Synthetic MRI for Quantitative Assessment of Intrinsic Alterations in Upper Airway Tissues in Obstructive Sleep Apnea

Synthetic MRI for Quantitative Assessment of Intrinsic Alterations in Upper Airway Tissues in Obstructive Sleep Apnea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Synthetic MRI for Quantitative Assessment of Intrinsic Alterations in Upper Airway Tissues in Obstructive Sleep Apnea Jia Chen, Ge Si, Shiyi Wen, Zhiyuan Wang, Pei Li, Yuchen Kang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6379963/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background : Non-invasive quantitative assessment of intrinsic upper airway tissue alterations in patients with obstructive sleep apnea (OSA) is vital for mechanistic research. This study aims to evaluate the application of synthetic MRI (SyMRI) in this field. Methods : This was a cross-sectional study that prospectively collected data from a single centre. Participants, both with and without OSA, underwent SyMRI of the upper airway to measure quantitative changes in the intrinsic properties (T1, T2, and PD values) of the soft palate and tongue.These changes were annotated using the IDEAL-IQ MRI sequence and histopathology methods, including Masson, H&E and Oil Red O staining. Results : 125 (82 OSA and 43 non-OSA) participants were enrolled. Patients with OSA exhibited higher T2 value in the soft palate and higher T2 and PD values in the tongue compared to non-OSA participants. SyMRI parameters were correlated with the fat fraction derived from IDEAL-IQsequence. A positive correlation was observed between the T2 value and the area of fat, mucous gland and fibrosis in the soft palate. T2 and PD values of tongue showed independent relationship with AHI. The area under curve (AUC) values of T2 and PD of tongue were 0.756. Conclusion : SyMRI could act as an effective noninvasive method to quantify inherent alterations of upper airway tissues in OSA, such as fat infiltration, fibrosis, and mucous gland hypertrophy. SyMRI parameters hold promise as the image biomarker, providing valuable information for differentiating various OSA subtypes and thereby promoting personalized treatment. obstructive sleep apnea synthetic MRI histopathology subtype Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Obstructive sleep apnea (OSA) causes repeated upper airway collapse during sleep, resulting in hypopnea or apnea [ 1 ]. Major clinical manifestations include daytime sleepiness (somnolence), nocturnal snoring, witnessed breathing pauses and nocturia [ 2 ]. OSA is highly prevalent worldwide and associated with severe comorbidities such as type 2 diabetes and cardiovascular and cerebrovascular diseases [ 1 – 3 ]. However, its underlying mechanisms are not completely understood. Previous studies have documented pathological changes upper airway tissues, including fat infiltration, muscle fiber type alterations, mucous gland hypertrophy, vascular dilation, lamina propria edema, and squamous metaplasia [ 4 – 7 ]. These pathological changes likely reflect an adaptive response to chronic obstruction. Understanding such structural and cellular alterations could provide valuable insight into the progression of OSA, potentially guiding the development of targeted treatments that address the root pathophysiological changes rather than symptoms alone. However, pathological examination is invasive, presenting challenges for sample collection. There is a clear need for methods that provide precise pathophysiologic information through non-invasive means. Magnetic resonance imaging (MRI) is a precise noninvasive technique showing qualitative and quantitative status of tissue. Recently, Synthetic MRI (SyMRI) has been proved useful in the qualification of intrinsic properties in tissues, utilizing multiple dynamic multi-echo (MDME) sequences to quantify relaxation data of multi-contrast images and synthesis contrast-weighted images [ 8 , 9 ]. SyMRI could generate longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) maps simultaneously, and provide inherent property information of tissues [ 8 , 9 ]. Previous studies have shown that T1 and T2 could reflect brain tissue maturation in infants [ 10 ]. To date, SyMRI has been applied to breast, prostate, brain and bladder, producing accurate tissue changes with quantitative maps for clinical diagnosis differentiation [ 9 , 11 ]. Meanwhile, the iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) sequence can effectively separate fat and water, generating fat fraction, water fraction and R2* images simultaneously, which can measure fat content in tissues [ 12 , 13 ]. Our hypothesis is that SyMRI could provide a wealth of quantitative data on the pathophysiological alterations in the upper airway tissues of patients with OSA. In this study, we applied SyMRI (MAGnetic resonance imaging Compilation [MAGiC] sequence) to the regions of soft palate and tongue in patients with OSA and non-OSA participants. The aim of this study is to assess the effectiveness of SyMRI as an valuable tool for quantifying intrinsic properties alterations from the pathophysiological perspective. Materials and Methods 1.1. Ethical approval The Clinical Medical Research Ethics Committee approved the research. All the subjects participating in our study had signed the written informed consent. 2.2. Study participants This was a cross-sectional study with prospective data collected from a single centre. Eligible healthy participants without OSA and participants with OSA were enrolled from the Otolaryngology Head and Neck Inpatient and Outpatient Department between July, 2023 and Monday, 2024. All participants underwent MRI of the upper airway and sleep monitoring. (For the details of inclusion criteria, see the Supplementary Material). 2.3. Sleep Study A type 3 portable monitor (BMC YH-600B) and in-laboratory polysomnography (PSG) (Compumedics ProFusion PSG V4.5, Australia) were used to diagnose OSA, following standard procedures [14]. All sleep studies were performed by an experienced sleep technologist and reported by a sleep physician according to the guidelines of the American Academy of Sleep Medicine v2.4 [14]. (For further details, see the Supplementary Material). 2.4. Magnetic resonance imaging The upper airway MRI experiment was performed according to standardized imaging protocol in the 3.0 Tesla MRI scanner (SIGNA Architect, GE Healthcare, Milwaukee, Wisconsin, USA) with a 48-channel head coil. Scanning sequences contained three-dimensional (3D) sagittal IDEAL-IQ, 3D sagittal T1-weighted cube, 2D sagittal MAGiC and 3D axial T1-weighted cube (see Table S1 for scanning parameters). 2.5. MR analysis The quantification measurements of the fat fraction and intrinsic properties of the soft palate and tongue were performed with fat fraction map and quantitative (T1, T2, PD) maps acquired from IDEAL-IQ and MAGiC sequences respectively, using a 64-bit Advantage workstation and FuncTool 6.3.1 software (Version 4.7, GE Healthcare, Milwaukee, Wisconsin, USA). The number and thickness of the slices were matched between MAGiC and IDEAL-IQ. Two operators who analyze the MR data were blinded to the demographic characters and laboratory indexes of all the participants. (more details in the Supplementary Material). 2.6. Clinical Sample Collection Soft palate tissue samples were obtained from OSA patients undergoing uvulopalatopharyngoplasty and from non-OSA participants undergoing tonsillectomy and palatopharyngoplasty. Samples were immediately fixed in formalin for histopathological analysis, including masson, hematoxylin-eosin and oil red o staining. (Additional details are provided in the Supplementary Material). 