Skip to main content
SpringerLink
Log in

The diagnostic value of the IDEAL-T2WI sequence in dysthyroid optic neuropathy: a quantitative analysis of the optic nerve and cerebrospinal fluid in the optic nerve sheath

  • Head and Neck
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the optic nerve and CSF in the optic nerve sheath as imaging markers of dysthyroid optic neuropathy (DON).

Methods

In this single-centre retrospective study, orbital images of 30 consecutive participants (54 orbits) with DON, 30 patients (60 orbits) with thyroid-associated ophthalmopathy (TAO) without DON, and 19 healthy controls (HCs; 38 orbits) were analysed. The diameter and cross-sectional area of the optic nerve and its sheath, water fraction of the optic nerve, and volume of the fluid in the optic nerve sheath were measured and compared. The associations between MR parameters and clinical measures were assessed using correlation analysis.

Results

The diameter and water fraction of the optic nerve (3 mm and 6 mm behind the eyeball), optic nerve subarachnoid space (ONSS) (3 mm and 6 mm behind the eyeball), and subarachnoid fluid volume in the optic nerve sheath were significantly greater in the DON group than in the TAO group (p < 0.01) or HC group (p < 0.01). ROC analysis showed that ONSS 3 mm behind the eyeball (ONSS3) was a robust predictor of DON (AUC = 0.957, sensitivity = 0.907, specificity = 0.9). Water fraction of the optic nerve 3 mm behind the eyeball (water fraction3) had the best specificity (0.967). Water fraction3, fluid volume in the optic nerve sheath, and optic nerve diameter (3 mm behind the eyeball) were correlated with clinical measures (i.e. clinical activity score, mean defect, and pattern standard deviation).

Conclusions

Increased water fraction of the optic nerve and ONSS3 are promising and easily accessible radiological markers for diagnosing DON.

Key Points

• The water fraction of the optic nerve and optic nerve subarachnoid space (ONSS) are greater in patients with dysthyroid optic neuropathy (DON) than in patients with thyroid-associated ophthalmopathy (TAO) without DON.

• The optic nerve and the cerebrospinal fluid in the optic nerve sheath measures are associated with visual dysfunction.

• The water fraction of the optic nerve and ONSS may be promising imaging markers for diagnosing DON.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

ANOVA:

Analysis of variance

AUC:

Area under the receiver operating characteristic curve

CAS:

Clinical Activity Score

CSF:

Cerebrospinal fluid

DON:

Dysthyroid optic neuropathy

EUGOGO:

European Group on Graves’ Orbitopathy

FOV:

Field of view

HC:

Healthy control

ICC:

Intraclass correlation coefficient

IDEAL:

Iterative decomposition of water and fat with echo asymmetry and least-squares estimation

IDI:

Integrated Discrimination Improvement

MD:

Mean deviation

NEX:

Number of excitations

ONA:

Cross-sectional area of optic nerve

OND:

Optic nerve diameter

ONSA:

Cross-sectional area of optic nerve sheath

ONSD:

Optic nerve sheath diameter

ONSS:

Optic nerve subarachnoid space

PSD:

Pattern standard deviation

ROC:

Receiver operating curve

SI:

Signal intensity

SIR:

Signal intensity ratio

TA:

Acquisition time

TAO:

Thyroid-associated ophthalmopathy

TE:

Echo time

TR:

Repetition time

VA:

Visual acuity

VF:

Visual field

References

  1. Khong JJ, Finch S, De Silva C et al (2016) Risk factors for Graves’ orbitopathy; the Australian Thyroid-Associated Orbitopathy Research (ATOR) Study. J Clin Endocrinol Metab 101:2711–2720

