10278-015-9847-8
  11. Tripathi, S., Anand, R. S., Fernandez, E. (2018). A review of brain MR image segmentation techniques. International Journal of Research and Analytical Reviews, 5 (2), 1295–1299.
  12. Alkhimova, S. M. (2018). Automated Detection of Regions of Interest for Brain Perfusion MR Images. Research Bulletin of the National Technical University of Ukraine “Kyiv Politechnic Institute”, 5, 14–21. doi: http://doi.org/10.20535/1810-0546.2018.5.146185
  13. Cancer Imaging Archive (TCIA). Available at: http://cancerimagingarchive.net/
  14. Alkhimova, S. M. (2015). Calculation accuracy evaluation of quantitative parameters of overall perfusion assessment. Eastern-European Journal of Enterprise Technologies, 6 (9 (78)), 4–9. doi: http://doi.org/10.15587/1729-4061.2015.55908
  15. Law, M., Young, R., Babb, J., Pollack, E., Johnson, G. (2007). Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. American Journal of Neuroradiology, 28 (4), 761–766.
  16. Young, R., Babb, J., Law, M., Pollack, E., Johnson, G. (2007). Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas. Journal of Magnetic Resonance Imaging, 26 (4), 1053–1063. doi: http://doi.org/10.1002/jmri.21064

Downloads

Published

2019-10-31

How to Cite

Alkhimova, S. (2019). Impact of perfusion ROI detection to the quality of CBV perfusion map. Technology Audit and Production Reserves, 5(2(49), 27–30. https://doi.org/10.15587/2312-8372.2019.182789

Issue

Vol. 5 No. 2(49) (2019): Information and control systems

Section

Reports on research projects

License

Copyright (c) 2019 Svitlana Alkhimova

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.