2.7. Statistical analysis According to the previous studies [4,15], the sample size of this study was calculated using PASS software (version 13) on the basis of the following parameters: (a) level of significance: 2-sided test at α=0.05; (b) power (1-β): 90%; (c) effect size (mean of patients: 0.31, mean of control: 0.27); (d) standard deviation (patient: 0.07, control: 0.06).The estimated sample size was 81 patients. Statistical analysis was conducted using SPSS (version 26.0, IBM Corporation). The Shapiro–Wilk test was used to estimate the normality of distribution of continuous variables. Statistical differences between the OSA and non-OSA groups were evaluated with the independent samples t-test or Mann–Whitney tests as appropriate and chi-squared tests for sex. The association between AHI, fat fraction, T1, T2 and PD values were tested with Spearman correlations and linear regression (adjusting for age, sex and BMI). The intra- and inter-rater reliability of the measurement of fat fraction and MAGiC values of the tongue and soft palate were evaluated using the Intra-class Correlation Coefficient (ICC). Diagnostic effectiveness of each parameter was evaluated by receiver operating characteristic (ROC). Continuous variables and categorical variables were presented as the means ± standard deviation and percentages, respectively. A p-value < 0.05 was considered statistically significant and it was two-sided. Diagrams were drawn using GraphPad Prism 9 software (GraphPad Software Inc., San Diego, CA, USA). Results 2.1. Characteristics of participants Between July, 2023 and Monday, 2024, 153 participants were enrolled in this study. Of these, 125 (82 OSA and 43 non-OSA) completed the MRI examination. 15 participants could not tolerate the MRI test, and 13 had dentures, which impaired image analysis on MRI (Figure 1). As shown in Table 1, no significant differences were found in sex, BMI and age across the groups. 2.2. Quantitative analysis of T1, T2 and PD values of the soft palate and tongue The T1, T2 and PD values of the soft palate and tongue are listed in Table S2. The T1 and PD values of the soft palate and T1 value of tongue did not significantly vary between the OSA and non-OSA groups; Participants with OSA had higher T2 and PD values of the tongue than non-OSA participants. T2 value of soft palate was significantly different between OSA and non-OSA participants (Figure 2). 2.3. Annotation of SyMRI Parameters Through IDEAL-IQ sequence and histopathology, we explored the intrinsic properties alterations of upper airway tissues quantifying by SyMRI parameters. As shown in Table 2, fat fraction of the soft palate and tongue correlated with T1 (r=-0.345, p<0.001; r=-0.380, p<0.001, respectively), T2 (r=0.574, p<0.001; r=0.696, p<0.001, respectively) and PD (r=0.248, p=0.005; r=0.516, p<0.001, respectively) values. As demonstrated in Figure 3A to 3D, OSA patients exhibited an increase in fibrosis and mucous gland area within the soft palate, along with a larger fat area compared to non-OSA participants. We then investigated the relationship between the SyMRI parameters and these pathological changes. A positive correlation was observed between the T2 value and the area of fat, mucous gland and fibrosis in soft palate (r=0.761, p=0.002; r=0.731, p=0.003; r=0.654, p=0.011, respectively) (Figure 4). 2.4. Diagnostic effectiveness of T2 and PD value of tongue in differentiating OSA from non-OSA In Table 3, AHI had independent relationship with T2 and PD values of tongue, indicating that these parameters could serve as the image biomarker of OSA. The area under curve (AUC) values of T2 and PD of tongue were 0.756 (95%CI: 0.665-0.846) (Figure 5). 2.5. The intra- and inter-rater reliability The intra- and inter-rater reliability of the measurement of fat content and T1, T2 and PD values of the tongue and soft palate were confirmed, showing good or excellent reliability (Table S3). Discussion Although SyMRI has been an established tool to quantify inherent properties of tissues for over a decade, to our knowledge, it has not previously been applied to obstructive sleep apnea (OSA). In this study, we showed that SyMRI was an effective method for quantifying intrinsic properties alterations of upper airway tissues in patients with OSA, such as fat infiltration, fibrosis and mucous gland hypertrophy. Moreover, SyMRI parameters could act as image biomarker, which provide more information to explore potential pathophysiological mechanisms and diagnosis of various OSA subtypes. SyMRI could capture essential quantitative parameters within a shorter scan time, producing uniform voxel geometries, which improves image accuracy and reproducibility. Previous studies have shown that quantitative longitudinal relaxation time (T1), transverse relaxation time (T2) and proton density (PD) values derived from SyMRI accurately quantify the pathophysiological changes of various tissues [8-11]. In our study, the T2 and PD values of the tongue and T2 value of soft palate demonstrated a significant increase in patients with OSA, compared to those in non-OSA participants, indicating that intrinsic property of tongue in patients with OSA had changed. To explore the exact inherent alterations reflected by SyMRI parameters, we investigated the underlying biological connections by the IDEAL-IQ MRI sequence and histopathology to indicate fat infiltration and pathophysiological changes of these tissues. IDEAL-IQ has been reported to precisely quantify fat fraction in various organs and tissues, such as the liver, lumbar spine, pancreas, and kidneys [16,17].The correlation analysis between SyRMI and IDEAL-IQ parameters in our study suggest that SyMRI could reflect fat content in soft palate and tongue, which was consistent with previous study [18]. Histopathological staining of the soft palate confirmed that fat areas were indeed larger in OSA patients than in non-OSA participants, with OSA patients also exhibiting increased mucous gland and fibrosis areas, consistent with previous findings [4-7]. Further analyses showed that T2 value of the soft palate corresponded to these anatomical alterations (Figure 4A-D). Therefore, SyMRI could quantify pathophysiological alterations of upper airway tissues in OSA by a non-invasive way, providing valuable insight into the progression of OSA. However, while SyMRI yields valuable quantitative data such as T1, T2 and PD, it lacks more detailed parameters to reflect intrinsic properties in tissues comprehensively. SyMRI-based radiomic could serve as a valuable tool to provide high-dimensional data, detecting subtle pathophysiological changes of tissues [19]. Previous studies have demonstrated that nonanatomical factors (PALM model) are also crucial contributors to OSA, leading to various subtypes of OSA [20]. Personalized treatment method could significantly improve patient outcomes. However, the current detection methods of nonanatomical phenotype are not applicable in clinical settings due to the complex procedures and discomfort [21]. In our study, T2 and PD values of tongue were positive associated with AHI (Table 3)and had statistically significant diagnostic effectiveness in differentiating OSA from non-OSA (Figure 5). Therefore, SyMRI not only has the potential to provide more image information for identifying OSA subtypes but also boasts significant advantages owing to its convenience and non-invasive nature. Our study has several limitations. First, it was a single-center study with a limited sample size and lacked external validation. This limitation arises because SyMRI is not yet a standard MRI sequence for OSA, requiring prospective data collection. To further validate the stability and repeatability of current findings in our study, a multi-center prospective research is needed. Second, the sleep monitor contained in-laboratory polysomnography and out-of-centre sleep testing (OCST). Although OCST often underestimates frequency owing to recording time, it can also be utilised to estimate the apnea-hypopnea index [35]. Meanwhile, aiming at this issue, we adopted the same strategy of scoring time for PSG and OCST [36]. However, to further reduce this bias, we will enrol more participants who undergo PSG to verify our current results. In conclusion, SyMRI could act as an effective noninvasive method to quantify inherent alterations of upper airway tissues in OSA, such as fat infiltration, fibrosis and mucous gland hypertrophy. Unlike qualitative or semi-quantitative assessment methods, SyMRI technology can provide a more precise and objective evaluation of the upper airway tissues in OSA patients, which may help to better understand the pathophysiological mechanisms of OSA and may offer a new perspective for the selection of treatment strategies and the assessment of their effectiveness. Moreover, SyMRI parameters hold promise as the image biomarker to provide valuable information for differentiating various OSA subtypes, promoting personalized treatment. Declarations 1, Ethics approval and consent to participate: The Clinical Medical Research Ethics Committee at the Third Affiliated hospital of Sun Yat-sen University approved the research (ZhongDaFuSanYiLunII2023-178-02). All the subjects participating in our study had signed the written informed consent . 