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Abstract 7‐Tesla (T) magnetic resonance imaging may allow for higher resolution images but may produce greater acoustic noise than 1.5‐ and 3‐T scanners. We sought to characterize the intensity of acoustic noise from 7‐ versus 3‐T scanners. A‐weighted sound pressure levels from 5 types of pulse sequences used for brain and inner ear imaging in 3‐ and 7‐T scanners were measured. Time‐averaged sound level and maximum sound levels generated for each sequence were compared. Time‐averaged sound levels exceeded 95 dB and reached maximums above 105 dB on the majority of 3‐ and 7‐T scans. The mean time‐averaged sound level and maximum sound level across pulse sequences were greater in 7‐ than 3‐T (105.6 vs 91.4, P = .01; 114.0 vs. 96.5 dB, P

Background: In-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. Methods: The 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). Results: The 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. Conclusion: IE-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. [ABSTRACT FROM AUTHOR]

In congenital vestibular disorders (CVDs), children develop an abnormal inner ear before birth and face postnatal challenges to maintain posture, balance, walking, eye-hand coordination, eye tracking, or reading. Only limited information on inner ear pathology is acquired from clinical imaging of the temporal bone or studying histological slides of the temporal bone. A more comprehensive and precise assessment and determination of the underlying mechanisms necessitate analyses of the disorders at the cellular level, which can be achieved using animal models. Two main criteria for a suitable animal model are first, a pathology that mirrors the human disorder, and second, a reproducible experimental outcome leading to statistical power. With over 40 genes that affect inner ear development, the phenotypic abnormalities resulting from congenital vestibular disorders (CVDs) are highly variable. Nonetheless, there is a large subset of CVDs that form a common phenotype of a sac-like inner ear with the semicircular canals missing or dysplastic, and discrete abnormalities in the vestibular sensory organs. We have focused the review on this subset, but to advance research on CVDs we have added other CVDs not forming a sac-like inner ear. We have included examples of animal models used to study these CVDs. Presently, little is known about the central pathology resulting from CVDs at the cellular level in the central vestibular neural network, except for preliminary studies on a chick model that show significant loss of second-order, vestibular reflex projection neurons. [ABSTRACT FROM AUTHOR]

In congenital vestibular disorders (CVDs), children develop an abnormal inner ear before birth and face postnatal challenges to maintain posture, balance, walking, eye-hand coordination, eye tracking, or reading. Only limited information on inner ear pathology is acquired from clinical imaging of the temporal bone or studying histological slides of the temporal bone. A more comprehensive and precise assessment and determination of the underlying mechanisms necessitate analyses of the disorders at the cellular level, which can be achieved using animal models. Two main criteria for a suitable animal model are first, a pathology that mirrors the human disorder, and second, a reproducible experimental outcome leading to statistical power. With over 40 genes that affect inner ear development, the phenotypic abnormalities resulting from congenital vestibular disorders (CVDs) are highly variable. Nonetheless, there is a large subset of CVDs that form a common phenotype of a sac-like inner ear with the semicircular canals missing or dysplastic, and discrete abnormalities in the vestibular sensory organs. We have focused the review on this subset, but to advance research on CVDs we have added other CVDs not forming a sac-like inner ear. We have included examples of animal models used to study these CVDs. Presently, little is known about the central pathology resulting from CVDs at the cellular level in the central vestibular neural network, except for preliminary studies on a chick model that show significant loss of second-order, vestibular reflex projection neurons.

