IE-Map: a novel in-vivo atlas and template of the human inner ear

Ahmadi, Seyed-Ahmad; Raiser, Theresa; Ruehl, Ria Maxine; Flanagin, Virginia L.; Eulenburg, Peter zu

doi:10.1038/s41598-021-82716-0

Cited by 8 publications

(3 citation statements)

References 76 publications

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“…(a) top row : sagittal, coronal, and axial views through the left inner ear of the Ahmadi et al, 2021 atlas. The cochlea is seen, as is a portion of the lateral semi-circular canal (SCC) in the axial view.…”

Section: Resultsmentioning

confidence: 99%

“…We also calculated the rotation of the vestibular apparatus in each subject with respect to the scanner coordinates and thus the B 0 magnetic field. To do so, we first diffeomorphically registered an MRI atlas of the human vestibular system (Ahmadi et al, 2021) to the T2 image from each subject using ANTs (Avants et al, 2011). The atlas outlines each of the six SSCs with a set of fiducial markers defined in the magnet coordinates of the image.…”

Section: Methodsmentioning

confidence: 99%

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Persistent horizontal and vertical, MR-induced nystagmus in resting state Human Connectome Project data

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Ahmadi

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Strong magnetic fields from magnetic resonance (MR) scanners induce a Lorentz force that contributes to vertigo and persistent nystagmus. Prior studies have reported a predominantly horizontal direction for healthy subjects in a 7 Tesla (T) MR scanner, with slow phase velocity (SPV) dependent on head orientation. Less is known about vestibular signal behavior for subjects in a weaker, 3T magnetic field, the standard strength used in the Human Connectome Project (HCP). The purpose of this study is to characterize the form and magnitude of nystagmus induced at 3T. Forty-two subjects were studied after being introduced head-first, supine into a Siemens Prisma 3T scanner. Eye movements were recorded in four separate acquisitions over 20 minutes. A biometric eye model was fit to the recordings to derive rotational eye position and then SPV. An anatomical template of the semi-circular canals was fit to the T2 anatomical image from each subject, and used to derive the angle of the B0 magnetic field with respect to the vestibular apparatus. Recordings from 37 subjects yielded valid measures of eye movements. The population-mean SPV ± SD for the horizontal component was -1.38 ± 1.27 deg/sec, and vertical component was -0.93 ± 1.44 deg/sec, corresponding to drift movement in the rightward and downward direction. Although there was substantial inter-subject variability, persistent nystagmus was present in half of subjects with no significant adaptation over the 20 minute scanning period. The amplitude of vertical drift was correlated with the roll angle of the vestibular system, with a non-zero vertical SPV present at a 0 degree roll. We conclude that non-habituating vestibular signals of varying amplitude are present in resting state data collected at 3T.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Persistent horizontal and vertical, MR-induced nystagmus in resting state Human Connectome Project data

Go¹,

Taskin²,

Ahmadi

et al. 2022

Preprint

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“…Most available 3D TFS segmentation approaches are either manual ( 24 , 26 ), or atlas-based ( 29 , 30 ). However, atlas-based segmentation uses deformable image registration that entails several challenges ( 31 ).…”

Section: Introductionmentioning

confidence: 99%

IE-Vnet: Deep Learning-Based Segmentation of the Inner Ear's Total Fluid Space

et al. 2022

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BackgroundIn-vivo MR-based high-resolution volumetric quantification methods of the endolymphatic hydrops (ELH) are highly dependent on a reliable segmentation of the inner ear's total fluid space (TFS). This study aimed to develop a novel open-source inner ear TFS segmentation approach using a dedicated deep learning (DL) model.MethodsThe model was based on a V-Net architecture (IE-Vnet) and a multivariate (MR scans: T1, T2, FLAIR, SPACE) training dataset (D1, 179 consecutive patients with peripheral vestibulocochlear syndromes). Ground-truth TFS masks were generated in a semi-manual, atlas-assisted approach. IE-Vnet model segmentation performance, generalizability, and robustness to domain shift were evaluated on four heterogenous test datasets (D2-D5, n = 4 × 20 ears).ResultsThe IE-Vnet model predicted TFS masks with consistently high congruence to the ground-truth in all test datasets (Dice overlap coefficient: 0.9 ± 0.02, Hausdorff maximum surface distance: 0.93 ± 0.71 mm, mean surface distance: 0.022 ± 0.005 mm) without significant difference concerning side (two-sided Wilcoxon signed-rank test, p>0.05), or dataset (Kruskal-Wallis test, p>0.05; post-hoc Mann-Whitney U, FDR-corrected, all p>0.2). Prediction took 0.2 s, and was 2,000 times faster than a state-of-the-art atlas-based segmentation method.ConclusionIE-Vnet TFS segmentation demonstrated high accuracy, robustness toward domain shift, and rapid prediction times. Its output works seamlessly with a previously published open-source pipeline for automatic ELS segmentation. IE-Vnet could serve as a core tool for high-volume trans-institutional studies of the inner ear. Code and pre-trained models are available free and open-source under https://github.com/pydsgz/IEVNet.

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