2, Consent for publication: Not applicable. 3, Availability of data and materials : The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 4, Competing interest: The authors declare that they have no competing interests. 5, Funding: This work was supported by the National Key R&D program of China (2023YFF0724200 to G.S.); Science and Technology Program of Guangzhou (2019030110024 to J.Y.); National Natural Science Foundation of China (82070811 to G.S.); Guangzhou Municipal Science and Technology Project (202201020497 and 2024A01J6567 to G.S.); Guangdong Basic and Applied Research Foundation (2024A1515012501 to G.S.). 6, Author Contribution: Conceptualization: JC, JY, ZK. Data curation: GS. Formal analysis: WXL. Funding acquisition: JY. Methodology: JC, PL. Project administration: ZYW, TW. Writing-original draft: JC, YCK. Writing-review & editing: JC, JY, ZK. 7, Acknowledge: The authors thank Jialu Zhang of GE Healthcare for her invaluable advice on describing the synthetic MRI technique used in this article and all of the participants enrolled in this trial and technicians in the sleep laboratory of Otolaryngology Head and Neck Surgery departments. 8,Clinical trial number : not applicable. References Dempsey JA, Veasey SC, Morgan BJ, O’Donnell CP. Pathophysiology of sleep apnea. Physiol Rev. 2010;90:47–112. Daniel J, Gottlieb, Naresh M, Punjabi. Diagnosis and Management of Obstructive Sleep Apnea A Review. JAMA. 2020;323:1389–400. Javaheri S, Barbe F, Campos-Rodriguez F, Dempsey JA, Khayat R, Javaheri S, et al. Sleep apnea: types, mechanisms, and clinical cardiovascular consequences. J Am Coll Cardiol. 2017;69:841–58. Kim AM, Keenan BT, Jackson N, Eugenia L, Chan B, Staley H, Poptani, et al. Tongue fat and its relationship to obstructive sleep apnea. Sleep. 2014;37:1639–48. Sériès FJ, Simoneau SA, St Pierre S, Marc I. Characteristics of the genioglossus and musculus uvulae in sleep apnea hypopnea syndrome and in snorers. Am J Respir Crit Care Med. 1996;153(6 Pt 1):1870–4. Cori JM, O'Donoghue FJ, Jordan AS. Sleeping tongue: current perspectives of genioglossus control in healthy individuals and patients with obstructive sleep apnea. Nat Sci Sleep. 2018;15:10:169–79. Woodson BT, Garancis JC, Toohill RJ. Histopathologic changes in snoring and obstructive sleep apnea syndrome. Laryngoscope. 1991;101(12 Pt 1):1318–22. Ji S, Yang D, Lee J, Choi SH, Kim H, Kang KM. Synthetic MRI: Technologies and applications in neuroradiology. J Magn Reson Imaging. 2022;55:1013–25. Cui Y, Han S, Liu M, Wu PY, Zhang W, Zhang JT, et al. Diagnosis and grading of prostate cancer by relaxation maps from synthetic MRI. J Magn Reson Imaging. 2020;52:552–64. Vanderhasselt T, Zolfaghari R, Naeyaert M, Dudink J, Buls N, Allemeersch G, et al. Synthetic MRI demonstrates prolonged regional relaxation times in the brain of preterm born neonates with severe postnatal morbidity. Neuroimage Clin. 2021;29:102544. Matsuda M, Tsuda T, Ebihara R, Toshimori W, Takeda S, Okada K, et al. Enhanced masses on contrast-enhanced breast: differentiation using a combination of dynamic contrast-enhanced MRI and quantitative evaluation with synthetic MRI. J Magn Reson Imaging. 2021;53:381–91. Kim H, Taksali SE, Dufour S, Douglas Befroy TR, Goodman KF, Petersen, et al. Comparative MR study of hepatic fat quantification using single-voxel proton spectroscopy, two-point dixon and three-point IDEAL. Magn Reson Med. 2008;59:521–7. Aoki T, Yamaguchi S, Kinoshita S, Hayashida Y, Korogi Y. Quantification of bone marrow fat content using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): reproducibility, site variation and correlation with age and menopause. Br J Radiol. 2016;89:20150538. Berry RB, Brooks R, Gamaldo C, Harding SM, Lloyd RM, Quan SF, et al. AASM Scoring Manual Updates for 2017 (Version 2.4). J Clin Sleep Med. 2017;13:665–6. Yu JL, Wiemken A, Schultz SM, Keenan BT, Sehgal CM, Schwab RJ. (2022) A comparison of ultrasound echo intensity to magnetic resonance imaging as a metric for tongue fat evaluation 2022;45:zsab295. Idilman IS, Aniktar H, Idilman R, Kabacam G, Savas B, Elhan A, et al. Quantification of liver, pancreas, kidney, and vertebral body MRI-PDFF in nonalcoholic fatty liver disease. Abdom Imaging. 2015;40:1512–9. Shen N, Li XY, Zheng S, Zhang L, Fu Y, Liu XM, et al. Automated and accurate quantification of subcutaneous and visceral adipose tissue from magnetic resonance imaging based on machine learning. Magn Reson Imaging. 2019;64:28–36. Zhang K, Liu C, Zhu Y, Li W, Li X, Zheng J, Hong G. Synthetic MRI in the detection and quantitative evaluation of sacroiliac joint lesions in axial spondyloarthritis. Front Immunol. 2022;13:1000314. Hwang KP, Elshafeey NA, Kotrotsou A, Chen H, Son JB, Boge M, et al. A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer. Radiol Imaging Cancer. 2023;5(4):e230009. Eckert DJ, White DP, Jordan AS, Malhotra A, Wellman A. Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets[J]. Am J Respir Crit Care Med. 2013;188(8):996–1004. Carberry JC, Amatoury J, Eckert DJ. Personalized Management Approach for OSA. Chest. 2018;153(3):744–55. Tables Table 1 Comparison of characteristics in OSA and non-OSA. non-OSA OSA Characteristic Overall (AHI<5) (AHI ≥ 5) p Sample Size,N 125 43 82 - Age,years 35.79 ± 7.60 34.12 ± 8.01 36.67 ± 7.264 0.74 BMI,kg/㎡ 25.58 ± 2.23 25.10 ± 1.94 25.82 ± 2.34 0.86 Male,n (%) 96(77.6) 29 (67.4) 67 (81.7) 0.73 AHI,events/h 23.3 ± 22.7 2.5 ± 1.2 34.3 ± 20.9 < 0.001* Study type,n (%) OCST 49 (39.2) 22 (51.2) 27 (32.9) PSG 76 (60.8) 21 (48.8) 55 (67.1) AHI = apnea-hypopnea index; OCST = out of center sleep testing; PSG = polysomnogram; OSA = obstructive sleep apnea. Variables are presented as mean± standard deviation. categorical variables were summarized with percentages. Statistical differences between the two groups were assessed using the Mann Whitney test. Chi-squared tests for Male. *P < 0.05. Table 2 Relationship between fat content and T1/PD value of soft palate and tongue. T1 (ms) T2 (ms) PD (pu) r (95%CI) P r (95%CI) P r (95%CI) P Soft palate FF (%) −0.345 (−0.491,−0.181) < 0.001* 0.574 (0.443,0.681) < 0.001* 0.248 (0.076,0.406) 0.005* Tongue FF (%) −0.380 (−0.521,−0.219) < 0.001* 0.696 (0.592,0.776) < 0.001* 0.516 (0.375,0.635) < 0.001* FF = fat fraction; FV = fat volume. Adjusting for age, BMI and gender, Pearson was used to assess these relationship. *P < 0.05. Table 3 Relationship between AHI and fat content and MAGiC values of soft palate and tongue. AHI (events/h) B 95%CI P Soft palate T2 (ms) 0.742 −0.770,1.560 0.760 Tongue T2 (ms) 1.007 0.249,1.764 0.010* PD (pu) 1.475 0.426,2.524 0.006* FP = fat percentage; TV = total volume; FV = fat volume; AHI = apnea-hypopnea index. PD = proton density. Adjusting for age, BMI and gender, linear regression was used to assess these relationship. *P < 0.05. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.doc Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Jun, 2025 Reviews received at journal 25 May, 2025 Reviews received at journal 22 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers invited by journal 05 May, 2025 Editor invited by journal 09 Apr, 2025 Editor assigned by journal 08 Apr, 2025 Submission checks completed at journal 08 Apr, 2025 First submitted to journal 05 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6379963","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453364736,"identity":"0d57cd68-d12d-4b48-a0f3-017587cc2672","order_by":0,"name":"Jia Chen","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Chen","suffix":""},{"id":453364737,"identity":"c199aca5-6414-4abc-ac9c-90ca8d96998a","order_by":1,"name":"Ge Si","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Ge","middleName":"","lastName":"Si","suffix":""},{"id":453364738,"identity":"195f663d-7323-4d76-8691-03898c025ae7","order_by":2,"name":"Shiyi Wen","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Shiyi","middleName":"","lastName":"Wen","suffix":""},{"id":453364739,"identity":"b6545a5a-441a-4199-9248-0ec13094c88c","order_by":3,"name":"Zhiyuan Wang","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Wang","suffix":""},{"id":453364740,"identity":"80861b75-02fb-4eb6-8e52-900b43369eb4","order_by":4,"name":"Pei Li","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Pei","middleName":"","lastName":"Li","suffix":""},{"id":453364741,"identity":"63b2fc9f-4090-43c4-a139-87377c7e9be8","order_by":5,"name":"Yuchen Kang","email":"","orcid":"","institution":"The College of Liberal Arts and Sciences of Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Yuchen","middleName":"","lastName":"Kang","suffix":""},{"id":453364742,"identity":"c0080e7e-a014-4cd9-adfa-f27c3420f4c8","order_by":6,"name":"Weixing Liu","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Weixing","middleName":"","lastName":"Liu","suffix":""},{"id":453364743,"identity":"4394de69-6afa-4805-869b-4250123ea038","order_by":7,"name":"Tao Wang","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Wang","suffix":""},{"id":453364744,"identity":"df0e2094-2742-4958-af17-cb5941952207","order_by":8,"name":"Guojun Shi","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Guojun","middleName":"","lastName":"Shi","suffix":""},{"id":453364745,"identity":"5f49cec8-25a5-4064-893c-c1a771014479","order_by":9,"name":"Zhuang Kang","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zhuang","middleName":"","lastName":"Kang","suffix":""},{"id":453364746,"identity":"0668c481-6c1a-4f7d-993d-5a23c45ad07b","order_by":10,"name":"Jin Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHCChAMMFQwMbOwIAWK0nAFqYSZBCwMDYxuQIFqLwfGGh4cL522T52NmYP7M8+cwAz97jgHDzx14tJw5kHB45rbbhm3MDGzSvG2HGSR73hgw9p7Bo+VGQsJh3m23GUFamHkbDgNFcgyYwU7FpeX+A6CWObft22AOsyeo5QYDUEvD7USgFgZpHjagLRIEtEieATqM59jt5DagMsm5bek8EmeeFRzsxaOF7/iZ5M88Nbdt57c3H/7w5o+1HH978sYHP/FoUTjAkwBlMjYw8TAw8ICYB3BrYGCQb2BHyDP+wKd0FIyCUTAKRiwAAIKvUcft43uCAAAAAElFTkSuQmCC","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2025-04-05 05:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6379963/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6379963/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82358338,"identity":"47433c9c-f216-4da0-ac79-dfb6a0ad8cbc","added_by":"auto","created_at":"2025-05-09 11:23:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1068888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the participant selection process\u003c/strong\u003e. OSA, obstructive sleep apnea; AHI, apnea-hypopnea index; MRI, magnetic resonance imaging. OSA = obstructive sleep apnea; AHI = apnea hypopnea index; MRI = magnetic resonance imaging.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6379963/v1/10e348ad93da92676aa9149b.png"},{"id":82355865,"identity":"dcb6dc3e-cdfb-4775-8616-6afba3bcf3da","added_by":"auto","created_at":"2025-05-09 11:15:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19197274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustration of synthetic MRI on OSA patients\u003c/strong\u003e. (A) Synthetic MRI of one OSA patient and one non-OSA participant. (B) Comparison of SyMRI parameters between OSA patients and non-OSA participants. White arrow points tongue, black arrow points soft palate. ns = not significant. *P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6379963/v1/c93a4deb6d356733c3a662ed.png"},{"id":82358339,"identity":"8edb4ce5-2d3a-44a4-a902-f9db04a10da7","added_by":"auto","created_at":"2025-05-09 11:23:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10485018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathological staining of soft palate.\u003c/strong\u003e The fat area, mucous gland area, and fibrosis area in the soft palate are larger in OSA participants compared to non-OSA participants (A-D). H\u0026amp;E = hematoxylin and eosin staining; ns = not significant. *P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6379963/v1/082c1e3c044ba62d22e4b3a4.png"},{"id":82355848,"identity":"d6fa58a2-f442-4be4-a6b9-a219cd7f07ab","added_by":"auto","created_at":"2025-05-09 11:15:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1512168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlations between SyMRI parameters and alterations in fat, mucous gland, mucous gland and muscle areas in the soft palate\u003c/strong\u003e. T2 value had positive correlation with fat area, mucous gland area and fibrosis area (A-D). *P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6379963/v1/aa3754ef5352eed0a987e41f.png"},{"id":82355847,"identity":"ccbe3464-11dd-4357-89a3-92bf6c444933","added_by":"auto","created_at":"2025-05-09 11:15:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":281003,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic efficacy of SyMRI parameters\u003c/strong\u003e. The area under curve (AUC) values of T2 and PD of tongue were 0.756 (95%CI: 0.665-0.846).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6379963/v1/0ab7bd2fc32d615d72f1eea8.png"},{"id":82360833,"identity":"00640f8b-a9e5-4f95-94fc-fd37fbe70ea3","added_by":"auto","created_at":"2025-05-09 11:40:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31743977,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6379963/v1/02bfb14c-ae08-47c8-9cd5-7791df984b43.pdf"},{"id":82358342,"identity":"65eef124-d079-4cc1-83ef-067529610d86","added_by":"auto","created_at":"2025-05-09 11:23:55","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17876992,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-6379963/v1/c63dd7489b81998ddb7b10fe.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Synthetic MRI for Quantitative Assessment of Intrinsic Alterations in Upper Airway Tissues in Obstructive Sleep Apnea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObstructive sleep apnea (OSA) causes repeated upper airway collapse during sleep, resulting in hypopnea or apnea [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Major clinical manifestations include daytime sleepiness (somnolence), nocturnal snoring, witnessed breathing pauses and nocturia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. OSA is highly prevalent worldwide and associated with severe comorbidities such as type 2 diabetes and cardiovascular and cerebrovascular diseases [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, its underlying mechanisms are not completely understood.