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Licensed Unlicensed Requires Authentication Published by De Gruyter July 24, 2020

Workflow and hardware for intraoperative hyperspectral data acquisition in neurosurgery

  • Richard Mühle ORCID logo EMAIL logo , Hannes Ernst , Stephan B. Sobottka and Ute Morgenstern

Abstract

To prevent further brain tumour growth, malignant tissue should be removed as completely as possible in neurosurgical operations. Therefore, differentiation between tumour and brain tissue as well as detecting functional areas is very important. Hyperspectral imaging (HSI) can be used to get spatial information about brain tissue types and characteristics in a quasi-continuous reflection spectrum. In this paper, workflow and some aspects of an adapted hardware system for intraoperative hyperspectral data acquisition in neurosurgery are discussed. By comparing an intraoperative with a laboratory setup, the influences of the surgical microscope are made visible through the differences in illumination and a pixel- and wavelength-specific signal-to-noise ratio (SNR) calculation. Due to the significant differences in shape and wavelength-dependent intensity of light sources, it can be shown which kind of illumination is most suitable for the setups. Spectra between 550 and 1,000 nm are characterized of at least 40 dB SNR in laboratory and 25 dB in intraoperative setup in an area of the image relevant for evaluation. A first validation of the intraoperative hyperspectral imaging hardware setup shows that all system parts and intraoperatively recorded data can be evaluated. Exemplarily, a classification map was generated that allows visualization of measured properties of raw data. The results reveal that it is possible and beneficial to use HSI for wavelength-related intraoperative data acquisition in neurosurgery. There are still technical facts to optimize for raw data detection prior to adapting image processing algorithms to specify tissue quality and function.


Corresponding author: Richard Mühle, Faculty of Electrical and Computer Engineering, Institute of Biomedical Engineering, Technische Universität Dresden, 01062 Dresden, Germany; Department of Neurosurgery, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany, E-mail:

Funding source: European Social Fund

Acknowledgments

The authors would like to thank Julian Fischer for his advice and experimental assistance.

  1. Research funding: This work was financially supported by the European Social Fund (ESF).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

References

1. Stummer, W, Reulen, H-J, Meinel, T, Pichlmeier, U, Schumacher, W, Tonn, J-C, et al. Extent of resection and survival in glioblastoma multiforme: identification of and adjustment for bias. Neurosurgery 2008;62:564–76. https://doi.org/10.1227/01.neu.0000317304.31579.17.Search in Google Scholar

2. Senft, C, Bink, A, Franz, K, Vatter, H, Gasser, T, Seifert, V. Intraoperative MRI guidance and extent of resection in glioma surgery: a randomised, controlled trial. Lancet Oncol 2011;12:997–1003. https://doi.org/10.1016/s1470-2045(11)70196-6.Search in Google Scholar

3. Tharin, S, Golby, A. Functional brain mapping and its applications to neurosurgery. Oper Neurosurg 2007;60(4 Suppl):185–202. https://doi.org/10.1227/01.neu.0000255386.95464.52.Search in Google Scholar PubMed

4. Sobottka, SB, Meyer, T, Kirsch, M, Koch, E, Steinmeier, R, Morgenstern, U, et al. Intraoperative optical imaging of intrinsic signals: a reliable method for visualizing stimulated functional brain areas during surgery. J Neurosurg 2013;119:853–63. https://doi.org/10.3171/2013.5.jns122155.Search in Google Scholar

5. Meyer, T, Morgenstern, U, Kirsch, M, Schackert, G, Sobottka, SB. Intraoperative optical imaging of intrinsic signals for delineation of active functional brain areas. In: Nabavi, A, Samii, A, Fahlbusch, R, editors. Visualization of the brain and its pathologies – technical and neurosurgical aspects; 2016. ISBN/EAN: 9783862475773. pp. 92–175.Search in Google Scholar

6. Meyer, T, Sobottka, SB, Kirsch, M, Schackert, G, Steinmeier, R, Koch, E, et al. Intraoperative optical imaging of functional brain areas for improved image-guided surgery. Biomed Tech 2013;58:225–36 https://doi.org/10.1515/bmt-2012-0072.Search in Google Scholar PubMed

7. Sobottka, SB, Meyer, T, Kirsch, M, Koch, E, Steinmeier, R, Morgenstern, U, et al. Evaluation of the clinical practicability of intraoperative optical imaging comparing three different camera setups. Biomed Tech 2013;58:237–48. https://doi.org/10.1515/bmt-2012-0073.Search in Google Scholar PubMed

8. Oelschlägel, M, Meyer, T, Schackert, G, Kirsch, M, Sobottka, SB, Morgenstern, U. Intraoperative optical imaging of metabolic changes after direct cortical stimulation – a clinical tool for guidance during tumor resection?. Biomed Tech 2018;63:587–94. https://dx.doi.org/10.1515/bmt-2017-0156.10.1515/bmt-2017-0156Search in Google Scholar PubMed