    Article  CAS  PubMed  Google Scholar 

  2. Saeed P, Tavakoli Rad S, Bisschop P (2018) Dysthyroid optic neuropathy. Ophthal Plast Reconstr Surg 34:S60–S67

    Article  PubMed  Google Scholar 

  3. Kazim M, Trokel S, Moore S (1991) Treatment of acute Graves orbitopathy. Ophthalmology 98:1443–1448

    Article  CAS  PubMed  Google Scholar 

  4. Campi I, Vannucchi G, Salvi M (2016) Therapy of endocrine disease: endocrine dilemma: management of Graves’ orbitopathy. Eur J Endocrinol 175:R117–R133

    Article  CAS  PubMed  Google Scholar 

  5. McKeag D, Lane C, Lazarus JH et al (2007) Clinical features of dysthyroid optic neuropathy: a European Group on Graves’ Orbitopathy (EUGOGO) survey. Br J Ophthalmol 91:455–458

    Article  PubMed  Google Scholar 

  6. Nugent RA, Belkin RI, Neigel JM et al (1990) Graves orbitopathy: correlation of CT and clinical findings. Radiology 177:675–682

  7. Yoshikawa K, Higashide T, Nakase Y, Inoue T, Inoue Y, Shiga H (1991) Role of rectus muscle enlargement in clinical profile of dysthyroid ophthalmopathy. Jpn J Ophthalmol 35:175–181

    CAS  PubMed  Google Scholar 

  8. Chan LL, Tan HE, Fook-Chong S, Teo TH, Lim LH, Seah LL (2009) Graves ophthalmopathy: the bony orbit in optic neuropathy, its apical angular capacity, and impact on prediction of risk. AJNR Am J Neuroradiol 30:597–602

    Article  PubMed  PubMed Central  Google Scholar 

  9. Gonçalves ACP, Gebrim EM, Monteiro ML (2012) Imaging studies for diagnosing Graves’ orbitopathy and dysthyroid optic neuropathy. Clinics 67:1327–1334

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gonçalves ACP, Silva LN, Gebrim EMMS, Monteiro MLR (2012) Quantification of orbital apex crowding for screening of dysthyroid optic neuropathy using multidetector CT. AJNR Am J Neuroradiol 33:1602–1607

    Article  PubMed  PubMed Central  Google Scholar 

  11. Weis E, Heran MK, Jhamb A et al (2012) Quantitative computed tomographic predictors of compressive optic neuropathy in patients with thyroid orbitopathy: a volumetric analysis. Ophthalmology 119:2174–2178

    Article  PubMed  Google Scholar 

  12. Al-Bakri M, Rasmussen AK, Thomsen C, Toft PB (2014) Orbital volumetry in Graves’ orbitopathy: muscle and fat involvement in relation to dysthyroid optic neuropathy. ISRN Ophthalmol 2014:435276

    Article  PubMed  PubMed Central  Google Scholar 

  13. Soni CR, Johnson LN (2018) Visual neuropraxia and progressive vision loss from thyroid-associated stretch optic neuropathy. Eur J Ophthalmol 20:429–436

    Article  Google Scholar 

  14. Dodds NI, Atcha AW, Birchall D, Jackson A (2009) Use of high-resolution MRI of the optic nerve in Graves’ ophthalmopathy. Br J Radiol 82:541–544

    Article  CAS  PubMed  Google Scholar 

  15. Rutkowska-Hinc B, Maj E, Jablonska A, Milczarek-Banach J, Bednarczuk T, Miskiewicz P (2018) Prevalence of radiological signs of dysthyroid optic neuropathy in magnetic resonance imaging in patients with active, moderate-to-severe, and very severe Graves orbitopathy. Eur Thyroid J 7:88–94

    Article  PubMed  PubMed Central  Google Scholar 

  16. Hoch MJ, Bruno MT, Shepherd TM (2017) Advanced MRI of the Optic Nerve. J Neuroophthalmol 37:187–196

    Article  PubMed  Google Scholar 

  17. Bartley GB, Gorman CA (1995) Diagnostic criteria for Graves’ ophthalmopathy. Am J Ophthalmol 119:792–795

    Article  CAS  PubMed  Google Scholar 

  18. Hu HH, Kim HW, Nayak KS, Goran MI (2010) Comparison of fat-water MRI and single-voxel MRS in the assessment of hepatic and pancreatic fat fractions in humans. Obesity (Silver Spring) 18:841–847