\u003c/p\u003e \u003cp\u003ePrevious studies have documented pathological changes upper airway tissues, including fat infiltration, muscle fiber type alterations, mucous gland hypertrophy, vascular dilation, lamina propria edema, and squamous metaplasia [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These pathological changes likely reflect an adaptive response to chronic obstruction. Understanding such structural and cellular alterations could provide valuable insight into the progression of OSA, potentially guiding the development of targeted treatments that address the root pathophysiological changes rather than symptoms alone. However, pathological examination is invasive, presenting challenges for sample collection. There is a clear need for methods that provide precise pathophysiologic information through non-invasive means.\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging (MRI) is a precise noninvasive technique showing qualitative and quantitative status of tissue. Recently, Synthetic MRI (SyMRI) has been proved useful in the qualification of intrinsic properties in tissues, utilizing multiple dynamic multi-echo (MDME) sequences to quantify relaxation data of multi-contrast images and synthesis contrast-weighted images [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. SyMRI could generate longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) maps simultaneously, and provide inherent property information of tissues [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous studies have shown that T1 and T2 could reflect brain tissue maturation in infants [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To date, SyMRI has been applied to breast, prostate, brain and bladder, producing accurate tissue changes with quantitative maps for clinical diagnosis differentiation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Meanwhile, the iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) sequence can effectively separate fat and water, generating fat fraction, water fraction and R2* images simultaneously, which can measure fat content in tissues [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our hypothesis is that SyMRI could provide a wealth of quantitative data on the pathophysiological alterations in the upper airway tissues of patients with OSA.\u003c/p\u003e \u003cp\u003e In this study, we applied SyMRI (MAGnetic resonance imaging Compilation [MAGiC] sequence) to the regions of soft palate and tongue in patients with OSA and non-OSA participants. The aim of this study is to assess the effectiveness of SyMRI as an valuable tool for quantifying intrinsic properties alterations from the pathophysiological perspective.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1. Ethical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Clinical Medical Research Ethics Committee approved the research. All the subjects participating in our study had signed the written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStudy participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a cross-sectional study with prospective data collected from a single centre. Eligible healthy participants without OSA and participants with OSA were enrolled from the Otolaryngology Head and Neck Inpatient and Outpatient Department between July, 2023 and Monday, 2024. All participants underwent MRI of the upper airway and sleep monitoring. (For the details of inclusion criteria, see the Supplementary Material).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSleep Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA type 3 portable monitor (BMC YH-600B) and in-laboratory polysomnography (PSG) (Compumedics ProFusion PSG V4.5, Australia) were used to diagnose OSA, following standard procedures [14]. All sleep studies were performed by an experienced sleep technologist and reported by a sleep physician according to the guidelines of the American Academy of Sleep Medicine v2.4 [14]. (For further details, see the Supplementary Material).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMagnetic resonance imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe upper airway MRI experiment was performed according to standardized imaging protocol in the 3.0 Tesla MRI scanner (SIGNA Architect, GE Healthcare, Milwaukee, Wisconsin, USA) with a 48-channel head coil. Scanning sequences contained three-dimensional (3D) sagittal IDEAL-IQ, 3D sagittal T1-weighted cube, 2D sagittal MAGiC and 3D axial T1-weighted cube (see Table S1 for scanning parameters).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantification measurements of the fat fraction and intrinsic properties of the soft palate and tongue were performed with fat fraction map and quantitative (T1, T2, PD) maps acquired from IDEAL-IQ and MAGiC sequences respectively, using a 64-bit Advantage workstation and FuncTool 6.3.1 software (Version 4.7, GE Healthcare, Milwaukee, Wisconsin, USA). The number and thickness of the slices were matched between MAGiC and IDEAL-IQ. Two operators who analyze the MR data were blinded to the demographic characters and laboratory indexes of all the participants. (more details in the Supplementary Material).\u003c/p\u003e\n\u003cp\u003e2.6.\u0026nbsp;\u003cstrong\u003eClinical Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoft palate tissue samples were obtained from OSA patients undergoing uvulopalatopharyngoplasty and from non-OSA participants undergoing tonsillectomy and palatopharyngoplasty. Samples were immediately fixed in formalin for histopathological analysis, including masson, hematoxylin-eosin and oil red o staining. (Additional details are provided in the Supplementary Material).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the previous studies [4,15], the sample size of this study was calculated using PASS software (version 13) on the basis of the following parameters: (a) level of significance: 2-sided test at α=0.05; (b) power (1-β): 90%; (c) effect size (mean of patients: 0.31, mean of control: 0.27); (d) standard deviation (patient: 0.07, control: 0.06).The estimated sample size was 81 patients. Statistical analysis was conducted using SPSS (version 26.0, IBM Corporation). The Shapiro–Wilk test was used to estimate the normality of distribution of continuous variables. Statistical differences between the OSA and non-OSA groups were evaluated with the independent samples t-test or Mann–Whitney tests as appropriate and chi-squared tests for sex. \u0026nbsp;The association between AHI, fat fraction, T1, T2 and PD values were tested with Spearman correlations and linear regression (adjusting for age, sex and BMI). The intra- and inter-rater reliability of the measurement of fat fraction and MAGiC values of the tongue and soft palate were evaluated using the Intra-class Correlation Coefficient (ICC). Diagnostic effectiveness of each parameter was evaluated by receiver operating characteristic (ROC). Continuous variables and categorical variables were presented as the means ± standard deviation and percentages, respectively. A p-value \u0026lt; 0.05 was considered statistically significant and it was two-sided. Diagrams were drawn using GraphPad Prism 9 software (GraphPad Software Inc., San Diego, CA, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1. Characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween July, 2023 and Monday, 2024, 153 participants were enrolled in this study. Of these, 125 (82 OSA and 43 non-OSA) completed the MRI examination. 15 participants could not tolerate the MRI test, and 13 had dentures, which impaired image analysis on MRI (Figure 1). As shown in Table 1, no significant differences were found in sex, BMI and age across the groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Quantitative analysis of T1, T2 and PD values of the soft palate and tongue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe T1, T2 and PD values of the soft palate and tongue are listed in Table S2. The T1 and PD values of the soft palate and T1 value of tongue did not significantly vary between the OSA and non-OSA groups; Participants with OSA had higher T2 and PD values of the tongue than non-OSA participants. T2 value of soft palate was \u0026nbsp;significantly different between OSA and non-OSA participants (Figure 2).