9. Raab, P, Pilatus, U, Lanfermann, H. Spektroskopie bei Hirntumoren. Radiologie up2date. 2008;8:239–55. https://doi.org/10.1055/s-2008-1077415.Search in Google Scholar

10. Holm, E. Stoffwechel und Ernährung bei Tumorkrankheiten: Analysen und Empfehlungen, 15th ed. New York; 2007: vol. 2007, 41–58 pp.10.1055/b-002-39787Search in Google Scholar

11. Lu, G, Fei, B. Medical hyperspectral imaging: a review. J Biomed Opt 2014;19. 010901. https://doi.org/10.1117/1.JBO.19.1.010901.Search in Google Scholar PubMed PubMed Central

12. Fabelo, H, Ortega, S, Lazcano, R, Madroñal, D, M, CG, Juárez, E, et al. An intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation. Sensors 2018;18:430. https://doi.org/10.3390/s18020430.Search in Google Scholar PubMed PubMed Central

13. Markgraf, W, Feistel, P, Thiele, C, Malberg, H. Algorithms for mapping kidney tissue oxygenation during normothermic machine perfusion using hyperspectral imaging. Biomed Eng-Biomed Te 2018;63:557–66. https://doi.org/10.1515/bmt-2017-0216.Search in Google Scholar PubMed

14. Shapey, J, Xie, Y, Nabavi, E, Bradford, R, Saeed, S, Ourselin, S, et al. Intraoperative multispectral and hyperspectral label-free imaging: a systematic review of in vivo clinical studies. J Biophotonics 2019:12:e201800455. https://doi.org/10.1002/jbio.201800455.Search in Google Scholar PubMed PubMed Central

15. Khan, MJ, Khan, HS, Yousaf, A, Khurshid, K, Abbas, A. Modern trends in hyperspectral image analysis: a review. IEEE Access 2018;6:14118–29. https://doi.org/10.1109/access.2018.2812999.Search in Google Scholar

16. Ortega, S, Fabelo, H, Iakovidis, DK, Koulaouzidis, A, Callico, GM. Use of hyperspectral/multispectral imaging in gastroenterology. Shedding some–different–light into the dark. J Clin Med 2019;8:36. https://doi.org/10.3390/jcm8010036.Search in Google Scholar PubMed PubMed Central

17. Jansen-Winkeln, B, Holfert, N, Köhler, H, Moulla, Y, Takoh, JP, Rabe, SM, et al. Determination of the transection margin during colorectal resection with hyperspectral imaging (HSI). Int J Colorectal Dis 2019;34:731–9. https://doi.org/10.1007/s00384-019-03250-0.Search in Google Scholar PubMed

18. Fabelo, H, Ortega, S, Kabwama, S, Callico, GM, Bulters, D, Szolna, A, et al. HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations. In: Bannon, DP, editor. International Society for Optics and Photonics; 2016:986002p.book-chapter.10.1117/12.2223075Search in Google Scholar

19. Fletcher, JT, Kong, SG. Principal component analysis for poultry tumor inspection using hyperspectral fluorescence imaging. In: Proceedings of the International Joint Conference on Neural Networks, IEEE; 2004:149–53pp.confproc.10.1109/IJCNN.2003.1223319Search in Google Scholar

20. Sharma, HS, Hoopes, PJ. Hyperthermia induced pathophysiology of the central nervous system. Int J Hyperthermia 2003;19:325–54. https://doi.org/10.1080/0265673021000054621.Search in Google Scholar PubMed

21. Ersen, A, Abdo, A, Sahin, M. Temperature elevation profile inside the rat brain induced by a laser beam. J Biomed Opt 2014;19. 015009. https://doi.org/10.1117/1.jbo.19.1.015009.Search in Google Scholar

22. Holmer, A, Marotz, J, Wahl, P, Dau, M, Kämmerer, PW. Hyperspectral imaging in perfusion and wound diagnostics – methods and algorithms for the determination of tissue parameters. Biomed Eng-Biomed Te 2018;63:547–56. https://doi.org/10.1515/bmt-2017-0155.Search in Google Scholar PubMed

23. Semiconductor Components Industries L. AR0130CS 1/3-inch CMOS Digital Image Sensor; 2018. Available from: https://www.onsemi.com/pub/Collateral/AR0130CS-D.PDF.Search in Google Scholar

24. Rasti, B, Scheunders, P, Ghamisi, P, Licciardi, G, Chanussot, J. Noise reduction in hyperspectral imagery: overview and application. Remote Sens 2018;10:482. https://doi.org/10.3390/rs10030482.Search in Google Scholar

25. Rasti, B, Ulfarsson, MO, Ghamisi, P. Automatic hyperspectral image restoration using sparse and low-rank modeling. IEEE Geosci Remote S 2017;14:2335–9. https://doi.org/10.1109/lgrs.2017.2764059.Search in Google Scholar

Received: 2019-12-20
Accepted: 2020-05-27
Published Online: 2020-07-24
Published in Print: 2021-02-23

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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