    Article  Google Scholar 

  19. Kaichi Y, Tanitame K, Itakura H et al (2016) Orbital fat volumetry and water fraction measurements using T2-weighted FSE-IDEAL imaging in patients with thyroid-associated orbitopathy. AJNR Am J Neuroradiol 37:2123–2128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Killer HE, Laeng HR, Flammer J, Groscurth P (2003) Architecture of arachnoid trabeculae, pillars, and septa in the subarachnoid space of the human optic nerve: anatomy and clinical considerations. Br J Ophthalmol 87:777–781

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Costa DN, Pedrosa I, McKenzie C, Reeder SB, Rofsky NM (2008) Body MRI using IDEAL. AJR Am J Roentgenol 190:1076–1084

    Article  PubMed  Google Scholar 

  22. Liu P, Wang Q, Peng C, Luo B, Zhang J (2019) Combined application of isotropic three-dimensional fast spin echo (3D-FSE-Cube) with 2-point Dixon fat/water separation (FLEX) and 3D-FSE-cube in MR dacryocystography. Br J Radiol 92:20180157

    Article  PubMed  Google Scholar 

  23. Ollitrault A, Charbonneau F, Herdan ML et al (2021) Dixon-T2WI magnetic resonance imaging at 3 tesla outperforms conventional imaging for thyroid eye disease. Eur Radiol. https://doi.org/10.1007/s00330-020-07540-y

  24. Reeder SB, Wen Z, Yu H et al (2004) Multicoil Dixon chemical species separation with an iterative least-squares estimation method. Magn Reson Med 51:35–45

    Article  CAS  PubMed  Google Scholar 

  25. Pineda AR, Reeder SB, Wen Z, Pelc NJ (2005) Cramér-Rao bounds for three-point decomposition of water and fat. Magn Reson Med 54:625–635

    Article  PubMed  Google Scholar 

  26. Reeder SB, Pineda AR, Wen Z et al (2005) Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): application with fast spin-echo imaging. Magn Reson Med 54:636–644

    Article  PubMed  Google Scholar 

  27. Gerdes CM, Kijowski R, Reeder SB (2007) IDEAL imaging of the musculoskeletal system: robust water fat separation for uniform fat suppression, marrow evaluation, and cartilage imaging. AJR Am J Roentgenol 189:W284–W291

    Article  PubMed  Google Scholar 

  28. Humbert IA, Reeder SB, Porcaro EJ, Kays SA, Brittain JH, Robbins J (2008) Simultaneous estimation of tongue volume and fat fraction using IDEAL-FSE. J Magn Reson Imaging 28:504–508

    Article  PubMed  PubMed Central  Google Scholar 

  29. Reeder SB, Robson PM, Yu H et al (2009) Quantification of hepatic steatosis with MRI: the effects of accurate fat spectral modeling. J Magn Reson Imaging 29:1332–1339

    Article  PubMed  PubMed Central  Google Scholar 

  30. Guo RM, Lin WS, Liu WM et al (2018) Quantification of fat infiltration in the sacroiliac joints with ankylosing spondylitis using IDEAL sequence. Clin Radiol 73:231–236

    Article  PubMed  Google Scholar 

  31. Khosa F, Clough RE, Wang X, Madhuranthakam AJ, Greenman RL (2018) The potential role of IDEAL MRI for identification of lipids and hemorrhage in carotid artery plaques. Magn Reson Imaging 49:25–31