\u003c/p\u003e\n\u003cp\u003e2.3. \u003cstrong\u003eAnnotation of SyMRI Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough IDEAL-IQ sequence and histopathology, we explored the intrinsic properties alterations of upper airway tissues quantifying by SyMRI parameters.\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, fat fraction of the soft palate and tongue correlated with T1 (r=-0.345, p\u0026lt;0.001; r=-0.380, p\u0026lt;0.001, respectively), T2 (r=0.574, p\u0026lt;0.001; r=0.696, p\u0026lt;0.001, respectively) and PD (r=0.248, p=0.005; r=0.516, p\u0026lt;0.001, respectively) values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs demonstrated in Figure 3A to 3D, OSA patients exhibited an increase in fibrosis and mucous gland area within the soft palate, along with a larger fat area compared to non-OSA participants. We then investigated the relationship between the SyMRI parameters and these pathological changes. A positive correlation was observed between the T2 value and the area of fat, mucous gland and fibrosis in soft palate (r=0.761, p=0.002; r=0.731, p=0.003; r=0.654, p=0.011, respectively) (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Diagnostic effectiveness of T2 and PD value of tongue in differentiating OSA from non-OSA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Table 3, AHI had independent relationship with T2 and PD values of tongue, indicating that these parameters could serve\u0026nbsp;as the image biomarker of OSA. The area under curve (AUC) values of T2 and PD of tongue were 0.756 (95%CI: 0.665-0.846) (Figure 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe intra- and inter-rater reliability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intra- and inter-rater reliability of the measurement of fat content and T1, T2 and PD values of the tongue and soft palate were confirmed, showing good or excellent reliability (Table S3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough\u0026nbsp;SyMRI has been an established tool to quantify inherent properties of tissues for over a decade, to our knowledge, it has not previously been applied to obstructive sleep apnea (OSA). In this study, we showed that SyMRI was an effective method for quantifying intrinsic properties alterations of upper airway tissues in patients with OSA, such as fat infiltration, fibrosis and mucous gland hypertrophy. Moreover, SyMRI parameters could act as image biomarker, which provide more information to explore potential pathophysiological mechanisms and diagnosis of various OSA subtypes.\u003c/p\u003e\n\u003cp\u003eSyMRI could capture essential quantitative parameters within a shorter scan time, producing uniform voxel geometries, which improves image accuracy and reproducibility. Previous studies have shown that quantitative longitudinal relaxation time (T1), transverse relaxation time (T2) and proton density (PD) values derived from SyMRI accurately quantify the pathophysiological changes of various tissues [8-11]. In our study, the T2 and PD values of the tongue and T2 value of soft palate demonstrated a significant increase in patients with OSA, compared to those in non-OSA participants, indicating that intrinsic property of tongue in patients with OSA had changed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo explore the exact inherent alterations reflected by SyMRI parameters, we investigated the underlying biological connections by the IDEAL-IQ MRI sequence and histopathology to indicate fat infiltration and pathophysiological changes of these tissues. IDEAL-IQ has been reported to precisely quantify fat fraction in various organs and tissues, such as the liver, lumbar spine, pancreas, and kidneys [16,17].The correlation analysis between SyRMI and IDEAL-IQ parameters in our study suggest that SyMRI could reflect fat content in soft palate and tongue, which was consistent with previous study [18]. Histopathological staining of the soft palate confirmed that fat areas were indeed larger in OSA patients than in non-OSA participants, with OSA patients also exhibiting increased mucous gland and fibrosis areas, consistent with previous findings [4-7]. Further analyses showed that T2 value of the soft palate corresponded to these anatomical alterations (Figure 4A-D). Therefore, SyMRI could quantify pathophysiological alterations of upper airway tissues in OSA by a non-invasive way, providing valuable insight into the progression of OSA. However, while SyMRI yields valuable quantitative data such as T1, T2 and PD, it lacks more detailed parameters to reflect intrinsic properties in tissues comprehensively. SyMRI-based radiomic could serve as a valuable tool to provide high-dimensional data, detecting subtle pathophysiological changes of tissues [19].\u003c/p\u003e\n\u003cp\u003ePrevious studies have demonstrated that nonanatomical factors (PALM model) are also crucial contributors to OSA, leading to various subtypes of OSA [20]. Personalized treatment method could significantly improve patient outcomes. However, the current detection methods of nonanatomical phenotype are not applicable in clinical settings due to the complex procedures and discomfort [21]. In our study, T2 and PD values of tongue were positive associated with AHI (Table 3)and had statistically significant diagnostic effectiveness in differentiating OSA from non-OSA (Figure 5). Therefore, SyMRI not only has the potential to provide more image information for identifying OSA subtypes but also boasts significant advantages owing to its convenience and non-invasive nature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study has several limitations. First, it was a single-center study with a limited sample size and lacked external validation. This limitation arises because SyMRI is not yet a standard MRI sequence for OSA, requiring prospective data collection. To further validate the stability and repeatability of current findings in our study, a multi-center prospective research is needed. Second, the sleep monitor contained in-laboratory polysomnography and out-of-centre sleep testing (OCST). Although OCST often underestimates frequency owing to recording time, it can also be utilised to estimate the apnea-hypopnea index [35]. Meanwhile, aiming at this issue, we adopted the same strategy of scoring time for PSG and OCST [36]. However, to further reduce this bias, we will enrol more participants who undergo PSG to verify our current results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, SyMRI could act as an effective noninvasive method to quantify inherent alterations of upper airway tissues in OSA, such as fat infiltration, fibrosis and mucous gland hypertrophy. Unlike qualitative or semi-quantitative assessment methods, SyMRI technology can provide a more precise and objective evaluation of the upper airway tissues in OSA patients, which may help to better understand the pathophysiological mechanisms of OSA and may offer a new perspective for the selection of treatment strategies and the assessment of their effectiveness. Moreover, SyMRI parameters hold promise as the image biomarker to provide valuable information for differentiating various OSA subtypes, promoting personalized treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e1, Ethics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe Clinical Medical Research Ethics Committee at the Third Affiliated hospital of Sun Yat-sen University approved the research (ZhongDaFuSanYiLunII2023-178-02). All the subjects participating in our study had signed the written informed consent .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2, Consent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3, Availability of data and materials\u003c/strong\u003e:\u0026nbsp;The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting interest:\u0026nbsp;\u003c/strong\u003eThe authors\u0026nbsp;declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Key R\u0026amp;D program of China (2023YFF0724200 to G.S.); Science and Technology Program of Guangzhou (2019030110024 to J.Y.); National Natural Science Foundation of China (82070811 to G.S.); Guangzhou Municipal Science and Technology Project (202201020497 and 2024A01J6567 to G.S.); Guangdong Basic and Applied Research Foundation (2024A1515012501 to G.S.