Dataset Information

Diffusion MRI of the facial-vestibulocochlear nerve complex: a prospective clinical validation study.

ABSTRACT:

Objectives

Surgical planning of vestibular schwannoma surgery would benefit greatly from a robust method of delineating the facial-vestibulocochlear nerve complex with respect to the tumour. This study aimed to optimise a multi-shell readout-segmented diffusion-weighted imaging (rs-DWI) protocol and develop a novel post-processing pipeline to delineate the facial-vestibulocochlear complex within the skull base region, evaluating its accuracy intraoperatively using neuronavigation and tracked electrophysiological recordings.

Methods

In a prospective study of five healthy volunteers and five patients who underwent vestibular schwannoma surgery, rs-DWI was performed and colour tissue maps (CTM) and probabilistic tractography of the cranial nerves were generated. In patients, the average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD-95) were calculated with reference to the neuroradiologist-approved facial nerve segmentation. The accuracy of patient results was assessed intraoperatively using neuronavigation and tracked electrophysiological recordings.

Results

Using CTM alone, the facial-vestibulocochlear complex of healthy volunteer subjects was visualised on 9/10 sides. CTM were generated in all 5 patients with vestibular schwannoma enabling the facial nerve to be accurately identified preoperatively. The mean ASSD between the annotators' two segmentations was 1.11 mm (SD 0.40) and the mean HD-95 was 4.62 mm (SD 1.78). The median distance from the nerve segmentation to a positive stimulation point was 1.21 mm (IQR 0.81-3.27 mm) and 2.03 mm (IQR 0.99-3.84 mm) for the two annotators, respectively.

Conclusions

rs-DWI may be used to acquire dMRI data of the cranial nerves within the posterior fossa.

Clinical relevance statement

Readout-segmented diffusion-weighted imaging and colour tissue mapping provide 1-2 mm spatially accurate imaging of the facial-vestibulocochlear nerve complex, enabling accurate preoperative localisation of the facial nerve. This study evaluated the technique in 5 healthy volunteers and 5 patients with vestibular schwannoma.

Key points

• Readout-segmented diffusion-weighted imaging (rs-DWI) with colour tissue mapping (CTM) visualised the facial-vestibulocochlear nerve complex on 9/10 sides in 5 healthy volunteer subjects. • Using rs-DWI and CTM, the facial nerve was visualised in all 5 patients with vestibular schwannoma and within 1.21-2.03 mm of the nerve's true intraoperative location. • Reproducible results were obtained on different scanners.

SUBMITTER: Shapey J

PROVIDER: S-EPMC10598116 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

Diffusion MRI of the facial-vestibulocochlear nerve complex: a prospective clinical validation study.

Shapey Jonathan J Vos Sjoerd B SB Mancini Laura L Sanders Brett B Thornton John S JS Tournier Jacques-Donald JD Saeed Shakeel R SR Kitchen Neil N Khalil Sherif S Grover Patrick P Bradford Robert R Dorent Reuben R Sparks Rachel R Vercauteren Tom T Yousry Tarek T Bisdas Sotirios S Ourselin Sebastien S

European radiology 20230617 11

<h4>Objectives</h4>Surgical planning of vestibular schwannoma surgery would benefit greatly from a robust method of delineating the facial-vestibulocochlear nerve complex with respect to the tumour. This study aimed to optimise a multi-shell readout-segmented diffusion-weighted imaging (rs-DWI) protocol and develop a novel post-processing pipeline to delineate the facial-vestibulocochlear complex within the skull base region, evaluating its accuracy intraoperatively using neuronavigation and tra ...[more]

PMID: 37328641

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Dataset Information

Diffusion MRI of the facial-vestibulocochlear nerve complex: a prospective clinical validation study.

Objectives

Methods

Results

Conclusions

Clinical relevance statement

Key points

Publications

Diffusion MRI of the facial-vestibulocochlear nerve complex: a prospective clinical validation study.

Similar Datasets

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