High-resolution in vivo imaging of peripheral nerves using optical coherence tomography: a feasibility study

Restricted access

OBJECTIVE

Because of their complex topography, long courses, and small diameters, peripheral nerves are challenging structures for radiological diagnostics. However, imaging techniques in the area of peripheral nerve diseases have undergone unexpected development in recent decades. They include MRI and high-resolution sonography (HRS). Yet none of those imaging techniques reaches a resolution comparable to that of histological sections. Fascicles are the smallest discernable structure. Optical coherence tomography (OCT) is the first imaging technique that is able to depict a nerve’s ultrastructure at micrometer resolution. In the current study, the authors present an in vivo assessment of human peripheral nerves using OCT.

METHODS

OCT measurement was performed in 34 patients with different peripheral nerve pathologies, i.e., nerve compression syndromes. The nerves were examined during surgery after their exposure. Only the sural nerve was twice examined ex vivo. The Thorlabs OCT systems Callisto and Ganymede were used. For intraoperative use, a hand probe was covered with a sterile foil. Different postprocessing imaging techniques were applied and evaluated. In order to highlight certain structures, five texture parameters based on gray-level co-occurrence matrices were calculated according to Haralick.

RESULTS

The intraoperative use of OCT is easy and intuitive. Image artifacts are mainly caused by motion and the sterile foil. If the artifacts are kept at a low level, the hyporeflecting bundles of nerve fascicles and their inner parts can be displayed. In the Haralick evaluation, the second angular moment is most suitable to depict the connective tissue.

CONCLUSIONS

OCT is a new imaging technique that has shown promise in peripheral nerve surgery for particular questions. Its resolution exceeds that provided by recent radiological possibilities such as MRI and HRS. Since its field of view is relatively small, faster acquisition times would be highly desirable and have already been demonstrated by other groups. Currently, the method resembles an optical biopsy and can be a supplement to intraoperative sonography, giving high-resolution insight into a suspect area that has been located by sonography in advance.

ABBREVIATIONS OCT = optical coherence tomography.

Article Information

Correspondence Anne Elisabeth Carolus: University Hospital Knappschaftskrankenhaus Bochum, Bochum, Germany. anneelisabeth.carolus@kk-bochum.de; anne.carolus@googlemail.com.

INCLUDE WHEN CITING Published online April 26, 2019; DOI: 10.3171/2019.2.JNS183542.

A.E.C. and M.L. contributed equally to this work.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

© AANS, except where prohibited by US copyright law.

Headings

Figures

  • View in gallery

    Comparison of field of view and resolution between sonography and OCT. An ex vivo ultrasonic (US) image (A) provides a larger field of view but poorer resolution compared with the OCT image (B). The red square in A depicts the field of view for OCT in B. Figure is available in color online only.

  • View in gallery

    Measuring procedure hand probe (A), hand probe with foil set down on exposed peripheral nerve (B), monitor (C), scan of the section (D), and measured OCT result (E). Figure is available in color online only.

  • View in gallery

    Sural nerve measured with the Thorlabs Callisto (A) and Ganymede (B) systems. Figure is available in color online only.

  • View in gallery

    Tibial nerve (A) and lateral femoral cutaneous nerve (LFCN) (B).

  • View in gallery

    Median nerve (A), ulnar nerve (B), radial nerve (C), and peroneal nerve (D).

  • View in gallery

    Texture analysis of the peroneal nerve using Haralick’s algorithm in a two-dimensional (A) and a three-dimensional (B) scan. DIS = dissimilarity; EN = entropy; IDM = inverse difference moment; KON = contrast; SAM = second angular moment. Figure is available in color online only.

References

  • 1

    Anantrasirichai NAchim AMorgan JEErchova INicholson L: SVM-based texture classification in optical coherence tomography. In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI). Piscataway, NJ: IEEE, 2013 pp 13321335

    • Search Google Scholar
    • Export Citation
  • 2

    Böhringer HJBoller DLeppert JKnopp ULankenau EReusche E: Time-domain and spectral-domain optical coherence tomography in the analysis of brain tumor tissue. Lasers Surg Med 38:5885972006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Boppart SA: Optical coherence tomography: technology and applications for neuroimaging. Psychophysiology 40:5295412003

  • 4

    Brezinski METearney GJBoppart SASwanson EASouthern JFFujimoto JG: Optical biopsy with optical coherence tomography: feasibility for surgical diagnostics. J Surg Res 71:32401997

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Carrasco-Zevallos OMViehland CKeller BDraelos MKuo ANToth CA: Review of intraoperative optical coherence tomography: technology and applications [Invited]. Biomed Opt Express 8:160716372017

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    Chianca VAlbano DMessina CCinnante CMTriulzi FMSardanelli F: Diffusion tensor imaging in the musculoskeletal and peripheral nerve systems: from experimental to clinical applications. Eur Radiol Exp 1:122017

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    El-Haddad MTTao YK: Advances in intraoperative optical coherence tomography for surgical guidance. Curr Opin Biomed Eng 3:37482017

  • 8

    Gebejes AHuertas R: Texture characterization based on grey-level co-occurrence matrix, in Proceedings of the Conference on Informatics and Management Sciences. Zilina, Slovakia: University of Zilina2013 Vol 2 pp 375378

    • Export Citation
  • 9

    Gossage KWSmith CMKanter EMHariri LPStone ALRodriguez JJ: Texture analysis of speckle in optical coherence tomography images of tissue phantoms. Phys Med Biol 51:156315752006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Gossage KWTkaczyk TSRodriguez JJBarton JK: Texture analysis of optical coherence tomography images: feasibility for tissue classification. J Biomed Opt 8:5705752003

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Haralick RMShanmugam KDinstein I: Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3:6106211973

  • 12

    Hiltunen JSuortti TArvela SSeppä MJoensuu RHari R: Diffusion tensor imaging and tractography of distal peripheral nerves at 3 T. Clin Neurophysiol 116:231523232005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13

    Kermarrec EDemondion XKhalil CLe Thuc VBoutry NCotten A: Ultrasound and magnetic resonance imaging of the peripheral nerves: current techniques, promising directions, and open issues. Semin Musculoskelet Radiol 14:4634722010

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Khalil CBudzik JFKermarrec EBalbi VLe Thuc VCotten A: Tractography of peripheral nerves and skeletal muscles. Eur J Radiol 76:3913972010

  • 15

    Lenz MKrug RDillmann CStroop RGerhardt NCWelp H: Automated differentiation between meningioma and healthy brain tissue based on optical coherence tomography ex vivo images using texture features. J Biomed Opt 23:172018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16

    Liu YYChen MIshikawa HWollstein GSchuman JSRehg JM: Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding. Med Image Anal 15:7487592011

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17

    Pham M: [MR neurography for lesion localization in the peripheral nervous system. Why, when and how?] Nervenarzt 85:2212372014 (Ger)

  • 18

    Romero-Ortega M: Peripheral nerves, anatomy and physiology of. In Jaeger DJung R (eds): Encyclopedia of Computational Neuroscience. New York: Springer2014

    • Crossref
    • Search Google Scholar
    • Export Citation

TrendMD

Metrics

Metrics

All Time Past Year Past 30 Days
Abstract Views 283 283 74
Full Text Views 52 52 8
PDF Downloads 37 37 7
EPUB Downloads 0 0 0

PubMed

Google Scholar