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6, Author Contribution:\u0026nbsp;\u003c/strong\u003eConceptualization: JC, JY, ZK. Data curation: GS. Formal analysis: WXL. Funding acquisition: JY. Methodology: JC, PL. Project administration: ZYW, TW. Writing-original draft: JC, YCK. Writing-review\u0026nbsp;\u0026amp; editing: JC, JY, ZK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7, Acknowledge:\u0026nbsp;\u003c/strong\u003eThe authors thank Jialu Zhang of GE Healthcare for her invaluable advice on describing the synthetic MRI technique used in this article and all of the participants enrolled in this trial and technicians in the sleep laboratory of Otolaryngology Head and Neck Surgery departments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8,Clinical trial number\u003c/strong\u003e: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDempsey JA, Veasey SC, Morgan BJ, O\u0026rsquo;Donnell CP. Pathophysiology of sleep apnea. Physiol Rev. 2010;90:47\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaniel J, Gottlieb, Naresh M, Punjabi. Diagnosis and Management of Obstructive Sleep Apnea A Review. JAMA. 2020;323:1389\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJavaheri S, Barbe F, Campos-Rodriguez F, Dempsey JA, Khayat R, Javaheri S, et al. Sleep apnea: types, mechanisms, and clinical cardiovascular consequences. J Am Coll Cardiol. 2017;69:841\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim AM, Keenan BT, Jackson N, Eugenia L, Chan B, Staley H, Poptani, et al. Tongue fat and its relationship to obstructive sleep apnea. Sleep. 2014;37:1639\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026eacute;ri\u0026egrave;s FJ, Simoneau SA, St Pierre S, Marc I. Characteristics of the genioglossus and musculus uvulae in sleep apnea hypopnea syndrome and in snorers. Am J Respir Crit Care Med. 1996;153(6 Pt 1):1870\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCori JM, O'Donoghue FJ, Jordan AS. Sleeping tongue: current perspectives of genioglossus control in healthy individuals and patients with obstructive sleep apnea. Nat Sci Sleep. 2018;15:10:169\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoodson BT, Garancis JC, Toohill RJ. Histopathologic changes in snoring and obstructive sleep apnea syndrome. Laryngoscope. 1991;101(12 Pt 1):1318\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi S, Yang D, Lee J, Choi SH, Kim H, Kang KM. Synthetic MRI: Technologies and applications in neuroradiology. J Magn Reson Imaging. 2022;55:1013\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui Y, Han S, Liu M, Wu PY, Zhang W, Zhang JT, et al. Diagnosis and grading of prostate cancer by relaxation maps from synthetic MRI. J Magn Reson Imaging. 2020;52:552\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanderhasselt T, Zolfaghari R, Naeyaert M, Dudink J, Buls N, Allemeersch G, et al. Synthetic MRI demonstrates prolonged regional relaxation times in the brain of preterm born neonates with severe postnatal morbidity. Neuroimage Clin. 2021;29:102544.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuda M, Tsuda T, Ebihara R, Toshimori W, Takeda S, Okada K, et al. Enhanced masses on contrast-enhanced breast: differentiation using a combination of dynamic contrast-enhanced MRI and quantitative evaluation with synthetic MRI. J Magn Reson Imaging. 2021;53:381\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim H, Taksali SE, Dufour S, Douglas Befroy TR, Goodman KF, Petersen, et al. Comparative MR study of hepatic fat quantification using single-voxel proton spectroscopy, two-point dixon and three-point IDEAL. Magn Reson Med. 2008;59:521\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAoki T, Yamaguchi S, Kinoshita S, Hayashida Y, Korogi Y. Quantification of bone marrow fat content using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): reproducibility, site variation and correlation with age and menopause. Br J Radiol. 2016;89:20150538.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerry RB, Brooks R, Gamaldo C, Harding SM, Lloyd RM, Quan SF, et al. AASM Scoring Manual Updates for 2017 (Version 2.4). J Clin Sleep Med. 2017;13:665\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu JL, Wiemken A, Schultz SM, Keenan BT, Sehgal CM, Schwab RJ. (2022) A comparison of ultrasound echo intensity to magnetic resonance imaging as a metric for tongue fat evaluation 2022;45:zsab295.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIdilman IS, Aniktar H, Idilman R, Kabacam G, Savas B, Elhan A, et al. Quantification of liver, pancreas, kidney, and vertebral body MRI-PDFF in nonalcoholic fatty liver disease. Abdom Imaging. 2015;40:1512\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen N, Li XY, Zheng S, Zhang L, Fu Y, Liu XM, et al. Automated and accurate quantification of subcutaneous and visceral adipose tissue from magnetic resonance imaging based on machine learning. Magn Reson Imaging. 2019;64:28\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang K, Liu C, Zhu Y, Li W, Li X, Zheng J, Hong G. Synthetic MRI in the detection and quantitative evaluation of sacroiliac joint lesions in axial spondyloarthritis. Front Immunol. 2022;13:1000314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang KP, Elshafeey NA, Kotrotsou A, Chen H, Son JB, Boge M, et al. A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer. Radiol Imaging Cancer. 2023;5(4):e230009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEckert DJ, White DP, Jordan AS, Malhotra A, Wellman A. Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets[J]. Am J Respir Crit Care Med. 2013;188(8):996\u0026ndash;1004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarberry JC, Amatoury J, Eckert DJ. Personalized Management Approach for OSA. Chest. 2018;153(3):744\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eComparison of characteristics in OSA and non-OSA.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003enon-OSA\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eOSA\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCharacteristic\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOverall\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(AHI\u0026lt;5)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e(AHI\u0026thinsp;\u0026ge;\u0026thinsp;5)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003ep\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSample Size,N\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e125\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e43\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e82\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge,years\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e35.79\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e34.12\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e36.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.264\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.74\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBMI,kg/㎡\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e25.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e25.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e25.