Role of dynamic sleep MRI in obstructive sleep apnea syndrome

Gamaleldin, Omneya; Bahgat, Ahmed; Anwar, Omnia; Seif-Elnasr, Mahmoud; Eissa, Lamya; Razek, Ahmed Abdel Khalek Abdel; Shehata, Gihan M.; Khalifa, Mohamed Hossameldin

doi:10.1007/s11282-020-00455-w

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…The VOTE classification with DISE, which describes the configuration of collapse as A-P, lateral, or concentric, can also be correlated with axial images being taken at the narrowest point in the RP and RG areas (which correlating to the inferior tip of the uvula and the superior tip of the epiglottis) [36]. Preliminary dynamic sleep MRI results have shown to be highly correlated to DISE findings in the RP area although correlation in the RG area was not statistically significant [27]. In addition, PSG while in MRI, Peripheral arterial tone (PAT), and Electroencephalogram (EEG) studies can assist with monitoring the quality and stage of sleep during which the dynamic MRI images are obtained.…”

Section: Discussionmentioning

confidence: 99%

“…The NP region is defined as the air space above the hard palate. RP is defined superiorly from an extension from the hard palate to the inferior border of the uvula [20,26,27]. RG is defined from inferior border of the uvula to the superior border of the epiglottis [6,24,[26][27][28].…”

Section: Meta-analysismentioning

confidence: 99%

“…RP is defined superiorly from an extension from the hard palate to the inferior border of the uvula [20,26,27]. RG is defined from inferior border of the uvula to the superior border of the epiglottis [6,24,[26][27][28]. Some studies extended RG to the base of epiglottis, however a HP region were not defined in those studies [20,25].…”

Section: Meta-analysismentioning

confidence: 99%

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Dynamic sleep MRI in obstructive sleep apnea: a systematic review and meta-analysis

Volner

Chao

Camacho

2021

Eur Arch Otorhinolaryngol

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Purpose The objective of this study is to systematically review the international literature for dynamic sleep magnetic resonance imaging (MRI) as a diagnostic tool in obstructive sleep apnea (OSA), to perform meta-analysis on the quantitative data from the review, and to discuss its implications in future research and potential clinical applications. Study design A comprehensive review of the literature was performed, followed by a detailed analysis of the relevant data that has been published on the topic. Methods Clinical key, Uptodate, Ovid, Ebscohost, Pubmed/MEDLINE, Scopus, Dynamed, Web of Science and The Cochrane Library were systematically searched. Once the search was completed, dynamic sleep MRI data were analyzed. Results Nineteen articles reported on 410 OSA patients and 79 controls that underwent dynamic sleep MRI and were included in this review. For meta-analysis of dynamic sleep MRI data, eight articles presented relevant data on 160 OSA patients. Obstruction was reported as follows: retropalatal (RP) 98%, retroglossal (RG) 41% and hypopharyngeal (HP) in 5%. Lateral pharyngeal wall (LPW) collapse was found in 35/73 (48%) patients. The combinations of RP + RG were observed in 24% and RP + RG + LPW in 16%. If sedation was used, 98% of study participants fell asleep compared to 66% of unsedated participants. Conclusions Dynamic sleep MRI has demonstrated that nearly all patients have retropalatal obstruction, retroglossal obstruction is common and hypopharyngeal obstruction is rare. Nearly all patients (98%) who are sedated are able to fall asleep during the MRI. There is significant heterogeneity in the literature and standardization is needed.

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Section: Discussionmentioning

confidence: 99%

Section: Meta-analysismentioning

confidence: 99%

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Dynamic sleep MRI in obstructive sleep apnea: a systematic review and meta-analysis

Volner

Chao

Camacho

2021

Eur Arch Otorhinolaryngol

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“…There have been numerous attempts to evaluate the upper airway anatomy in OSA patients. Imaging including videofluoroscopy, cephalometry, computed tomography (CT), and dynamic sleep magnetic resonance imaging (MRI) have been identified to play important roles in locating obstruction sites and abnormalities of hard and soft tissues of the upper airway [6][7][8]. Previous studies showed that certain structural craniofacial characteristics of patients observed on lateral cephalometric analysis were closely associated with pathophysiologic aspects of OSA [9,10].…”

