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THE CHALLENGE OF TRACTOGRAPHY APPLIED TO CRANIAL NERVES: OUR EXPERIENCE ON DESIGN OF REGIONS OF INTEREST

Timothée Jacquesson^1,2,3, François Cotton^1,4, Justine Bosc¹, Moncef Berhouma^1,2, Emmanuel Jouanneau², Arnaud Attye⁵, and Carole Frindel¹

¹CREATIS UMR 5220, U1206, University of Lyon, Lyon, France, ²Skull Base Multi-disciplinary Unit, Hospices Civils de Lyon, Lyon, France, ³Department of Anatomy, University of Lyon, Lyon, France, ⁴Department of Radiology, Hospices Civils de Lyon, Lyon, France, ⁵Department of Radiology, Grenoble University Hospital, Grenoble, France

Synopsis

Recent studies have demonstrated diffusion tensor imaging tractography of cranial nerves (CNs). Spatial and angular resolution, however, is limited with this imaging technique. In this study, we reported our experience in CNs tractography detailing the influence of ROI design. We demonstrated that understanding in detail the key role of ROI design and its influence helps to provide coherent tracts. We expect this work to enable a more reliable CNs tractography and made it a useful tool for surgical planning of complex skull base tumors.

INTRODUCTION

Diffusion tensor imaging (DTI) allow to track white matter fibers in vivo through tractography ^1–3. Predicting cranial nerves (CNs) trajectory is important for skull base tumors surgery as well as for neuro-anatomy teaching ^4,5. However, the accuracy of tractography is hampered by the small-scale of CNs regarding the low spatial and angular resolution of DTI ^6,7. Tractography of CNs requires also a selective drawing of regions of interest (ROIs)⁸ to initiate the tracking process. ROI design was found to be highly variable in CN tractography studies: single ^9,10 versus multiple ROIs^11–15; ROI positioned on mid-cisternal segment^16,17, brainstem entry zone ^11–15 or cross section orthogonal to the CN trajectory ⁴; “single voxel” super selective strategy based on primary tracking map^18,19. We therefore propose to report our experience on CNs tractography from data acquisition to tracking parameters and study more specifically the influence of ROI size and placement.

METHODS

Participants: Patients who presented complex skull base tumors were addressed to our neurosurgical department (Lyon, France) and were proposed to participate in this study after information and consent.

Image acquisition: A dedicated diffusion sequence was acquired on a 3-T Achieva machine (Philips medical system) using a 32-channel head coil. The parameters of diffusion were: b-value = 1000 s/ mm2; 32 directions encoding scheme; voxel size = 2 mm isotropic; slice thickness = 2 mm; no slice gap; field of view = 224x224; scan time = 9’52. A T2 steady state sequence and a T1 post-contrast weighted sequence were added for anatomical reference and tumor morphology reconstruction.

Post processing tracking: Using FSL® (FMRIB software library, UK), geometric distortions were corrected using acquisition of two images for each diffusion gradient as proposed by Andersson et al. ²⁰. Tractography process was performed using Mrtrix3 package software (J-D Tournier, Brain Research Institute, Melbourne, Australia) ²¹. A constrained spherical deconvolution (6 spherical harmonic terms) was used to create a fiber orientation distribution function (ODF) map ²². ROIs to initiate tractography were selected by superimposing ODF map on T2 in order to identify the CN cisternal trajectory with high accuracy (Figure 1). For all CNs, probabilistic tractography was applied with the following parameters: step size=0.1 mm, minimum fiber length=10 mm, maximal turning angle=45°, fractional anisotropy cut-off=0.3. An estimated number of fibers was targeted for each CN on the basis of their own anatomical diameter. Fibers crossing towards the cerebellum were excluded using a mask. The whole post-processing lasted around 30 minutes using a computer with a multi-core processor (Intel Core® i7, 2.3 GHz, Intel Corporation®, USA / 16 Go 1600 MHz DDR3).

Validation: Fiber tracts were assessed by comparison with the previously identified nerves on the T2 anatomical reference or with known anatomical CNs trajectory through skull base cisterns or brainstem ^23,24. The position of displaced nerves was then confirmed intra operatively by direct visualization.

RESULTS AND DISCUSSION

Between 2016-2017, 35 patients were included and thus 700 CNs were tracked. At the healthy side, most CNs were properly tracked according to the T2 anatomic images (Figure 2) including optic, oculomotor, trigeminal, abducens, acoustic-facial and lower nerves. The trochlear nerve was too thin to be seen on T2 and the hypoglossal nerve was often out of the acquisition box. Around skull base tumors, the tracking was made difficult especially when CNs features were highly modified: stretching, encasement and environment changes. Concerning ROI design, the number of fibers as well as their dispersion slightly increased with the ROI size (Figure 3, A). But, changing the ROI size had in fact little influence as long as tensors that depicted the targeted CN trajectory were still picked and others CNs weren’t reached (as shown for the acoustic facial bundle in Figure 3 A). Conversely, even slightly moving the ROI changed significantly the amount of fibers reconstructed. Indeed, a ROI seeded close to the brainstem led to false continuations by recruiting ponto-cerebellar tracts (Figure 3 B left). While a ROI displaced laterally provided less fibers due to a decrease of anisotropy away from the brainstem through skull base foramina (Figure 3 B right).

CONCLUSION

In this study, we reported our experience in CNs tractography detailing the influence of ROI design. We demonstrated that understanding in detail the key role of ROI design and its influence helps to provide coherent tracts. We expect this work to enable a more reliable CNs tractography and made it a useful tool for surgical planning of complex skull base tumors.

Acknowledgements

No acknowledgement found.

References

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Figures

Figure 1: (A) Axial T2 cerebral MRI focused on right oculomotor nerve and (B) superimposition of orientation distribution function (ODF) map. (C) ROI placement (in yellow) was done at the best visualization of the CN cisternal tractography in the three dimensions (axial, sagittal and coronal). (D) Resulting fiber tracking: the oculomotor nerve trajectory is thus reconstructed in its whole cisternal segment.

Figure 2: CNs tractography superimposed on T2-Cerebral MRI in axial (A) sagittal (B) and coronal (C) views. Optic nerve (ON = II), oculomotor nerve (Oc = III), trigeminal nerve (Trig = V), abducens nerve (Abd = VI), acoustic-facial bundle (A-F = VII-VIII), lower nerves (LN = glosso-pharyngeal (IX), vagus (X) and accessory (XI) nerves) and hypoglossal nerve (Hyp) are seen in their own skull base cisternal trajectory.

Figure 3: Tractography results on the left acoustic-facial bundle superimposed on T2-cerebral MRI in axial view. (A) On the basis of the ROI design strategy explained in Figure 2 (central picture, ROI in yellow), the ROI is eroded (to the left) and expanded (to the right). (B) Then the ROI position is displaced medially (to the left) and laterally away from the brainstem (to the right). The number of fibers as well as their dispersion slightly increases with the ROI size. Moving the ROI close to the brainstem induces false continuations by recruiting ponto-cerebellar tracts.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)

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