    Article  CAS  PubMed  Google Scholar 

  32. Hisanaga S, Aoki T, Shimajiri S et al (2020) Peritumoral fat content correlates with histological prognostic factors in breast carcinoma: a study using Iterative Decomposition of Water and Fat with Echo Asymmetry and Least-Squares Estimation (IDEAL). Magn Reson Med Sci. https://doi.org/10.2463/mrms.mp.2019-0201

  33. Geeraerts T, Newcombe VF, Coles JP et al (2008) Use of T2-weighted magnetic resonance imaging of the optic nerve sheath to detect raised intracranial pressure. Crit Care 12:R114

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ohnishi T, Noguchi S, Murakami N et al (1994) Extraocular muscles in Graves ophthalmopathy: usefulness of T2 relaxation time measurements. Radiology 190:857–862

    Article  CAS  PubMed  Google Scholar 

  35. Hickman SJ, Miszkiel KA, Plant GT, Miller DH (2005) The optic nerve sheath on MRI in acute optic neuritis. Neuroradiology 47:51–55

    Article  CAS  PubMed  Google Scholar 

  36. Wostyn P, Van Dam D, Audenaert K, Killer HE, De Deyn PP, De Groot V (2015) A new glaucoma hypothesis: a role of glymphatic system dysfunction. Fluids Barriers CNS 12:16

    Article  PubMed  PubMed Central  Google Scholar 

  37. Deng W, Liu C, Parra C et al (2020) Quantitative imaging of the clearance systems in the eye and the brain. Quant Imaging Med Surg 10:1–14

    Article  PubMed  PubMed Central  Google Scholar 

  38. Wang X, Lou N, Eberhardt A et al (2020) An ocular glymphatic clearance system removes β-amyloid from the rodent eye. Sci Transl Med. https://doi.org/10.1126/scitranslmed.aaw3210

  39. Jacobsen HH, Ringstad G, Jorstad OK, Moe MC, Sandell T, Eide PK (2019) The human visual pathway communicates directly with the subarachnoid space. Invest Ophthalmol Vis Sci 60:2773–2780

    Article  CAS  PubMed  Google Scholar 

  40. Hayreh SS (1984) The sheath of the optic nerve. Ophthalmologica 189:54–63

    Article  CAS  PubMed  Google Scholar 

  41. Mishra S, Maurya VK, Kumar S, Kaur A, Saxena SK (2020) Clinical management and therapeutic strategies for the thyroid-associated ophthalmopathy: current and future perspectives. Curr Eye Res. https://doi.org/10.1080/02713683.2020.1776331:1-17

Download references

Acknowledgements

We thank Wenzhi Lv (Julei Technology Corporation, China) for supporting part of the data extraction and processing.

Funding

This study has received funding by grant from the National Natural Science Foundation of China (No. 81771793) and the Young Science Foundation of Guangdong Second Provincial General Hospital (2019-QNJJ-01).

Author information

Author notes

  1. Hongyu Wu and Ban Luo contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China

    Hongyu Wu, Qiuxia Wang, Yali Zhao, Linhan Zhai & Jing Zhang

  2. Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China

    Ban Luo

  3. Department of Endocrinology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China

    Gang Yuan

  4. Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China

    Ping Liu

  5. Ultrasound Medical Center, Lanzhou University Sencond Hospital, Lanzhou University, Lanzhou, 730030, Gansu, China

    Yanqiang Ma

  6. Department of Artificial Intelligence, Julei Technology Company, Wuhan, 430030, Hubei, China

    Wenzhi Lv

Authors
  1. Hongyu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ban Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiuxia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yali Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Linhan Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yanqiang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wenzhi Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Zhang.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Jing Zhang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• observational

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, H., Luo, B., Yuan, G. et al. The diagnostic value of the IDEAL-T2WI sequence in dysthyroid optic neuropathy: a quantitative analysis of the optic nerve and cerebrospinal fluid in the optic nerve sheath. Eur Radiol 31, 7419–7428 (2021). https://doi.org/10.1007/s00330-021-08030-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-08030-5

Keywords

Search

Navigation