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.86\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMale,n (%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e96(77.6)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e29 (67.4)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e67 (81.7)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.73\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAHI,events/h\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;22.7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001*\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eStudy type,n (%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eOCST\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e49 (39.2)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e22 (51.2)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e27 (32.9)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePSG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e76 (60.8)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e21 (48.8)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e55 (67.1)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAHI\u0026thinsp;=\u0026thinsp;apnea-hypopnea index; OCST\u0026thinsp;=\u0026thinsp;out of center sleep testing; PSG\u0026thinsp;=\u0026thinsp;polysomnogram; OSA\u0026thinsp;=\u0026thinsp;obstructive sleep apnea. Variables are presented as mean\u0026plusmn;\u0026thinsp;standard deviation. categorical variables were summarized with percentages. Statistical differences between the two groups were assessed using the Mann Whitney test. Chi-squared tests for Male. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eRelationship between fat content and T1/PD value of soft palate and tongue.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eT1 (ms)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eT2 (ms)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003ePD (pu)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003er (95%CI)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eP\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003er (95%CI)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eP\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003er (95%CI)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eP\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSoft palate\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFF (%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026minus;0.345 (\u0026minus;0.491,\u0026minus;0.181)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.574 (0.443,0.681)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.248 (0.076,0.406)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.005*\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTongue\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFF (%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026minus;0.380 (\u0026minus;0.521,\u0026minus;0.219)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.696 (0.592,0.776)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.516 (0.375,0.635)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001*\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFF\u0026thinsp;=\u0026thinsp;fat fraction; FV\u0026thinsp;=\u0026thinsp;fat volume. Adjusting for age, BMI and gender, Pearson was used to assess these relationship. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eRelationship between AHI and fat content and MAGiC values of soft palate and tongue.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAHI (events/h)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eB\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e95%CI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eP\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSoft palate\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eT2 (ms)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.742\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026minus;0.770,1.560\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.760\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTongue\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eT2 (ms)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.007\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.249,1.764\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.010*\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePD (pu)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.475\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.426,2.524\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.006*\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eFP\u0026thinsp;=\u0026thinsp;fat percentage; TV\u0026thinsp;=\u0026thinsp;total volume; FV\u0026thinsp;=\u0026thinsp;fat volume; AHI\u0026thinsp;=\u0026thinsp;apnea-hypopnea index. PD\u0026thinsp;=\u0026thinsp;proton density. Adjusting for age, BMI and gender, linear regression was used to assess these relationship. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"obstructive sleep apnea, synthetic MRI, histopathology, subtype","lastPublishedDoi":"10.21203/rs.3.rs-6379963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6379963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Non-invasive quantitative assessment of intrinsic upper airway tissue alterations in patients with obstructive sleep apnea (OSA) is vital for mechanistic research. This study aims to evaluate the application of synthetic MRI (SyMRI) in this field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This was a cross-sectional study that prospectively collected data from a single centre. Participants, both with and without OSA, underwent SyMRI of the upper airway to measure quantitative changes in the intrinsic properties (T1, T2, and PD values) of the soft palate and tongue.These changes were annotated using the IDEAL-IQ MRI sequence and histopathology methods, including Masson, H\u0026amp;E and Oil Red O staining.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: 125 (82 OSA and 43 non-OSA) participants were enrolled. Patients with OSA exhibited higher T2 value in the soft palate and higher T2 and PD values in the tongue compared to non-OSA participants. SyMRI parameters were correlated with the fat fraction derived from IDEAL-IQsequence. A positive correlation was observed between the T2 value and the area of fat, mucous gland and fibrosis in the soft palate. T2 and PD values of tongue showed independent relationship with AHI. The area under curve (AUC) values of T2 and PD of tongue were 0.756.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: SyMRI could act as an effective noninvasive method to quantify inherent alterations of upper airway tissues in OSA, such as fat infiltration, fibrosis, and mucous gland hypertrophy. SyMRI parameters hold promise as the image biomarker, providing valuable information for differentiating various OSA subtypes and thereby promoting personalized treatment.\u003c/p\u003e","manuscriptTitle":"Synthetic MRI for Quantitative Assessment of Intrinsic Alterations in Upper Airway Tissues in Obstructive Sleep Apnea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:15:50","doi":"10.21203/rs.3.rs-6379963/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-17T16:40:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-25T14:32:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-22T07:03:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339140429072657231333575880491260607782","date":"2025-05-20T07:22:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202947018808785831248442209718535492045","date":"2025-05-20T07:14:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-05T14:31:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-09T09:24:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-08T09:11:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-08T09:08:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-04-05T05:36:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e56f9ec-cf3f-4c84-b2dc-d0f313edb89b","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-18T11:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 11:15:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6379963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6379963","identity":"rs-6379963","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}