Section: Introductionmentioning

confidence: 99%

Hyoid bone position as an indicator of severe obstructive sleep apnea

Park

Jang

et al. 2022

BMC Pulm Med

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Background The objective of this study was to evaluate the relationship between hyoid bone position and severity of obstructive sleep apnea (OSA), and to investigate its value as a complementary diagnostic method. Methods A total of 133 patients who were diagnosed as OSA with an apnea-hypopnea index ≥ 5 were included. Clinical examination, level I polysomnography (PSG) and lateral cephalographic analysis were done. Comprehensive PSG characteristics were compared according to hyoid bone position and the predictive power of the distance between the mandible and hyoid was assessed. Results The distance between the hyoid bone and mandibular plane was significantly longer in the severe OSA group (p = 0.013). The distance from hyoid bone to third vertebrae (C3) and hyoid bone to mentum were also longer in the severe OSA group but the difference did not reach statistical significance. The distance between hyoid bone and mandibular plane was effective in predicting severe OSA, with a cut-off value of 19.45 mm (AUC = 0.623, p = 0.040). When grouped according to a distance cut-off value of 19.45 mm, those with a longer distance between the hyoid bone and mandibular plane showed more respiratory disturbance, lower oxygen saturation levels, less deep slow wave sleep, and more fragmented sleep with arousals. Conclusions The distance between the hyoid bone and mandibular plane derived from cephalometric analysis can be a valuable diagnostic parameter that can be easily applied in differentiating severe OSA patients.

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“…Untreated OSA may cause serious health issues, including cardiovascular disease, metabolic disorders, and cognitive impairment, among others 2 . Common approaches for investigating OSA include polysomnography (PSG) for recording physiological data associated with sleep and breathing, 3 drug‐induced sleep endoscopy (DISE) for assessing the airway for anatomical sites of airway narrowing or obstruction, 4 computed tomography (CT) for measuring nasopharynx, oropharynx, and hypopharynx areas, 5,6 and magnetic resonance imaging (MRI) for studying the anatomical structure of the upper airway and surrounding tissues 7,8 . Compared with PSG, DISE, and CT, MRI has the advantages of delineating upper airway anatomy in OSA, locating sites of airway obstruction, guiding surgical treatment, 8 as well as lack of ionizing radiation exposure (for DISE and MRI 9 ), affording an increasing popularity in OSA application.…”

Section: Introductionmentioning

confidence: 99%

Automatic upper airway segmentation in static and dynamic MRI via anatomy‐guided convolutional neural networks

et al. 2021

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Upper airway segmentation on MR images is a prerequisite step for quantitatively studying the anatomical structure and function of the upper airway and surrounding tissues. However, the complex variability of intensity and shape of anatomical structures and different modes of image acquisition commonly used in this application makes automatic upper airway segmentation challenging. In this paper, we develop and test a comprehensive deep learning-based segmentation system for use on MR images to address this problem. Materials and Methods: In our study, both static and dynamic MRI data sets are utilized, including 58 axial static 3D MRI studies, 22 mid-retropalatal dynamic 2D MRI studies, 21 mid-retroglossal dynamic 2D MRI studies, 36 mid-sagittal dynamic 2D MRI studies, and 23 isotropic dynamic 3D MRI studies, involving a total of 160 subjects and over 20 000 MRI slices. Samples of static and 2D dynamic MRI data sets were randomly divided into training, validation, and test sets by an approximate ratio of 5:2:3. Considering that the variability of annotation data among 3D dynamic MRIs was greater than for other MRI data sets, we increased the ratio of training data for these data to improve the robustness of the model. We designed a unified framework consisting of the following procedures. For static MRI, a generalized region-of -interest (GROI) strategy is applied to localize the partitions of nasal cavity and other portions of upper airway in axial data sets as two separate subobjects. Subsequently, the two subobjects are segmented by two separate 2D U-Nets. The two segmentation results are combined as the whole upper airway structure. The GROI strategy is also applied to other MRI modes. To minimize false-positive and false-negative rates in the segmentation results, we employed a novel loss function based explicitly on these rates to train the segmentation networks. An inter-reader study is conducted to test the performance of our system in comparison to human variability in ground truth (GT) segmentation of these challenging structures. Results: The proposed approach yielded mean Dice coefficients of 0.84±0.03, 0.89±0.13, 0.84±0.07, and 0.86±0.05 for static 3D MRI, mid-retropalatal/midretroglossal 2D dynamic MRI, mid-sagittal 2D dynamic MRI, and isotropic dynamic 3D MRI, respectively. The quantitative results show excellent agreement with manual delineation results. The inter-reader study results demonstrate that the segmentation performance of our approach is statistically indistinguishable from manual segmentations considering the inter-reader variability in GT.

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Role of dynamic sleep MRI in obstructive sleep apnea syndrome

Cited by 10 publications

References 21 publications

Dynamic sleep MRI in obstructive sleep apnea: a systematic review and meta-analysis

Dynamic sleep MRI in obstructive sleep apnea: a systematic review and meta-analysis

Hyoid bone position as an indicator of severe obstructive sleep apnea

Automatic upper airway segmentation in static and dynamic MRI via anatomy‐guided convolutional neural networks

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