In-vivo non-invasive verification of endolymphatic hydrops (ELH) by means of intravenous delayed gadolinium (Gd) enhanced magnetic resonance imaging of the inner ear (iMRI) is rapidly developing into a standard clinical tool to investigate peripheral vestibulo-cochlear syndromes. In this context, methodological comparative studies providing standardization and comparability between labs seem even more important, but so far very few are available. One hundred eight participants [75 patients with Meniere's disease (MD; 55.2 ± 14.9 years) and 33 vestibular healthy controls (HC; 46.4 ± 15.6 years)] were examined. The aim was to understand (i) how variations in acquisition protocols influence endolymphatic space (ELS) MR-signals; (ii) how ELS quantification methods correlate to each other or clinical data; and finally, (iii) how ELS extent influences MR-signals. Diagnostics included neuro-otological assessment, video-oculography during caloric stimulation, head-impulse test, audiometry, and iMRI. Data analysis provided semi-quantitative (SQ) visual grading and automatic algorithmic quantitative segmentation of ELS area [2D, mm2] and volume [3D, mm3] using deep learning-based segmentation and volumetric local thresholding. Within the range of 0.1–0.2 mmol/kg Gd dosage and a 4 h ± 30 min time delay, SQ grading and 2D- or 3D-quantifications were independent of signal intensity (SI) and signal-to-noise ratio (SNR; FWE corrected, p < 0.05). The ELS quantification methods used were highly reproducible across raters or thresholds and correlated strongly (0.3–0.8). However, 3D-quantifications showed the least variability. Asymmetry indices and normalized ELH proved the most useful for predicting quantitative clinical data. ELH size influenced SI (cochlear basal turn p < 0.001), but not SNR. SI could not predict the presence of ELH. In conclusion, (1) Gd dosage of 0.1–0.2 mmol/kg after 4 h ± 30 min time delay suffices for ELS quantification. (2) A consensus is needed on a clinical SQ grading classification including a standardized level of evaluation reconstructed to anatomical fixpoints. (3) 3D-quantification methods of the ELS are best suited for correlations with clinical variables and should include both ears and ELS values reported relative or normalized to size. (4) The presence of ELH increases signal intensity in the basal cochlear turn weakly, but cannot predict the presence of ELH.

In-vivo non-invasive verification of endolymphatic hydrops (ELH) by means of intravenous delayed gadolinium (Gd) enhanced magnetic resonance imaging of the inner ear (iMRI) is rapidly developing into a standard clinical tool to investigate peripheral vestibulo-cochlear syndromes. In this context, methodological comparative studies providing standardization and comparability between labs seem even more important, but so far very few are available. One hundred eight participants [75 patients with Meniere's disease (MD; 55.2 ± 14.9 years) and 33 vestibular healthy controls (HC; 46.4 ± 15.6 years)] were examined. The aim was to understand (i) how variations in acquisition protocols influence endolymphatic space (ELS) MR-signals; (ii) how ELS quantification methods correlate to each other or clinical data; and finally, (iii) how ELS extent influences MR-signals. Diagnostics included neuro-otological assessment, video-oculography during caloric stimulation, head-impulse test, audiometry, and iMRI. Data analysis provided semi-quantitative (SQ) visual grading and automatic algorithmic quantitative segmentation of ELS area [2D, mm2] and volume [3D, mm3] using deep learning-based segmentation and volumetric local thresholding. Within the range of 0.1–0.2 mmol/kg Gd dosage and a 4 h ± 30 min time delay, SQ grading and 2D- or 3D-quantifications were independent of signal intensity (SI) and signal-to-noise ratio (SNR; FWE corrected, p < 0.05). The ELS quantification methods used were highly reproducible across raters or thresholds and correlated strongly (0.3–0.8). However, 3D-quantifications showed the least variability. Asymmetry indices and normalized ELH proved the most useful for predicting quantitative clinical data. ELH size influenced SI (cochlear basal turn p < 0.001), but not SNR. SI could not predict the presence of ELH. In conclusion, (1) Gd dosage of 0.1–0.2 mmol/kg after 4 h ± 30 min time delay suffices for ELS quantification. (2) A consensus is needed on a clinical SQ grading classification including a standardized level of evaluation reconstructed to anatomical fixpoints. (3) 3D-quantification methods of the ELS are best suited for correlations with clinical variables and should include both ears and ELS values reported relative or normalized to size. (4) The presence of ELH increases signal intensity in the basal cochlear turn weakly, but cannot predict the presence of ELH. [ABSTRACT FROM AUTHOR]

7-Tesla (T) magnetic resonance imaging may allow for higher resolution images but may produce greater acoustic noise than 1.5- and 3-T scanners. We sought to characterize the intensity of acoustic noise from 7- versus 3-T scanners. A-weighted sound pressure levels from 5 types of pulse sequences used for brain and inner ear imaging in 3- and 7-T scanners were measured. Time-averaged sound level and maximum sound levels generated for each sequence were compared. Time-averaged sound levels exceeded 95 dB and reached maximums above 105 dB on the majority of 3- and 7-T scans. The mean time-averaged sound level and maximum sound level across pulse sequences were greater in 7- than 3-T (105.6 vs 91.4, P  = .01; 114.0 vs. 96.5 dB, P  < .01). 7- and 3-T magnetic resonance imaging scanners produce high levels of acoustic noise that exceed acceptable safety limits, emphasizing the need for active and passive noise protection., Competing Interests: The authors declare that there is no conflict of interest., (© 2023 The Authors. OTO Open published by Wiley Periodicals LLC on behalf of American Academy of Otolaryngology–Head and Neck Surgery Foundation.)